diff --git a/README.md b/README.md index a711aa635..e949b646c 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,17 @@ -# Welcome to ZZSC9020 GitHub repository for group [GROUP-NAME] - -This GitHub repository is the main point of access for students and lecturers of the ZZSC9020 capstone course. - -In this repository, you will find the data to start developing your project. Also, we will use the repository to share code, documentation, data, models and other resources between the group members and course lecturers. - -Complete the information below regarding your group. +# Welcome to ZZSC9020 GitHub repository for Group D ## Group and project information ### Group members and zIDs -- Member 1 (zID1) - Group leader -- Member 2 (zID2) - role -- Member 3 (zID3) - role -- Member 4 (zID4) - role -- Member 5 (zID5) - role +- Katelyn Kemp (z5459347) - Group leader +- David Valido Ramos (z5516338) - Researcher +- Shanjay Perinpanathan (z5339723) - Researcher +- Nick Mutton (z5549371) - Coder +- Senarath Seelanatha (z5595581) - Coder +- Waseem Alashqar (z5514810) - Coder ### Brief project description - -Describe your project in one paragraph. +This project took the concepts of a hybrid model to improve AEMO’s short-term electricity demand forecast by incorporating modelling the AEMO forecast errors in a subsequent model and adding these the AEMO’s forecast to produce a more accurate prediction. The project found that for all models tested, modelling the errors improved the accuracy of the short-term forecast improved. ## Repository structure diff --git a/agendas/Consultation agenda 20250325.docx b/agendas/Consultation agenda 20250325.docx new file mode 100644 index 000000000..2fcdec8db Binary files /dev/null and b/agendas/Consultation agenda 20250325.docx differ diff --git a/agendas/Consultation agenda 20250409.docx b/agendas/Consultation agenda 20250409.docx new file mode 100644 index 000000000..005e59133 Binary files /dev/null and b/agendas/Consultation agenda 20250409.docx differ diff --git a/agendas/Team project agenda 20250310.docx b/agendas/Team project agenda 20250310.docx new file mode 100644 index 000000000..b1d28fc5a Binary files /dev/null and b/agendas/Team project agenda 20250310.docx differ diff --git a/agendas/Team project agenda 20250324.docx b/agendas/Team project agenda 20250324.docx new file mode 100644 index 000000000..5038e074a Binary files /dev/null and b/agendas/Team project agenda 20250324.docx differ diff --git a/agendas/Team project agenda 20250405.docx b/agendas/Team project agenda 20250405.docx new file mode 100644 index 000000000..d2c61ff6b Binary files /dev/null and b/agendas/Team project agenda 20250405.docx differ diff --git a/agendas/Team project consultation Week 1 agenda 20250313.docx b/agendas/Team project consultation Week 1 agenda 20250313.docx new file mode 100644 index 000000000..d083b9750 Binary files /dev/null and b/agendas/Team project consultation Week 1 agenda 20250313.docx differ diff --git a/checklists/test.txt b/checklists/test.txt new file mode 100644 index 000000000..77356c314 --- /dev/null +++ b/checklists/test.txt @@ -0,0 +1 @@ +test diff --git a/data/NSW/Actual Temp.zip b/data/NSW/Actual Temp.zip new file mode 100644 index 000000000..ed78dd724 Binary files /dev/null and b/data/NSW/Actual Temp.zip differ diff --git a/data/NSW/NSW.001 b/data/NSW/NSW.001 new file mode 100644 index 000000000..4b06d3d92 Binary files /dev/null and b/data/NSW/NSW.001 differ diff --git a/data/NSW/NSW.002 b/data/NSW/NSW.002 new file mode 100644 index 000000000..b2a2e0d2a Binary files /dev/null and b/data/NSW/NSW.002 differ diff --git a/data/NSW/NSW.003 b/data/NSW/NSW.003 new file mode 100644 index 000000000..befe0b3c9 Binary files /dev/null and b/data/NSW/NSW.003 differ diff --git a/data/NSW/NSW.004 b/data/NSW/NSW.004 new file mode 100644 index 000000000..31fd27dcc Binary files /dev/null and b/data/NSW/NSW.004 differ diff --git a/data/NSW/NSW.005 b/data/NSW/NSW.005 new file mode 100644 index 000000000..19a1c8fce Binary files /dev/null and b/data/NSW/NSW.005 differ diff --git a/data/NSW/NSW.006 b/data/NSW/NSW.006 new file mode 100644 index 000000000..644c42a14 Binary files /dev/null and b/data/NSW/NSW.006 differ diff --git a/data/NSW/NSW.007 b/data/NSW/NSW.007 new file mode 100644 index 000000000..68f2cb8f3 Binary files /dev/null and b/data/NSW/NSW.007 differ diff --git a/data/NSW/NSW.008 b/data/NSW/NSW.008 new file mode 100644 index 000000000..bd27a4321 Binary files /dev/null and b/data/NSW/NSW.008 differ diff --git a/minutes/Consultation notes 20250325.docx b/minutes/Consultation notes 20250325.docx new file mode 100644 index 000000000..05639ef09 Binary files /dev/null and b/minutes/Consultation notes 20250325.docx differ diff --git a/minutes/Consultation notes 20250409.docx b/minutes/Consultation notes 20250409.docx new file mode 100644 index 000000000..3b6e3a9d4 Binary files /dev/null and b/minutes/Consultation notes 20250409.docx differ diff --git a/minutes/Team meeting notes 20250405.docx b/minutes/Team meeting notes 20250405.docx new file mode 100644 index 000000000..aa81ddd77 Binary files /dev/null and b/minutes/Team meeting notes 20250405.docx differ diff --git a/minutes/Team project meeting notes 20250310.docx b/minutes/Team project meeting notes 20250310.docx new file mode 100644 index 000000000..e828f2a63 Binary files /dev/null and b/minutes/Team project meeting notes 20250310.docx differ diff --git a/minutes/Team project meeting notes 20250313.docx b/minutes/Team project meeting notes 20250313.docx new file mode 100644 index 000000000..81a36e74e Binary files /dev/null and b/minutes/Team project meeting notes 20250313.docx differ diff --git a/minutes/Team project meeting notes 20250324.docx b/minutes/Team project meeting notes 20250324.docx new file mode 100644 index 000000000..6c9b248b0 Binary files /dev/null and b/minutes/Team project meeting notes 20250324.docx differ diff --git a/report/Group D Report Final.Rmd b/report/Group D Report Final.Rmd new file mode 100644 index 000000000..2d55a5b96 --- /dev/null +++ b/report/Group D Report Final.Rmd @@ -0,0 +1,3201 @@ +--- +title: "Hybrid Model Approaches to Improve Short-Term Energy Demand Forecasts in New South Wales, Australia " +author: +- 'David Valido Ramos (z5516338), ' +- 'Katelyn Kemp (z5459347), ' +- 'Nick Mutton (z5549371), ' +- 'Senarath Seelanatha (z5595581), ' +- 'Shanjay Perinpanathan (z5339723), ' +- 'Waseem Alashqar (z5514810).' +date: "21/04/2025" +Abstract: "Electricity demand forecasting is a difficult problem every country faces. In this paper, we attempt to utilise the concept of hybrid models to improve the energy forecast of AEMO, the Australian body that manages power systems and markets, to predict energy demand in NSW. It was found that historical forecast errors and weather variables had some correlation with forecasting errors, therefore were included in the models. SARIMA, Random Forest, and XGBoost models were tested to determine the best fit for correcting AEMO forecasting bias and reducing overall energy demand forecast error. We argue the hybrid modification enables us to correctly factor relationships not supported by AEMO’s original model. All hybrid models tested provided some reduction in the overall forecast error and supported the hybrid model process." +output: + bookdown::pdf_document2: + template: template.tex + latex_engine: xelatex + md_extensions: +raw_attribute + keep_md: true + keep_tex: true + pandoc_args: + - --top-level-division="chapter" + - --bibliography="references.bib" + toc: true + toc_depth: 1 + number_sections: true + fig_caption: yes +Team: Group D +session: Hexamester 2, 2025 +coursecode: ZZSC9020 +bibliography: references.bib +csl: university-of-south-wales-harvard.csl +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE, warning = FALSE) +library(reticulate) +if (.Platform$OS.type == "unix") { + use_python("/usr/bin/python") +} else if (.Platform$OS.type == "windows") { + +} + +``` + +# Introduction {.label:s-intro} + +Energy forecasts play a crucial role in planning and maintaining the energy sector. Ensuring forecast accuracy helps to manage imbalances in energy production and consumption, reduce power system costs, and improve operational safety (Mystakidis et al., 2024). Energy demand management is also linked with self sufficiency and cost effectiveness that facilitate sustainable economic development (Suganthi and Samiel, 2012). Energy forecasting therefore has a broad impact on a wide variety of stakeholders including residential customers, power generators, retailers, traders, industrial and commercial customers, system operators, and financial investors (Ghalehkhondabi et al., 2016). + +\bigskip + +There are many risks in inaccurate energy forecasting. Over forecasting has cost and resource implications for providers, as well as environmental impacts. Under forecasting can cause outages, as well as having down the stream increased costs from inconsistent supply (Suganthi and Samuel, 2012). Shortages are also linked to political instability (Rakpho and Yamaka, 2021). + +\bigskip + +The Australian Energy Market Operator (AEMO) is responsible for managing Australia’s electricity and gas systems and markets to ensure Australians have access to reliable, affordable and secure energy. AEMO performs a wide range of functions, however one of their key roles is to balance electricity supply and demand through dispatching electricity generation based on forecasts updated every 5 minutes. It is therefore critical that their electricity demand forecasting is accurate to reduce the risks associated with over, or under supply of electricity to the market. While the AEMO short-term electricity forecast is generally quite accurate, it is valuable to understand where the forecast may be underperforming to consider how accuracy could be improved. + +\bigskip + +**The goal of this report is to identify variables that may contribute to errors in AEMO’s electricity demand forecasts, with the aim of using these insights to improve forecast accuracy.** + +\bigskip + +The report will consider a 12-hour interval with a step-ahead forecast. This is considered a ‘pre-dispatch’ interval based on AEMO’s definitions and is important for operational planning, therefore its accuracy is critical. + +\clearpage + +# Literature Review + +## Forecasting electricity demand background + +In today’s context of near-constant energy consumption, the task of energy forecasting has become increasingly complex. Given the absence of a universally applicable forecasting method, the selection of an appropriate technique is typically guided by the nature of the available data and the specific objectives of the forecasting exercise (Pinheiro, Madeira, & Francisco, 2023). Additionally, the forecast interval, which often reflects the purpose of the forecast, plays a large role in determining the suitability of different modeling approaches. + +\bigskip + +Forecasting models are typically categorised into short-, medium-, and long-term, and while there is not a unanimous definition of what constitutes these time periods, researchers generally agree that short-term is a few minutes up to a few days (Ahmad and Chen, 2018) or two weeks (Klyuev et al., 2022), medium-term as one month to one year, and long-term as one year to ten years (Ahmad and Chen, 2018). AEMO defines its short term forecast as up to 7 days ahead (AEMO, 2023). + +\bigskip + +Short-term intervals tend to require the greatest accuracy as they support a wide variety of operational planning, or network management activities including scheduling, planning of power generation, cost optimisation and guaranteeing continuous electricity supply (Sanhudo, Rodrigues and Filho, 2021). Short-term forecast methods can be broadly categorised into two categories – mathematical algorithms such as time-series analysis and logistic regression, and artificial intelligence (AI) algorithms such as machine learning, deep learning and ensemble learning models (Deng et al., 2022). For short-term forecasting, AI methods are becoming more popular as they can consider the non-linear nature of power demand. Short term forecasting is also generally more interested in the accuracy of the forecast rather than the interpretability of the results which makes these ‘black box’ approaches appropriate (Phyo and Byun, 2021). Other studies have found that machine learning models tend to outperform traditional models such as ARIMA in short-term forecasting (Divina et al., 2019). + +\bigskip + +Medium- and long-term forecasting supports the planning and maintenance of the electrical network such as smart grid eco-systems (Ahmad & Chen, 2018). Furthermore, long-term forecasting is more strategic and is necessary for the development of energy systems, planning capital construction at production or infrastructure facilities (Klyuev et al., 2022). These forecast intervals typically use econometric models, system dynamics, and grey prediction, with a focus on policy adjustments, economic indicators (such as GDP and CPI), and population trends (Koukaras et al., 2024). + +## Weather in forecasting electricity demand + +Temperature is a primary driver of electricity demand, shaping heating and cooling loads that dictate energy consumption. Research consistently identifies it as the dominant weather factor in electricity demand prediction, especially during peak periods. Liu et al. (2021) demonstrate that extreme temperatures lead to increased residential electricity consumption, finding that for each additional day in which the mean temperature exceeds 30 °C, there is an 16.8% increase in monthly residential electricity consumption. Similarly, for each additional day below -6 °C there is a 6% increase in monthly residential electricity consumption. This underscores temperature’s critical role in accurate demand forecasting, as it directly influences consumption patterns. + +\bigskip + +Extreme temperatures can lead to significant errors in electricity demand forecasts, often underestimating demand. During Winter Storm Uri in Texas in February 2021 (Añel, 2024), minimum extreme cold temperatures of –34 °C and high winds of 260 km/h impacted 170 million people. Due to this extreme weather event, electricity demand unexpectedly increased from 40 GW to over 70 GW, resulting in blackouts that affected more than 4 million people. The economic cost of the power outages and disruption has been estimated between 26.1 and 130 billion U.S. dollars. + +\bigskip + +Other weather variables, particularly humidity and "feels like" temperature, enhance forecasting accuracy. Maia-Silva et al. (2020) found that using humidity-related measures, such as dew point and heat index, improves prediction accuracy, especially in high-energy-consuming regions, with improvements up to 8-9%. This highlights the need to consider composite weather indices, as air temperature alone underestimates demand. + +## Historical Forecasting Error Incorporation + +Besides temperature and other weather components, historical measurements of energy demand or forecasted energy demand are highly reliable factors for predicting future energy demand (Singh and Yassine, 2018). Historical energy demand is important for capturing seasonal effects in different time horizons (day, week, month, season etc). However, historical energy forecasts (and by extension their differentials) are valuable because, in addition to seasonal effects, they capture bias and allow for corrections to the future forecast. Historical forecast factors are so influential that there is evidence that it can create reasonable forecasts without additional weather variables (Boroojeni et al., 2017). + +## Modelling electricity demand + +As previously stated, short-term energy models can be effectively categorised into two groups: classical statistical techniques, and machine learning or AI techniques. Traditional statistical and econometric models tend to be explainable and interpretable. While often less accurate, these models are widely used in energy demand forecasting and include methods such as regression (Papalexopoulos and Hesterberg, 1990) (Ertuğrul, Tekin and Tekin, 2020) and time-series such as ARIMA (Tarmanini et al., 2023) (Ediger and Akar, 2007). They also have natural extrapolations to medium-to-long term models, that are also econometric-based due to their relationship with longitudinal factors such as policy changes, modifications to the energy grid, or economic factors (such as GDP and population) (Ardakani and Ardehali, 2014). While a machine learning model, decision tree methods also provide interpretability in energy demand forecasting (Kopyt et al., 2024) (Wang et al., 2018). + +\bigskip + +Black box machine learning models provide a greater focus on model accuracy rather than interpretability. Some common models used in energy demand forecasting include Neural Networks (Manno, Martelli and Amaldi, 2022) (Kuo and Huang, 2018) (Pao, 2009), Support Vector Machines (Ahmad et al., 2014) (Ahmad et al., 2020), and ensemble methods, such as Random Forests (Divina et al., 2019) and XGBoost (Abbasi et al., 2019). + +\bigskip + +Divina et al. (2019) studied short-term energy consumption forecasting in smart buildings using several models such as linear regression, auto-regressive integrated moving average (ARIMA), artificial Neural Networks (ANNs) and ensemble methods such as random forests (RF) and extreme gradient boosting (XGBoost). They measured the performance of these models using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). They found that the best performing models were machine based approaches, and more so ensemble methods such as RF, GBM and XGBoost. On the other hand, ARIMA was the worst performing method that was tested. Further, the optimal historical window was found to be 10 days where accuracy improves up to this point, but does not improve much beyond this. Tarmanini et al. (2023) considered ARIMA and Artificial neural network (ANN) models to forecast daily electricity load in Ireland. The study found that both ARIMA and ANN produced more error in winter than in other seasons. Despite this, the ANN method performed better in terms of accuracy due to it better coping with non-linear data, but suggest a hybrid approach may provide more accurate results. + +\bigskip + +Other studies have also concluded that the best performing models tend to be hybrid models which use a combination of explainable and/or black-box methods, such as NN–ARIMA or CNN-LTSM due to their stability and potential to reduce overfitting (Deng et al. 2022). For example, Suganthi and Samuel (2012) compared various approaches to energy demand forecasting and found often hybrid approaches such as linking ARIMA models with neural networks often produce more accurate results. The superiority of hybrid models for energy forecasts are due to corrections of the original forecast output in the second modelling component (Savić, Selakov and Milošević, 2014). + +\bigskip + +One important method used in hybrid modelling is residual error forecasting. Andronikos, Tzelepi and Tefas (2023) proposed a residual error learning methodology for electricity demand forecasting which involved training a model on actual load values, then calculating the residual errors which would subsequently be used as targets to train a second model. The final prediction of forecast load would then be the sum of the first model’s prediction and the second model’s prediction. The authors found that if the errors have an underlying structure, the residual error forecasting method will improve forecasting accuracy. + +\bigskip + +A common method used in many hybrid studies which also aligns closely with modelling residuals is to decompose time series data into trend and residual components and model these components separately with appropriate methods. The forecasts from each component are then summed together for the final forecast. Amara et al. (2019) used decomposition to extract the temperature-related component that makes up electricity demand and then analysed and forecasted the residual component. The two forecasts were then summed to produce the final forecast. This allowed for understanding of periodicity in the residuals and to improve the overall forecast accuracy. Zhang et al (2022) considered the Australian electricity market in their study using a decomposition-hybrid approach. They first extracted a trend component from the original electricity load, then obtained the nonlinear component by subtracting the trend component from the original electricity load. The two components were forecast separately and then added together to make up the final forecast. Their proposed model improved the forecasting accuracy against all comparison models. Another approach to hybrid modelling considered by Pao (2009) was a two step approach where a linear model was built and the results of this inputted into a neural network model to capture both linear and non-linear relationships in the data. This showed to produce superior predictions to a linear model alone. + +\bigskip + +The approach proposed in this report is based on a hybrid approach where the AEMO forecast will act as the initial model and a new model will be built considering the errors from that model in an attempt to improve the overall forecast accuracy. + +## AEMO forecasting methodology + +The AEMO load Forecasting Methodology (AEMO, 2023) details the organisation’s approach to forecasting electricity demand. With particular relevance to this report, AEMO pre-dispatch forecasts are short-term electricity demand forecasts that include intervals up to 40 hours. One of the important uses of pre-dispatch forecasting is to support operational planning that ensures electricity reliability and security of the network. The key inputs into the forecast include: + +- Historical demand (such as recent load patterns) + +- Weather forecast variables (particularly those that describe the temperature profile) + +- Calendar variables (e.g. weekday or weekend, public or school holiday, daylight savings) + +- Solar and wind generation forecasts. + +\bigskip + +There is very little manual intervention for these forecasts, with AEMO’s Demand Forecasting System (DFS) generating forecasts automatically through a combination of statistical and machine learning models, every half hour. + +\bigskip + +The pre-dispatch load forecasting error threshold for NSW is 150 MW based on historical peak demand for NSW and previous forecasting performance. The load forecast is reviewed whenever the forecast error is greater than the threshold for two consecutive 30-minute periods, therefore at an overall level the forecast is already quite accurate. + +# Material and Methods + +Figure \@ref(fig:modelDiagram) shows the overall structure of this project designed to address the research question. The process began with data collection and pre-processing, including calculating the forecast error. Exploratory data analysis was conducted to understand relationships between different variables and the forecast error. The data were then split into training and testing samples for models to be built, fine-tuned and compared. The remaining sections of this report detail the steps undertaken in the modelling as well as analysis and presentation of the results. + + +```{r modelDiagram, fig.cap = "Project structure followed to address the research question", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/MaterialMethods.png') + +``` + + +## Software + +Python was the primary software used for data analysis and modelling based on its flexibility in data visualisations and ability to execute machine learning models. + +\bigskip + +To ensure reproducibility, RMarkdown was used to prepare the final report. Power BI was also used in initial data exploration to understand high-level trends in the data. A Github repository was used to store data, code and working documents. The repository can be found here: https://github.com/unswnick/project. All relevant code for this project can be found in the Appendices. Appendix A contains data processing code, Appendix B contains Modelling code, and Appendix C contains code for plots. + +\bigskip + +A summary of software used as part of the project is summarised in Table \@ref(tab:tab1). +\bigskip + +\begin{table}[H] +\caption{Summary of software used} +\begin{center} +\begin{tabular}{|l|l|p{19em}|} +\hline +\textbf{Software} & \textbf{Library(s)} & \textbf{Purpose} \\ +\hline +\multirow{4}{4em}{Python} & Pandas & Reading, manipulating, cleaning and analysing datasets. \\ +\cline{2-3} +& Numpy & Manipulating data and mathematical \newline calculations. \\ +\cline{2-3} +& Matplotlib, Seaborn & Visualising data to understand trends and \newline patterns. \\ +\cline{2-3} +& Scikit-learn & Implementing and evaluating machine learning algorithms. \\ +\hline +PowerBI & - & Summarising and visualising data. \\ +\hline +RMarkdown & - & Writing final report. \\ +\hline +Github & - & Repository for project documents \\ +\hline +\end{tabular} +\end{center} +(\#tab:tab1) +\end{table} + +## Description of the Data + +Table \@ref(tab:tab2) describes the data that was used in analysis. In addition to the data files provided by the client, historical weather forecasts including temperature, humidity and wind speed were sourced from OpenWeather (OpenWeather, 2025), a global company specialising in environmental data products. Forecast weather data rather than actual weather data was used as an input to ensure the forecast models were realistic. + +\begin{table}[H] +\caption{Datasets used in this project and their properties} +\centering +\begin{tabular}{|p{13em}|p{20em}|} +\hline +\textbf{Data} & \textbf{Description} \\ +\hline +\textbf{Electricity demand}\newline Use for both training and testing models. & Electricity demand from 2010 to 2021. Well-structured and low complexity with no duplicates and no null values.\newline Variables: Date-time, totalDemand, regionID\newline +Format: CSV, Storage: Github, Size: 6 Mb, Rows: 196,513 \\ +\hline +\textbf{Forecast demand} \newline Used as a baseline forecast model and improve on. & Provides forecasted demand data from 2010 to 2021. Well-structured with no null values. It is high complexity due to uneven time increments and duplicate rows. \newline Variables: Date-time, forecastDemand, totalDemand, regionID, preDispatchSeqNo, periodID, lastChange\newline Format: CSV, Storage: Github, Size: 722 Mb, Rows: 10,906,019 \\ +\hline +\textbf{Forecast weather indicators}\newline Exogenous variables included in modelling. & Provides previous forecast weather data for Bankstown from October 7 2017. Well-structured with no null values. \newline Variables: Date-time, temperature, humidity, wind speed, rain\newline Format: CSV, Storage: GitHub, Sharepoint/Teams, Size: 1327.1 MB, Rows: 10854100 \\ +\hline +\end{tabular} +(\#tab:tab2) +\end{table} + +## Data Cleaning + +Data was found to be complete for Electricity Demand data Some forecast data were missing for forecast intervals >12 hours. To ensure complete data was used, and to reduce computational complexity, the forecast models were trained and tested on 12 hour forecast intervals only. There were no missing values in the Forecast Weather Indicators data, however the available data begins on October 7 2017. Consequently, the relevant data used to train and test the forecast model was between October 7 2017 and 17 March 2021 with a 12 hour forecast interval. No further missing values were present in the data. + +\bigskip + +Outliers were not removed from the data to ensure data completeness and to avoid introducing bias through exclusions. Further advice from industry experts would be required to determine which outliers, if any, should be removed based on appropriate criteria. + +\bigskip + +Additional data cleaning steps performed on all datasets are detailed below: + +1. Date/time variables were formatted consistently (i.e. d/m/y H:M) + +2. Date/time variables were rounded to the nearest 30 minute increment to provide consistent 30-minute intervals + +3. Duplicate date/time rows were removed to ensure each date/time row was unique + +\bigskip + +After each dataset was cleaned and checked, they were merged into one clean dataset, joined on the unique date/time variable. + +## Data pre-processing + +Outlined below are the steps undertaken to pre-process the data: + +1. **Feature extraction** – The following features were extracted from date/time variables: + + Hour_of_day + + Month_of_year + + Day_of_week + +2. **Label enconding** – Hour_of_day, Day_of_week and Month_of_year variables were one-hot-encoded into binary variables + +3. **Feature engineering** – The following new features were created: + + + Forecast interval (date/time future – date/time current) + + Forecast error (total demand – forecast demand) + + 24-hour Forecast Error (Forecast error from 24 hours ago) + + 48-hour Forecast Error (Forecast error from 48 hours ago) + + 72-hour Forecast Error (Forecast error from 72 hours ago) + + 7-day Forecast Error (Forecast error from 7 days ago) + + 14-day Forecast Error (Forecast error from 14 days ago) + + Relative error (Forecast error / total demand) + + Hour × Temperature (Hour * Temperature) + + Hour (Sine) (hour_sin) — sin(2π × Hour / 24) + + Hour (Cosine) (hour_cos) — cos(2π × Hour / 24) + + Month × Temperature (MonthNumb * Temperature) + + Hour × Forecast Demand (Hour * forecast_demand) + + Temperature × Forecast Demand (Temperature * forecast_demand) + + Temperature × Hour (Sine) (Temperature * hour_sin) + + Temperature × Hour (Cosine) (Temperature * hour_cos) + + Forecast Demand × Hour (Sine) (forecast_demand * hour_sin) + + Forecast Demand × Hour (Cosine) (forecast_demand * hour_cos) + + 24-hour Forecast Error × Hour (Cosine) (24hrpreverrors * hour_cos) + + 24-hour Forecast Error × Hour (Sine) (24hrpreverrors * hour_sin) + +4. **Splitting the data** - As a final step in pre-processing, the data were split into 70% training 7 October 2017 – 5 March 2020) and 30% testing (6 March 2020 – 17 March 2021). This split allowed for a large number of data to be trained on, and a full year to test which captured all seasonal effects. The same split was used across the models. + +Note that modelling methods chosen did not require normalisation of the data. + +## Assumptions + +- AEMO’s forecast data is released every 5 minutes, therefore forecast data for the 12 hour interval is available to use in the model + +- Temperature/weather forecasts are available for 12 hours into the future. + +- Bankstown weather variables are reasonable representations of weather conditions across New South Wales. + +## Modelling Methods + +The following methods were in this study: + +* Linear Regression: Baseline model for improving forecasts due to its simple implementation and interpretability. + +* SARIMA: EDA identified autocorrelation between forecast errors. Due to the seasonal nature of electricity demand, SARIMA modelling was conducted. + +* Decision Trees: EDA identified non-linearity between electricity demand and its explanatory variables. As such, decisions trees were implemented to explore simpler non-linear behaviors. + +* XGBoost: Implemented to explore non-linear behaviors using advanced techniques. + +These modelling methodologies are described below. + +\bigskip + +\noindent \textbf{ARIMA} + +\bigskip + +\noindent Auto Regressive Integrated Moving Average (ARIMA) is a time series forecasting model. Besides being well-researched and more readily explainable compared to machine learning models, its algorithm specifications make it suitable for energy demand forecasting. The model consists of three main components: + +\bigskip + +Auto Regression: The model utilises lagged observations or previous time points. Due to the weather conditions of previous days having a direct influence on future weather, previous time points are relevant for forecasting. In addition, energy demand also exhibits seasonality that can be captured by previous inputs. + +\bigskip + +Differencing (Integration): Energy and weather demands over different time horizons exhibit slight trend. Raw observations are differenced to make statistical properties (such as mean or variance) stabilised over time. + +\bigskip + +Moving average: Smooths variance by modelling a moving average of lagged variables against point residuals. This reduces noise in highly variable factors susceptible to measurement error like weather. + +\bigskip + +Seasonal Auto Regressive Intergrated Moving Average (SARIMA) is an extension of the ARIMA model. SARIMA is designed to support seasonality in time series data. It can be modified to incorporate seasonality in different time horizons such as weekly, monthly, or quarterly time frames. The model parameters are the same as ARIMA with the inclusion of seasonal variants to control for seasonal effects: seasonal autoregressive order, seasonal differencing order, and seasonal moving average order. + +\bigskip + +\noindent \textbf{Decision Trees} + +\bigskip + +\noindent Decision Trees are a type of explainable machine learning model. They are trained by recursively dividing the dataset into subsets using entropy (a measure of impurity or randomness in the dataset) and optimise for information gain. The impurity is in context to the target variable. When a subset of data is comprised of an entire class, it is considered pure. It is interpretable because the model construction can be read as a series of conditional IF statements to achieve certain outputs. + +\bigskip + +A Random Forest is a collection of generated Decision Trees. The generation formula is consistent across each decision tree, the difference being each tree is generated from a different bootstrap sample. The prediction outputs for regression tasks, such as energy demand forecasting, is an average of all decision tree outputs. Random Forests lose the ability of decision trees to be interpretable, the benefit however, is improved accuracy and robustness. + +\bigskip + +\noindent \textbf{XGBoost} + +\bigskip + +\noindent XGBoost, short for extreme gradient boosting, is a gradient descent machine learning method. Its formulation is by use of a loss function to measure the difference between predicted and actual values and a regularization term to penalize complex models. + +\bigskip + +It functions by building decision trees sequentially. Each tree is trained to predict the residuals from previous trees. Each tree split mechanism follows the process of regular decision tree training. Each tree's contribution to the final prediction is weighted by a learning rate. It generally outperforms regular decision tree models due to its internal corrections of error and feature selection. Its construction makes it suitable for regression tasks such as energy demand forecasting. + +# Exploratory Data Analysis + +This section presents an exploratory analysis of the temperature, forecasted demand, and actual electricity demand data. Exploratory data analysis (EDA) explored how demand responds to temperature variations and where forecast discrepancies are most pronounced. The data is manipulated and visualised with Python. + +\bigskip + +We begin the analysis by focusing on the individual distributions and characteristics of each dataset. This stage provides context on the seasonal variability of the data. + +```{python import datasets, echo=FALSE, eval = FALSE} + +### Demand dataset +df_demand = pd.read_csv('data//totaldemand_nsw.csv', + names = ['date_time', 'total_demand', 'region_id'], skiprows = 1) +df_demand.date_time = pd.to_datetime( + df_demand.date_time, format = "%d/%m/%Y %H:%M") + +### Forecast dataset +df_forecast = pd.read_csv('data//forecastdemand_nsw.csv', + names = ['id', 'region_id', 'period_id', 'forecast_demand', 'date_time_forecast', 'date_time'], + skiprows = 1) +df_forecast.date_time_forecast = pd.to_datetime( + df_forecast.date_time_forecast, format = "%Y-%m-%d %H:%M:%S") +df_forecast.date_time = pd.to_datetime( + df_forecast.date_time, format = "%Y-%m-%d %H:%M:%S") + +### Merged dataset +df_all = pd.read_csv('data//combined_data.csv', + names = ['id','period_id','forecast_demand','date_time_current','date_time_future', + 'date_time_current_rounded','total_demand','temperature_future','temperature_current', + 'temperature_future_forecast','humidity_future_forecast','rain_future_forecast', + 'wind_speed_future_forecast','forecast_interval'], skiprows = 1) +df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = "%Y-%m-%d %H:%M:%S") +df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = "%Y-%m-%d %H:%M:%S") +df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, + format = "%Y-%m-%d %H:%M:%S") + + +``` + +## Electricity Demand + + +Electricity demand shows a cyclical pattern with a downward trend when observing it throughout the years (Figure \@ref(fig:yeardemand)). This may be due to more households investing in embedded generation to supplement their electricity supply. + + +```{python yeardemandPlot, fig.cap = "Electricity Demand vs Time", echo = FALSE, eval = FALSE} + +df_demand_14d = df_demand[['date_time','total_demand']].copy() + +df_demand_14d['dem_14d'] = df_demand.total_demand.rolling(window=672).mean() + +plt.figure(figsize=(6, 4)) +plt.plot(df_demand_14d['date_time'], df_demand_14d['dem_14d'], + label='14-Day Rolling Avg', color='blue') +plt.xlabel('Year') +plt.ylabel('Total Demand (MW)') +plt.legend() +plt.show() + +``` + + +```{r yeardemand, fig.cap = 'Electricity Demand vs Time', echo = FALSE, out.width ="100%", out.height="40%"} + +knitr::include_graphics('images/demandvtime.png') + +``` + + +Electricity demand is higher during winter and summer months (Figure \@ref(fig:monthdemand)). This is likely due to higher consumption of electricity to power heating and cooling appliances. + +```{python monthdemandPlot, fig.cap = "Total Demand by Month", echo = FALSE, eval = FALSE} + +df_demand_month = df_all[['date_time_current','total_demand']].copy() + +df_demand_month['month'] = df_demand_month.date_time_current.dt.month +df_demand_month['month_name'] = df_demand_month.date_time_current.dt.month_name().str[:3] + +plt.figure(figsize = (6,4)) +sns.boxplot(data = df_demand_month.groupby("date_time_current", as_index = False).first().sort_values("month"), + x = 'month_name', y = "total_demand", hue = 'month', palette = 'Blues', showfliers = False, legend = False) +plt.xlabel('Month') +plt.ylabel('Total Demand (MW)') +plt.grid(alpha = 0.5) + +``` + + +```{r monthdemand, fig.cap = "Total Demand by Month", out.width ="100%", out.height="40%", echo=FALSE} + +knitr::include_graphics('images/demandMonth.png') + +``` + + +Demand was observed to be greater in weekdays than weekends (Figure \@ref(fig:weekdemand)). This may be due to many businesses closing during weekends. + + +```{python weekdemandPlot, fig.cap = "Total Demand by Day of the Week", echo = FALSE, eval = FALSE} + +df_demand_weekday = df_all[['date_time_current','total_demand']].copy() + +df_demand_weekday['weekday'] = df_demand_weekday.date_time_current.dt.day_of_week +df_demand_weekday['weekday_name'] = df_demand_weekday.date_time_current.dt.day_name() + +plt.figure(figsize = (6,4)) +sns.boxplot(data = df_demand_weekday.groupby("date_time_current", + as_index = False).first().sort_values("weekday"), x = 'weekday_name', y = "total_demand", + hue = 'weekday', palette = 'Blues', showfliers = False, legend = False) +plt.grid(alpha = 0.5) +plt.xlabel('Day of the Week') +plt.ylabel('Total Demand (MW)') + +``` + + +```{r weekdemand, fig.cap = "Total Demand by Day of the Week", out.width ="100%", out.height="40%", echo=FALSE} + +knitr::include_graphics('images/WeekDemand.png') + +``` + + +Electricity demand is relatively high between 8am and 11pm (Figure \@ref(fig:hourdemand)), likely due to the human sleeping cycle. + + +```{r hourdemand, fig.cap = "Total Demand by Hour of Day", out.width ="100%", out.height="30%", echo=FALSE} + +knitr::include_graphics('images/demandHour.png') + +``` + + +## Forecast Electricity Demand + +This dataset contains electricity demand forecasts made every 30 minutes. Each time a forecast is made, it includes 48 predictions—one for each half-hour period from 30 minutes ahead up to 24 hours ahead. + +\bigskip + +The scatter plot of electricity demand forecasts vs actual electricity demand across different prediction time periods (Figure \@ref(fig:forecastdem)), reveals lower correlation as both the prediction time period and actual electricity demand increase. + + +```{python forecastdemPlot, include = FALSE, eval = FALSE } +df_forecast['forecast_hours'] = (df_forecast['date_time_forecast'] - df_forecast['date_time']).dt.total_seconds() / 3600 + +merged_df = forecast_df.merge( + actual_df, + on=['date_time'], + how='inner' +) + +hours = [6, 12, 18, 24] +dfs = { + h: merged_df[merged_df['forecast_hours'].round() == h].sample(n=3000, random_state=42) + for h in hours +} + +fig, axes = plt.subplots(2, 2, figsize=(8, 6)) +for ax, h in zip(axes.flat, hours): + sns.regplot( + data=dfs[h], + x='total_demand', + y='forecast_demand', + line_kws={'color': 'red'}, + ax=ax + ) + ax.set_title(f'{h}-Hour Ahead Forecast') + ax.set_xlabel('Actual Demand') + ax.set_ylabel('Forecast Demand') + ax.axhline(y = 9000, + color = 'green') + leg = ax.legend(['Correlation points', 'Trendline','', + 'Forecast = 9000']) + sns.move_legend(ax, "upper right") + +plt.tight_layout() +plt.show() +``` + + +```{r forecastdem, fig.cap = {'Scatter plots of Forecast Demand vs Total Demand by Lag interval'}, out.width ="100%", out.height="40%", echo=FALSE} + +knitr::include_graphics('images/forecastdemscatter.png') + +``` + + +## Weather Variables vs Electricity Demand + +In this next section, we will examine if and how weather affects both the electricity demand and its forecast. + +\bigskip + +The correlation of relevant weather variables with electricity demand shows weak correlation across all variables, with humidity having the highest correlation and rain having the lowest (Figure \@ref(fig:weatherdemand)). + + +```{python demandvweatherPlot, eval = FALSE, echo = FALSE} +df_corr_demand = df_all[['total_demand', 'temperature_future_forecast', + 'humidity_future_forecast','rain_future_forecast', + 'wind_speed_future_forecast']] + +df_corr_demand = df_corr_demand.rename(columns={'temperature_future_forecast': 'Temperature Forecast', + 'humidity_future_forecast': 'Humidity Forecast', 'rain_future_forecast': 'Rain Forecast', + 'wind_speed_future_forecast': 'Wind Speed Forecast'}) + +correlation_demand = df_corr_demand.corr() +correlationsD = correlation_demand['total_demand'].drop('total_demand') + +plt.figure(figsize=(6, 4)) +correlationsD.sort_values().plot(kind='barh', color=plt.cm.coolwarm(np.abs(correlationsD)/max(abs(correlationsD)))) +plt.title('Correlation with Electricity Demand') +plt.xlabel('Correlation Coefficient') +plt.axvline(x=0, color='k', linestyle='-', alpha=0.3) +plt.grid(axis='x', alpha=0.3) +plt.tight_layout() +plt.show() + +``` + + +```{r weatherdemand, fig.cap = "Correlation of Weather variables with Electricity Demand", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/WeatherVDemand.png') + +``` + + + +```{python forecastvtempPlot, echo = FALSE, eval = FALSE} + +df_corr_forecast = df_all[['forecast_demand', + 'temperature_future_forecast','humidity_future_forecast','rain_future_forecast', + 'wind_speed_future_forecast']] + +df_corr_forecast = df_corr_forecast.rename(columns={'temperature_future_forecast': 'Temperature Forecast', + 'humidity_future_forecast': 'Humidity Forecast', 'rain_future_forecast': 'Rain Forecast', + 'wind_speed_future_forecast': 'Wind Speed Forecast'}) + +correlation_forecast = df_corr_forecast.corr() +correlationsF = correlation_forecast['forecast_demand'].drop('forecast_demand') + +plt.figure(figsize=(10, 6)) +correlationsF.sort_values().plot(kind='barh', color=plt.cm.coolwarm(np.abs(correlationsF)/max(abs(correlationsF)))) +plt.xlabel('Correlation Coefficient') +plt.axvline(x=0, color='k', linestyle='-', alpha=0.3) +plt.grid(axis='x', alpha=0.3) +plt.tight_layout() +plt.show() + +``` + + +The plot of temperature against electricity demand reveals a distinct U-shaped correlation. This pattern reflects energy usage behaviour in response to extreme temperatures (Figure \@ref(fig:tempcorr)). The lowest demand levels generally occur in temperate conditions where neither heating nor cooling is heavily used. + + +```{python tempcorrCodePlot, eval = FALSE, include = FALSE} + +df_sampletemp = df_all[['temperature_current', 'total_demand']].sample(10000, random_state=42) + +coeffs = np.polyfit(df_sampletemp.temperature_current, df_sampletemp.total_demand, 2) +trend = np.poly1d(coeffs) + + +x_fit = np.linspace(df_sampletemp.temperature_current.min(), df_sampletemp.temperature_current.max(), 500) +y_fit = trend(x_fit) + +plt.figure(figsize=(6, 4)) +plt.scatter(df_sampletemp.temperature_current, df_sampletemp.total_demand, alpha=0.7, label='Data points') +plt.plot(x_fit, y_fit,linewidth=2, label='Quadratic Fit', color = "red") +plt.xlabel("Temperature (°C)") +plt.ylabel('Electricity Demand (MW)') +plt.legend() +plt.tight_layout() +plt.show() + +``` + + +```{r tempcorr, fig.cap = "Scatter plot of Electricity demand vs Temperature", out.width ="100%", out.height="35%", echo=FALSE} + +knitr::include_graphics('images/tempvsdemand.png') + +``` + + +## Forecast Error of Electricity Demand + + +In this section, we will explore whether forecast inaccuracies are correlated with known variables which contribute to electricity demand. Forecast error was defined as actual demand less forecast demand. + +\bigskip + +```{python forecasterrorCalculation, eval = FALSE, echo=FALSE} + +#Calculating forecast error +df_all["forecast_error"] = df_all.total_demand - df_all.forecast_demand +df_all["forecast_error_relative"] = df_all.forecast_error/df_all.total_demand + +``` + + +Figure \@ref(fig:errorvtemp) shows that forecast error increases with temperature for temperatures greater than ~29°C. This suggests the current forecasting model may lack information regarding forecasted temperatures. The trend also occurs for normalised demand (Figure \@ref(fig:relerrorvtemp)). + + +```{python errorvtempPlot, fig.cap = "Forecast Error vs Temperature Forecast", echo = FALSE, eval = FALSE, out.width ="100%", out.height="20%"} + +df_error = df_all[ + ["temperature_future_forecast","forecast_error", "forecast_error_relative"]].copy() +df_error.temperature= df_all.temperature_future_forecast.round() + +plt.figure(figsize = (6,4)) +sns.boxplot(data=df_error, x="temperature_future_forecast", y="forecast_error", fliersize = 1) +plt.axhline(0, color='r', alpha = 0.2) +plt.xticks(rotation = 90); +plt.xlabel("Temperature (°C)") +plt.ylabel("Forecast error (MW)") +plt.title("Accuracy of forecasting 24h into the future") + +``` + + +```{r errorvtemp, fig.cap = "Forecast Error vs Temperature Forecast", out.width ="100%", out.height="40%", echo=FALSE} + +knitr::include_graphics('images/forecastErrorTemp.png') + +``` + + +```{python relerrorvtempPlot, fig.cap = "Forecast Error as Portion of Actual Demand vs Temperature Forecast",echo = FALSE, eval=FALSE} + +plt.figure(figsize = (6,4)) +sns.boxplot(data=df_error, x="temperature_future_forecast", y="forecast_error_relative", fliersize = 1) +plt.axhline(0, color='r', alpha = 0.2) +plt.xticks(rotation = 45); +plt.xlabel("Temperature (°C)") +plt.ylabel("Forecast Error as Portion of Actual Demand") + +``` + + +```{r relerrorvtemp, fig.cap = "Normalised forecast error vs temperature forecast", out.width ="100%", out.height="40%", echo=FALSE} + +knitr::include_graphics('images/PortionErrorTemp.png') + +``` + + +### Time Series Analysis + +Time series analysis of the forecast error was conducted to understand whether errors persisted with time. It was conducted for 6, 12, 18 and 24-hour forecasts. + +\bigskip + +Augmented Dickey-Fuller test (ADF Test) was conducted. It showed significant evidence for stationary forecast errors (Table \@ref(tab:tab3)). + +\begin{table}[H] +\caption{ADF tests conducted for 6, 12, 18 and 24-hour forecasts} +\centering +\begin{tabular}{||c||c||} +\hline +Hour of day = 6 & Hour of Day = 18 \\ +\hline +ADF Statistic: -33.863838 & ADF Statistic: -31.926828 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.430 & 1\%: -3.430 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\hline +Hour of Day = 12 & Hour of Day = 24 \\ +\hline +ADF Statistic: -33.407029 & ADF Statistic: -29.030458 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.430 & 1\%: -3.430 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\end{tabular} +(\#tab:tab3) +\end{table} + +Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots were generated to understand the relationship between forecast errors and lagged versions of itself over successive time lags (Figure \@ref(fig:acfErrors), Figure \@ref(fig:pacfErrors)). PACF plots showed that forecast errors were significantly partially correlated with the most recent forecasts and ones made 24 and 48 hours prior. The partial correlation of the 24-hour lagged forecast error was of note due its greater significance than the 48-hour lag and its availability when forecasting. + + +```{r acfErrors, fig.cap = "ACF of forecast errors", out.width ="100%", out.height="65%", echo=FALSE} + +knitr::include_graphics('images/ACFForecastErrors.png') + +``` + + +```{r pacfErrors, fig.cap = "PACF of forecast errors", out.width ="100%", out.height="60%", echo=FALSE} + +knitr::include_graphics('images/PACFForecastErrors.png') + +``` + + +Scatterplots and correlations for forecast errors and its 24-hour lagged error can be seen in Figure \@ref(fig:laggedError). + +```{r laggedError, fig.cap = "Scatterplots and correlations for forecast errors and its 24-hour lagged errors", out.width ="100%", out.height="60%", echo=FALSE} + +knitr::include_graphics('images/ForecastErrorCorrelations.png') + +``` + +## Summary of Key Findings + +- **U-shaped relationship between temperature and electricity demand:** Electricity demand increases during both extreme cold and extreme heat conditions, with the lowest demand observed during temperate conditions. This pattern is consistent with expected heating and cooling behavior and is evident in both actual and forecasted demand data. + +- **Forecasting models capture seasonal trends:** Forecasted electricity demand shows a similar U-shaped relationship with temperature, indicating that the models are aligned with seasonal usage patterns. + +- **Forecast error increases non-linearly with temperature, especially during extreme heat:** Forecast accuracy deteriorates significantly at higher temperatures, suggesting that current models underperform during periods of extreme heat. In comparison, performance during extreme cold is better, though still less accurate than under mild conditions. + +- **Forecast errors are autocorrelated with past errors:** Forecast errors may be modelled by understanding historical forecast errors. Of note, the 24-hour lagged forecast error may be used for improving forecasts. + +- **The forecast has the potential to be improved:** These findings highlight some correlations between forecast errors, temperature and time variables such as season which may indicate the model could be improved by modelling forecast errors. + +# Analysis and Results + +## Performance measures + +Two common performance measures were chosen to calculate prediction accuracy and compare models. Mean square error (MSE) and mean absolute percentage error (MAPE) were chosen to be the most appropriate measures. MSE penalises large errors which is useful to assess when the aim is to reduce large errors. Furthermore, MAPE provides an easily interpretable and comparable result. The measures are described below: + +\bigskip + +MSE is the average of the squared difference between actual demand and forecasted demand. + +\begin{equation*} +\text{MSE} = \frac{1}{n}\sum^{n}_{i=1}{(Y_i-{\hat{Y}_{i}})^2} +\end{equation*} + +MAPE takes the absolute value of the difference between the actual demand and forecast demand expresses it as a percentage of actual demand, and takes the average of this. + +\begin{equation*} +\text{MAPE} = \frac{1}{n}\sum^{n}_{i=1}{\frac{\lvert A_{i}-F_{i}\rvert}{A_{i}} * 100} +\end{equation*} + +For both measures, a smaller value represents higher accuracy, and a better performing model. + +\bigskip + +MSE and MAPE values for the original forecast can be seen below. + + +```{python originalMSEandMAPECalculation, eval = FALSE, echo=FALSE} + +delta = 24 + +df_lag = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) +df_lag_temp = df_lag.copy()[["forecast_error", + "forecast_error_relative", "date_time_future"]].rename({ + "forecast_error" : "forecast_error_24h_ago", + "forecast_error_relative": + "forecast_error_relative_24h_ago", + "date_time_future": "date_time_future_24h_ago"}, axis = 1) +df_lag["date_time_current_24h_ago"] = df_lag.date_time_current - + pd.DateOffset(hours = 24) +df_lag["date_time_future_24h_ago"] = df_lag.date_time_future - + pd.DateOffset(hours = 24) + +df_lag = df_lag.loc[df_lag.date_time_future_24h_ago >= +min(df_lag.date_time_future)] +df_lag = pd.merge(df_lag, df_lag_temp, on = + "date_time_future_24h_ago", how = 'left') +df_lag = df_lag.loc[df_lag.forecast_error_relative_24h_ago.notna()] + +train_test_split = 0.7 +split_int = int(train_test_split * len(df_lag)) +df_lag_train, df_lag_test = df_lag[:split_int], df_lag[split_int:] + +mse = mean_squared_error(df_lag_test.forecast_demand, + df_lag_test.total_demand) +mape = mean_absolute_percentage_error(df_lag_test.forecast_demand, + df_lag_test.total_demand) + +print(f"Existing model MSE = {round(mse)}") +print(f"Existing model MAPE = {round(100*mape,2)}%") + +``` +\begin{align*} +\text{Existing model MSE} &= 55159 \\ +\text{Existing model MAPE} &= 2.19\% \\ +\end{align*} + + +## Linear Regression + +Forecast error was defined as actual demand less forecast demand (Equation \@ref(eq:oneA)). + + +\begin{equation} +\varepsilon_t = y_t - \hat{y}_{t} (\#eq:oneA) +\end{equation} + + +Two linear regression models were trained for predicting the forecast error (Equation \@ref(eq:oneB)). The predicted forecast error was then used to update the forecast (Equation \@ref(eq:oneC)). The aim of the models was to reduce the updated forecast error (i.e. $\varepsilon^{*}_t < \varepsilon$). + + +\begin{equation} +\varepsilon_t = \hat{\varepsilon}_t(...) + \varepsilon^{*}_t (\#eq:oneB) +\end{equation} + + +\begin{equation} +\hat{y}^{*}_{t} = \hat{y}_t + \hat{\varepsilon}_{t}(...) + \varepsilon^{*}_t (\#eq:oneC) +\end{equation} + + +### Model Construction +\bigskip +**Linear Regression - Model 1** +\bigskip + +\noindent The first model used only the 24-hour lag forecast error for predicting the forecast error (Equation \@ref(eq:oneD)). Hence, it was a simple autoregressive model. + + +\begin{equation} +\varepsilon^{\text{(LR1)}}_t = \theta_{0} + \theta_{1}\varepsilon_{t-24h} (\#eq:oneD) +\end{equation} + + +The model summary can be seen in Table \@ref(tab:tab4). It showed that all variables are significant at the 0.05 significance level. +\bigskip + + +```{python OLS1Calculation, eval = FALSE, echo=FALSE} + +x_columns = ["forecast_error_24h_ago"] +x = sm.add_constant(df_lag_train[x_columns]) +x = sm.add_constant(x) +y = np.array(df_lag_train.forecast_error) + +model = sm.OLS(y, x) +results = model.fit() +print(results.summary()) + +df_lag_test["lm_forecast_error_pred"] = + results.predict(sm.add_constant(df_lag_test[x_columns])) +df_lag_test["lm_forecast_demand_new"] = + df_lag_test.forecast_demand + df_lag_test.lm_forecast_error_pred + +mse_lm1 = mean_squared_error( + df_lag_test.lm_forecast_demand_new, df_lag_test.total_demand) +mape_lm1 = mean_absolute_percentage_error( + df_lag_test.lm_forecast_demand_new, df_lag_test.total_demand) + +``` + + +\begin{table}[H] +\caption{Linear Regression Model 1, OLS Regression Results} +\centering +\begin{tabular}{lr|lr} +\hline +\hline +\multicolumn{4}{c}{OLS Regression Results} \\ +\hline +\hline +Dep. Variable: & y & R-squared: & 0.113 \\ +Model: & OLS & Adj. R-squared: & 0.113 \\ +Method: & Least Squares & F-statistic: & 2696. \\ +Date: & Sun, 20 Apr 2025 & Prob (F-statistic): & 0.00 \\ +Time: & 11:19:06 & Log-Likelihood: & -1.4233e+05 \\ +No. Observations: & 21064 & AIC: & 2.847e+05 \\ +Df Residuals: & 21062 & BIC: & 2.847e+05 \\ +Df Model: & 1 & & \\ +Covariance Type: & nonrobust & & \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrrrrrr} + & coef & std err & t & P>|t| & [0.025 & 0.975] \\ +\hline +const & 10.5613 & 1.438 & 7.346 & 0.000 & 7.743 & 13.379 \\ +forecast\_error & 0.3369 & 0.006 & 51.927 & 0.000 & 0.324 & 0.350 \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrlr} +Omnibus: & 2737.590 & Durbin-Watson: & 0.201 \\ +Prob(Omnibus): & 0.000 & Jarque-Bera (JB): & 23155.543 \\ +Skew: & -0.344 & Prob(JB): & 0.00 \\ +Kurtosis: & 8.090 & Cond. No. & 222.\\ +\hline +\hline +\end{tabular} +(\#tab:tab4) +\end{table} + +\noindent \textbf{Linear Regression - Model 2} + +\bigskip +\noindent The second model used the 24-hour lag forecast error and all possible explanatory variables for the forecast error identified in EDA (Equation \@ref(eq:oneE)). + + + +\begin{equation} +\begin{split} +\varepsilon^{(LR2)}_{t} = &\theta_0 + \theta_1\varepsilon_{t-24h} +\theta_{2}forecastTemperature_t + \\ +& \theta_{3}forecastHumidity_t + \theta_{4}forecastWind_t + \theta_{5}forecastRain_t + \\ +& \theta_{6}isSaturday_t + \theta_{7}isSunday_t + \theta_{8}isJanuary_t + \\ +& \theta_{9}isNovember_t + \theta_{10}isDecember_t +\end{split} +(\#eq:oneE) +\end{equation} + + +The model summary can be seen in Table \@ref(tab:tab5). It showed that all variables, except \textit{forecastRain}, are significant at the 0.05 significance level. + +\begin{table}[H] +\centering +\caption{Linear Regression Model 2, OLS Regression Results} +\begin{tabular}{lr|lr} +\hline +\hline +\multicolumn{4}{c}{OLS Regression Results} \\ +\hline +\hline +Dep. Variable: & y & R-squared: & 0.124 \\ +Model: & OLS & Adj. R-squared: & 0.124 \\ +Method: & Least Squares & F-statistic: & 298.6 \\ +Date: & Sun, 20 Apr 2025 & Prob (F-statistic): & 0.00 \\ +Time: & 11:19:08 & Log-Likelihood: & -1.4220e+05 \\ +No. Observations: & 21064 & AIC: & 2.844e+05 \\ +Df Residuals: & 21053 & BIC: & 2.845e+05 \\ +Df Model: & 10 & & \\ +Covariance Type: & nonrobust & & \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrrrrrr} + & coef & std err & t & P>|t| & [0.025 & 0.975] \\ +\hline +const & 10.5613 & 1.438 & 7.346 & 0.000 & 7.743 & 13.379 \\ +forecast\_error & 0.3369 & 0.006 & 51.927 & 0.000 & 0.324 & 0.350 \\ +Temperature & -1.1963 & 0.296 & -4.040 & 0.000 & -1.777 & -0.616 \\ +Humidity & 0.9220 & 0.094 & 9.811 & 0.000 & 0.738 & 1.106 \\ +Wind\_speed & 6.7613 & 0.927 & 7.296 & 0.000 & 4.945 & 8.578 \\ +Rain & -3.8983 & 2.807 & -1.389 & 0.165 & -9.399 & 1.603 \\ +isSaturday & 29.1366 & 4.143 & 7.033 & 0.000 & 21.016 & 37.257 \\ +isSunday & 8.2777 & 4.149 & 1.995 & 0.046 & 0.145 & 16.410 \\ +isDecember & 20.6354 & 4.986 & 4.139 & 0.000 & 10.863 & 30.408 \\ +isJanuary & 13.2394 & 5.233 & 2.530 & 0.011 & 2.982 & 23.497 \\ +isNovember & 10.3352 & 4.832 & 2.139 & 0.032 & 0.865 & 19.805 \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrlr} +Omnibus: & 2701.773 & Durbin-Watson: & 0.203 \\ +Prob(Omnibus): & 0.000 & Jarque-Bera (JB): & 23691.264 \\ +Skew: & -0.315 & Prob(JB): & 0.00 \\ +Kurtosis: & 8.157 & Cond. No. & 1.67e+03\\ +\hline +\hline +\end{tabular} +(\#tab:tab5) +\end{table} + +```{python OLS2Calculation, eval = FALSE, echo=FALSE} + +x_columns = ["forecast_error_24h_ago", "Temperature", "Humidity", + "Wind_speed", "Rain", "isSaturday", "isSunday", "isDecember", + "isJanuary", "isNovember"] +x = sm.add_constant(df_lag_train[x_columns]) +x = sm.add_constant(x) +y = np.array(df_lag_train.forecast_error) + +model = sm.OLS(y, x) +results = model.fit() +print(results.summary()) + +df_lag_test["lm2_forecast_error_pred"] = results.predict( + sm.add_constant(df_lag_test[x_columns])) +df_lag_test["lm2_forecast_demand_new"] = + df_lag_test.forecast_demand + df_lag_test.lm2_forecast_error_pred + +mse_lm2 = mean_squared_error(df_lag_test.lm2_forecast_demand_new, + df_lag_test.total_demand) +mape_lm2 = mean_absolute_percentage_error( + df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand) + +``` + +### Model Performance + +The predicted forecast error was then used to update the forecast error (Equation \@ref(eq:oneC). Model evaluation (MSE and MAPE) can be seen below. + +\begin{multicols}{2} +\noindent\textbf{LRegression Model 1 Performance} \\ +- MSE: 49348 \\ +- MAPE: 2.06\% \\ + + +\columnbreak + + +\noindent\textbf{LRegression Model 2 Performance} \\ +- MSE: 49718\\ +- MAPE: 2.078\%\\ +\end{multicols} + +Model 1 performed better as it minimised both MAPE and MSE values. + +## S-ARIMA + +Two SARIMA model collections were trained for predicting the forecast error (Equation \@ref(eq:oneB). The predicted forecast error was then used to update the forecast (Equation \@ref(eq:oneC)). The aim of the models was to reduce the updated forecast error (i.e. $\varepsilon^{*}_{t} < \varepsilon_t$). + +\bigskip + +A model collection contained a SARIMA model for each hour of the day. This reduced overall computation time, while allowing hour of day to be an explanatory variable (note, training the model on all data was not feasible due to limited computing power). The large data size should allow for data segmentation to have minimal impact on model training. + +\bigskip + +EDA, conducted earlier, showed that forecast errors are partially correlated with lagged values of itself in 24-hour intervals. As such, SARIMA modelling only considered lags of 24-hours. + +### Parameter Selection + +ADF tests conducted showed significant evidence for stationary forecast errors, after segmentation by hour of day (Table \@ref(tab:tab6)). As such no differencing (d, D) was considered for SARIMA modelling. + +```{python stationarityTest, eval = FALSE, echo=FALSE} + +def check_stationarity(series): + # Copied from https://machinelearningmastery.com/ + #time-series-data-stationary-python/ + + result = adfuller(series.values) + + print('ADF Statistic: %f' % result[0]) + print('p-value: %f' % result[1]) + print('Critical Values:') + for key, value in result[4].items(): + print('\t%s: %.3f' % (key, value)) + + if (result[1] <= 0.05) & (result[4]['5%'] > result[0]): + print("\u001b[32mStationary\u001b[0m") + else: + print("\x1b[31mNon-stationary\x1b[0m") + +df_all_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + +check_stationarity(df_all_delta.forecast_error_relative) + +``` + + +\begin{table}[H] +\caption{ADF tests for 4, 10, 16, 22-hour forecasts} +\centering +\begin{tabular}{||c||c||} +\hline +Hour of day = 4 & Hour of Day = 10 \\ +\hline +ADF Statistic: -5.875132 & ADF Statistic: -7.680207 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.432 & 1\%: -3.432 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\hline +Hour of Day = 16 & Hour of Day = 22 \\ +\hline +ADF Statistic: -9.601104 & ADF Statistic: -7.968233 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.432 & 1\%: -3.432 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\end{tabular} +(\#tab:tab6) +\end{table} + + +\bigskip + +ACF and PACF plots were generated to assist in SARIMA parameters selection (Figure \@ref(fig:ACF), Figure \@ref(fig:PACF)). The PACF plot showed that, generally, forecast errors are partially correlated with the first lagged term, followed by the next six lagged term, then 1-week and 2-week lags. As such, auto-regressed parameters (p) considered were 1, 2, 6 and 7, and the auto-regressed seasonal parameters (P) considered were 1 and 2. The seasonality parameter (s) was set at 7 for a weekly seasonality. + + +```{r ACF, fig.cap = "ACF of forecast errors with 24-hour lags", echo=FALSE, out.width ="100%", out.height="35%"} + +knitr::include_graphics('images/Stationarity1.png') + +``` + + +```{r PACF, fig.cap = "PACF of forecast errors with 24-hour lags", echo=FALSE, out.width ="100%", out.height="35%"} + +knitr::include_graphics('images/Stationarity2.png') + +``` + + + +\bigskip + +Moving average parameters considered (q, Q) were 0, 1 and 2. A summary of model parameters can be seen in Table \@ref(tab:tab7). + +\bigskip + +\begin{table}[H] +\centering +\caption{SARIMA Model parameters} +\begin{tabular}{|c|ccc|cccc|} +\hline +\multirow{2}{*}{\textbf{ID}} & \multicolumn{3}{c|}{\textbf{ARIMA Order}} & \multicolumn{4}{c|}{\textbf{Seasonal Order}} \\ \cline{2-8} + & \multicolumn{1}{c|}{\textbf{p}} & \multicolumn{1}{c|}{\textbf{d}} & \textbf{q} & \multicolumn{1}{c|}{\textbf{p}} & \multicolumn{1}{c|}{\textbf{D}} & \multicolumn{1}{c|}{\textbf{Q}} & \textbf{s} \\ \hline +0 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 0 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +1 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +2 & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +3 & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{0} & 7 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +4 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 0 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +5 & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{0} & 2 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +6 & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{0} & 2 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{2} & 7 \\ \hline +7 & \multicolumn{1}{c|}{8} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +8 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +\end{tabular} +(\#tab:tab7) +\end{table} + +\bigskip + +MSE and MAPE values were generated for each model in Table \@ref(tab:tab7) (Figure \@ref(fig:MSEsarima), Figure \@ref(fig:MAPEsarima)). Models 5, 6 and 7 equally improved MSE and minimised MAPE values. Model 5 was selected as it was the least complex of the three. + + +```{r MSEsarima, fig.cap = "MSE improvement for each SARIMA model", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/MSEarima.png') + +``` + + +```{r MAPEsarima, fig.cap = "MAPE for each SARIMA model", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/MAPEtuning.png') + +``` + + +### Model Construction + +**SARIMA - Model 1** + +\bigskip +The first model used only lagged versions of the forecast error for predicting the forecast error (Equation \@ref(eq:sarima1)). + +\begin{equation} +\varepsilon^{\text{(SARIMA1)}}_{t} = \text{SARIMA}(6,0,2)(1,0,1,7) +(\#eq:sarima1) +\end{equation} +\ +\noindent \textbf{SARIMA - Model 2} + +\bigskip + +The second model used lagged versions of the forecast error and exogenous data (forecast temperature, humidity, wind and rainfall) for predicting the forecast error (Equation \@ref(eq:sarima2)). + +\begin{equation} +\begin{split} +\varepsilon^{\text(SARIMA2)}_t =& \text(SARIMA)(6,0,2)(1,0,1,7) + \\ +& \theta_{1}forecastTemperature_t + \\ +& \theta_{2}forecastHumidity_t + \theta_{3}forecastWind_t + \\ +& \theta_{4}forecastRain_t +\end{split} +(\#eq:sarima2) +\end{equation} + +### Model Performance + +The predicted forecast error was then used to update the forecast error (Equation \@ref(eq:oneC)). Model evaluation (MSE and MAPE) can be seen below. + +```{python mseCalculation, eval=FALSE,echo=FALSE} + +df_predict["forecast_error_old"] = df_predict.total_demand - df_predict.forecast_demand +df_predict["forecast_error_new"] = df_predict.total_demand - df_predict.new_forecast +df_predict_with_exog["forecast_error_new"] = df_predict_with_exog.total_demand - + df_predict_with_exog.new_forecast + +mse_pre = mean_squared_error(df_predict.total_demand, df_predict.forecast_demand) +mse_sarima = mean_squared_error(df_predict.total_demand, df_predict.new_forecast) +mse_sarima_with_exog = mean_squared_error(df_predict_with_exog.total_demand, + df_predict_with_exog.new_forecast) + +mape_pre = mean_absolute_percentage_error(df_predict.total_demand, df_predict.forecast_demand) +mape_sarima = mean_absolute_percentage_error(df_predict.total_demand, df_predict.new_forecast) +mape_sarima_with_exog = mean_absolute_percentage_error(df_predict_with_exog.total_demand, + df_predict_with_exog.new_forecast) + +``` + +\begin{multicols}{3} +\noindent\textbf{Old Model}\\ +\textbf{Performance}\\ +- MSE: 55078.33198 \\ +- MAPE: 2.178\% \\ + + +\columnbreak + + +\noindent\textbf{SARIMA Model} \\ +\textbf{(no Exog) Performance} \\ +- MSE: 49519.31974\\ +- MAPE: 2.049\%\\ + +\columnbreak + + +\noindent\textbf{SARIMA Model (with} \\ +\textbf{Exog) Performance} \\ +- MSE: 49165.32932 \\ +- MAPE: 2.082\% \\ +\end{multicols} + +Model 1 performed better as it minimised MAPE, which was given greater importance. + +## Random Forest +Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the average prediction of the individual trees. + +The Random Forest model discussed aims to reduce the demand forecast error by predicting demand directly rather than predicting the error and then updating the original forecast. + +### Model Construction + +**RFMF1 - Model 1** +The first model used the lag of the forecast error. The accuracy of predictions improved slightly in this model. +\bigskip + +\noindent\textbf{RFMF1 - Model 2} +The second model used the lag of the forecast error and included weather variables (forecast temperature, wind speed, humidity and rain). The model predictions improved in this model (Figure \@ref(fig:ForestModel2)). + + +### Parameter Selection (Fine Tuning) + +Fine tuning was done by utilising Grid Search on the Random Forest Model. + +\bigskip + +By trialing many parameters combinations, the following combination was found to be the best performing. + + +\begin{align*} +n\_estimators&=200\\ +max\_depth&=10\\ +min\_samples\_split&=5\\ +min\_samples\_leaf&=2\\ +\end{align*} + + +\noindent Where $n\_estimator$ is the number of trees, $max\_depth$ is the maximum depth of each individual tree, $min\_samples\_split$ is the minimum number of samples required to split an internal node and, $min\_samples\_leaf$ is the minimum number of samples required to be at a leaf node. + +### Model Performance + +Setting up models with the values above had the following results (Figure \@ref(fig:ForestModel1), Figure \@ref(fig:ForestModel2)): + + +\begin{multicols}{2} +\noindent\textbf{RFMF1 Performance} \\ +- MSE: 51971.086 \\ +- MAPE: 2.111% \\ + + +\columnbreak + + +\noindent\textbf{RFMF2 Performance} \\ +- MSE: 51395.334\\ +- MAPE: 2.095%\\ +\end{multicols} + + +```{r ForestModel1, fig.cap = "Line Plot of RFMF1's Performance vs Original Forecast Model Performance", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/ForestModel1.png') + +``` + + +```{r ForestModel2, fig.cap = "Line Plot of RFMF2's Performance vs Original Forecast Model Performance", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/ForestModel2.png') + +``` + +## XGBoost + +Extreme Gradient Boosting (XGBoost) is a machine learning algorithm that utilises gradient boosting decision trees that generates fast and effective models used for forecasting, classification and regression problems. + +\bigskip + +As discussed above, forecasting has been seen to improve when incorporating the new weather forecast values combined with previous errors. The overall aim being to reduce the demand forecast error. + +\bigskip + +The XGBoost model discussed aims to reduce the demand forecast error by predicting demand directly rather than predicting the error and then updating the original forecast. + +### Model Construction + +The base model involved using forecasted temperature, humidity, wind speed and rain, combining that with the hour, month and the day of week. Taking the model to the next level involved including the previous forecasted demand and the previous forecast error from 24 hours, 48 hours, 7 days and 14 days ago. + +\bigskip + +Assessing where a base model performs worse based on hour of day yields the following. + +\bigskip + +```{r MAPEXGBoost, fig.cap = "MAPE by Hour of the Day", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/MAPEXGBoost.png') + +``` + +\bigskip + +Taking this error as a wave format, the model improved when variables were combined with a sin or cos wave. Specifically, combining hour with sin/cos wave and then multiplying by forecasted temperature improved the model. + +### Parameter Selection (Fine Tuning) + +Fine tuning is especially important for XGBoost and a grid search was utilised to find the highest performing combination from a wide distribution of parameters. The following was found to be the most effective combination of parameters. + +\begin{center} +\begin{align*} +learning\_rate &= 0.1,\\ +n\_estimators &= 150,\\ +max\_depth &= 3,\\ +subsample &= 0.8 +\end{align*} +\end{center} + +### Model Performance + +Utilising the above parameters gave accuracy scores of + +\begin{align*} +\text{MSE} &= 46526.86 \\ +\text{MAPE} &= 2.042\% +\end{align*} + +### Combining variables + +Introducing new variables as functions of other variables boosted the performance of XGBoost. Whilst in theory introducing variables such as Temperature * Humidity could improve the model, introducing them created unnecessary complexity that reduced the accuracy of the model. It could also been seen that XGBoost took these into account inside the algorithm. + + +## Model Comparison + +Comparison of all models tested against the baseline AEMO model (Table \@ref(tab:tab8)). The XG Boost model produced the highest level of accuracy of the models considered. This is further evident when observing prediction error distribution (Figure \@ref(fig:Cpmparison)). + + +\begin{table}[H] +\caption{Models compared by MSE and MAPE} +\centering +\begin{tabular}{|ll|cc|} +\hline +\multicolumn{2}{|l|}{\multirow{2}{*}{}} & \multicolumn{2}{c|}{\textbf{Measure}} \\ \cline{3-4} +\multicolumn{2}{|l|}{} & \multicolumn{1}{c|}{MSE} & MAPE \\ \hline +\multicolumn{1}{|l|}{\multirow{5}{*}{\textbf{Model}}} & {\textbf{AEMO}} & \multicolumn{1}{c|}{{55,159}} & {2.190\%} \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{Linear Regression} & \multicolumn{1}{c|}{49,718} & 2.080\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{SARIMA} & \multicolumn{1}{c|}{49,519} & 2.049\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{XGBoost} & \multicolumn{1}{c|}{46,526} & 2.042\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{Random Forest} & \multicolumn{1}{c|}{51,395} & 2.095\% \\ \hline +\end{tabular} +(\#tab:tab8) +\end{table} + + +```{r Cpmparison, fig.cap = "MSE Distribution", echo=FALSE, out.width ="100%", out.height="30%"} + +knitr::include_graphics('images/Comparison.png') + +``` + + +## Summary of Key Findings + +- **All models outperformed AEMO’s model in terms of MSE and MAPE.** This indicates that there are likely underlying patterns in the AEMO residuals that are not currently captured in their model, therefore their model could be improved. This also validates forecasting methodologies which aims to use forecasting errors in modelling to further improve forecasting accuracy. + +- **XGBoost performed best compared with linear regression, SARIMA and Decision trees.** This may indicate that some underlying patterns in AEMO’s forecast errors are likely non-linear, and therefore best forecasted by a black box method that can handle non-linear relationships. + +- **SARIMA also performed strongly**, indicating seasonality in the forecast error. This reinforces EDA findings where correlations existed between lagged forecast errors and current forecast errors. The SARIMA model also performed quite strongly based on MSE and MAPE. Furthermore, the SARIMA model which only included the lagged error performed the strongest, which provides further evidence of this relationship. + +- **Both machine learning models performed better when including weather related variables.** This may indicate a non-linear relationship between the forecast error (at least partly) and weather indicators (note, SARIMA would be limited in its ability to capture non-linearity). + +\newpage + +# Discussion + +## Interpretation of results + +The results of this project demonstrate the validity of using forecast errors in modelling to further improve electricity demand predictions. This is particularly important for short-term energy demand forecasting which relies on the precision of forecasts to balance electricity demand and supply rather than requiring an interpretable model. + +\bigskip + +While XGBoost was the best performing model for the datasets considered, this may not be the case for all forecasts, depending on the underlying patterns in the forecast errors. If patterns in forecast errors are linear, traditional models such as ARIMA may perform better to improve the forecast. On the other hand, this project demonstrated that non-linear trends were present in the forecast errors which allowed the XGBoost model to be the better performing model. + +\bigskip + +Both machine learning methods performed better when incorporating weather indicator variables which may point to different models capturing different types of relationships (e.g. SARIMA capturing the effect of the lagged error and XGBoost capturing effects of weather-related variables). This could indicate that modelling the forecast errors of the best model (XGBoost) with a different model (e.g. SARIMA) has the potential to produce even more accurate results. + +## Implications for energy planning + +Future short-term electricity forecasts should consider how residuals could be used to further improve forecasting accuracy. This could involve decomposing the data in the first instance as others have done and discussed in the literature review, or incorporating residuals from an initial forecasted model in a subsequent model. Improving forecast accuracy will likely have the benefit of greater efficiency in managing electricity generation. + +\bigskip + +It should be noted that the method described in this report has proven useful to increase accuracy of forecast predictions which is essential for short-term forecasting, however may not be appropriate for longer-term forecasting where interpretability of the model is more important. + +## Limitations, challenges, and further research + +While this project was able to improve on the AEMO forecast by modelling the forecast error, there is the potential that this could have been achieved more efficiently if a detailed AEMO forecasting methodology was available. This could have provided a better idea of what information or trends could be missing from the original forecast, and therefore which method would have been most useful to model the residuals. + +\bigskip + +This study only considered 12-hour interval due to the complexity of including several intervals and computational burden. Future research could consider longer or shorter forecast intervals to test the impact of modelling forecasting errors to improve accuracy for different intervals. Further research could also consider forecasting model errors using multiple models to capture the different patterns of errors. + +# Conclusion and Further Issues {} + +Hybrid models are known to improve the accuracy of electricity demand forecasts. This report took the concepts of a hybrid model to improve AEMO’s short-term electricity demand forecast by incorporating AEMO forecast errors in a subsequent model to produce a more accurate prediction. The report found that for all models tested, the accuracy of the short-term forecast improved. This is valuable to industry as balancing the supply and demand of energy requires highly accurate forecasting. While this report only considered a 12-hour forecast interval, future studies could investigate different forecast intervals or multiple-iteration error-corrections to improve the accuracy of energy demand forecasts. + +\newpage + +# References {-} + +\begin{hangparas}{.25in}{1} +Abbasi, R.A., Javaid, N., Ghuman, M.N.J., Khan, Z.A., Ur Rehman, S. \& Amanullah (2019). ‘Short Term Load Forecasting Using XGBoost’, \textit{Advances in Intelligent Systems and Computing}, pp.1120–1131. doi:\url{https://doi.org/10.1007/978-3-030-15035-8_108}. + +AEMO. (2023). Load forecasting. Available at: \url{https://aemo.com.au/-/media/files/electricity/nem/security_and_reliability/power_system_ops/procedures/so_op_3710-load-forecasting.pdf?la=en} [Accessed 24 Mar. 2025]. + +Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H. \& Saidur, R. (2014). ‘A review on applications of ANN and SVM for building electrical energy consumption forecasting’, \textit{Renewable and Sustainable Energy Reviews}, [online] 33, pp.102–109. doi:\url{https://doi.org/10.1016/j.rser.2014.01.069}. + +Ahmad, T. \& Chen, H. (2018). ‘Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment’, \textit{Energy}, 160, pp.1008–1020. doi:\url{https://doi.org/10.1016/j.energy.2018.07.084}. + +Ahmad, W., Ayub, N., Ali, T., Irfan, M., Awais, M., Shiraz, M. \& Glowacz, A. (2020). ‘Towards short term electricity load forecasting using improved support vector machine and extreme learning machine’, \textit{Energies}, 13(11), p.2907. doi:\url{https://doi.org/10.3390/en13112907}. + +Amara, F., Agbossou, K., Dubé, Y., Kelouwani, S., Cardenas, A. \& Hosseini, S.S. (2019). ‘A residual load modeling approach for household short-term load forecasting application’, \textit{Energy and Buildings}, 187, pp.132–143. doi:\url{https://doi.org/10.1016/j.enbuild.2019.01.009}. + +Andronikos, A., Tzelepi, M. \& Tefas, A. (2023). ‘Residual Error Learning for Electricity Demand Forecasting’, In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C. \& Pimenidis, E. (eds) \textit{Engineering Applications of Neural Networks}. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. doi:\url{https://doi.org/10.1007/978-3-031-34204-2_33}. + +Añel, J.A., Pérez-Souto, C., Bayo-Besteiro, S., Prieto-Godino, L., Bloomfield, H., Troccoli, A. \& Laura (2024). ‘Extreme weather events and the energy sector in 2021’, \textit{Weather Climate and Society}. doi:\url{https://doi.org/10.1175/wcas-d-23-0115.1}. + +Ardakani, F.J. \& Ardehali, M.M. (2014). ‘Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types’, \textit{Energy}, 65, pp.452–461. doi:\url{https://doi.org/10.1016/j.energy.2013.12.031}. + +Boroojeni, K.G., Amini, M.H., Bahrami, S., Iyengar, S.S., Sarwat, A.I. \& Karabasoglu, O. (2017). ‘A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon’, \textit{Electric Power Systems Research}, 142, pp.58–73. doi:\url{https://doi.org/10.1016/j.epsr.2016.08.031}. + +Deng, X., Ye, A., Zhong, J., Xu, D., Yang, W., Song, Z., Zhang, Z., Guo, J., Wang, T., Tian, Y., Pan, H., Zhang, Z., Wang, H., Wu, C., Shao, J. \& Chen, X. (2022). ‘Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model’, \textit{Energy Reports}, 8, pp.8661–8674. doi:\url{https://doi.org/10.1016/j.egyr.2022.06.072}. + +Divina, F., García Torres, M., Goméz Vela, F.A. \& Vázquez Noguera, J.L. (2019). ‘A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings’, \textit{Energies}, 12(10), p.1934. doi:\url{https://doi.org/10.3390/en12101934}. + +Ediger, V.Ş. \& Akar, S. (2007). ‘ARIMA forecasting of primary energy demand by fuel in Turkey’, \textit{Energy Policy}, 35(3), pp.1701–1708. doi: \url{https://doi.org/10.1016/j.enpol.2006.05.009}. + +Ertuğrul, Ö.F., Tekin, H. \& Tekin, R. (2020). ‘A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid’, \textit{Electrical Engineering}, 103, pp: 717-728. doi:\url{https://doi.org/10.1007/s00202-020-01114-3}. + +Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R. \& Young, W.A. (2016). ‘An overview of energy demand forecasting methods published in 2005–2015’, \textit{Energy Systems}, 8(2), pp.411–447. doi:\url{https://doi.org/10.1007/s12667-016-0203-y}. + +Klyuev, R.V., Morgoev, I.D., Morgoeva, A.D., Gavrina, O.A., Martyushev, N.V., Efremenkov, E.A. \& Mengxu, Q. (2022). ‘Methods of Forecasting Electric Energy Consumption: A Literature Review’, \textit{Energies}, 15(23), p.8919. doi:\url{https://doi.org/10.3390/en15238919}. + +Kopyt, M., Piotrowski, P. \& Baczyński, D. (2024). ‘Short-Term Energy Generation Forecasts at a Wind Farm—A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models’, \textit{Energies}, 17(23), p.6194. doi:\url{https://doi.org/10.3390/en17236194}. + +Koukaras, P., Mustapha, A., Mystakidis, A. \& Tjortjis, C. (2024). ‘Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models’, \textit{Energies}, 17(6), p.1450. doi:\url{https://doi.org/10.3390/en17061450}. + +Kuo, P.-H. \& Huang, C.-J. (2018). ‘A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting’, \textit{Energies}, 11(1), p.213. doi:\url{https://doi.org/10.3390/en11010213}. + +Liu, X.-Q., Zhang, C., Zhou, Y. \& Liao, H. (2021). ‘Temperature change and electricity consumption of the group living: A case study of college students’, \textit{Science of The Total Environment}, 781, p.146574. doi:\url{https://doi.org/10.1016/j.scitotenv.2021.146574}. + +Maia-Silva, D., Kumar, R. \& Nateghi, R. (2020). ‘The critical role of humidity in modeling summer electricity demand across the United States’, \textit{Nature Communications}, 11, p.1686. doi:\url{https://doi.org/10.1038/s41467-020-15393-8}. + +Manno, A., Martelli, E. and Amaldi, E. (2022). ‘A Shallow Neural Network Approach for the Short-Term Forecast of Hourly Energy Consumption’, \textit{Energies}, [online] 15(3), pp.958. doi:\url{https://doi.org/10.3390/en15030958}. + +Marco G. Pinheiro, Sara C. Madeira, Alexandre P. Francisco, (2023). ‘Short-term electricity load forecasting—A systematic approach from system level to secondary substations’, \textit{Applied Energy}, 332, pp.120493, ISSN 0306-2619, doi:\url{https://doi.org/10.1016/j.apenergy.2022.120493}. + +Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D. and Tjortjis, C. (2024). ‘Energy Forecasting: A Comprehensive Review of Techniques and Technologies’, \textit{Energies}, [online] 17(7), pp.1662. doi:\url{https://doi.org/10.3390/en17071662}. + +OpenWeather. (2025). \textit{Custom Weather Products.} [Online] Available at: \url{https://home.openweathermap.org/marketplace} [Accessed 29 Mar. 2025] + +Pao, H.T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear models. \textit{Energy}, 34(10), pp.1438–1446. doi:\url{https://doi.org/10.1016/j.energy.2009.04.026}. + +Papalexopoulos, A.D. and Hesterberg, T.C. (1990). ‘A regression-based approach to short-term system load forecasting’, \textit{IEEE Transactions on Power Systems}, 5(4), pp.1535–1547. doi:\url{https://doi.org/10.1109/59.99410}. + +Phyo, P.P. and Byun, Y.-C. (2021). ‘Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction’, \textit{Symmetry}, 13(10), pp.1942. doi:\url{https://doi.org/10.3390/sym13101942}. + +Rakpho, P. and Yamaka, W. (2021). ‘The forecasting power of economic policy uncertainty for energy demand and supply’, \textit{Energy Reports}, 7, pp.338–343. doi:\url{https://doi.org/10.1016/j.egyr.2021.06.059}. + +Sanhudo, L., Rodrigues, J. and Filho, Ê.V. (2021). ‘Multivariate time series clustering and forecasting for building energy analysis: Application to weather data quality control’, \textit{Journal of Building Engineering}, 35, pp.101996. doi:\url{https://doi.org/10.1016/j.jobe.2020.101996}. + +Savić, S., Selakov, A. and Milošević, D. (2014). ‘Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas’, \textit{Natural Hazards}, 73(2), pp.373–387. doi:\url{https://doi.org/10.1007/s11069-014-1074-y}. + +Singh, S. and Yassine, A. (2018). ‘Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting’, \textit{Energies}, 11(2), pp.452. doi:\url{https://doi.org/10.3390/en11020452}. + +Suganthi, L. and Samuel, A.A. (2012). ‘Energy models for demand forecasting—A review’, \textit{Renewable and Sustainable Energy Reviews}, 16(2), pp.1223–1240. doi:\url{https://doi.org/10.1016/j.rser.2011.08.014}. + +Tarmanini, C., Sarma, N., Gezegin, C. and Ozgonenel, O. (2023). ‘Short term load forecasting based on ARIMA and ANN approaches’, \textit{Energy Reports}, 9, pp.550–557. doi:\url{https://doi.org/10.1016/j.egyr.2023.01.060}. + +Wang, J., Li, P., Ran, R., Che, Y. and Zhou, Y. (2018). ‘A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree’, \textit{Applied Sciences}, 8(5), pp.689. doi:\url{https://doi.org/10.3390/app8050689}. + +Zhang, S., Guo, Q., Smyth, R. and Yao, Y. (2022). ‘Extreme temperatures and residential electricity consumption: Evidence from Chinese households’, \textit{Energy Economics}, pp.105890. doi:\url{https://doi.org/10.1016/j.eneco.2022.105890}. +\end{hangparas} + +\newpage + +# Appendix {-} + +\appendix + +## Appendix A: Data Processing {-} + +Packages used for data cleaning + +```{python, eval=FALSE} + +import pandas as pd +import numpy as np + +pd.options.mode.chained_assignment = None + +``` + +Importing the data + +```{python importdata, eval=FALSE} + +# Forecast Data +## Reading data and formating data-time columns +df_forecast = pd.read_csv('data/forecastdemand_nsw.csv', names = + ['id', 'region_id', 'period_id', 'forecast_demand', + 'date_time_current', 'date_time_future'], skiprows = 1) +df_forecast.date_time_current = pd.to_datetime( + df_forecast.date_time_current, format = "%Y-%m-%d %H:%M:%S") +df_forecast.date_time_future = pd.to_datetime( + df_forecast.date_time_future, format = "%Y-%m-%d %H:%M:%S") + +## Using 'period_id' to round 'current time' +df_forecast["date_time_current_rounded"] = df_forecast.period_id.apply( + lambda x: pd.Timedelta(hours = x/2)) +df_forecast.date_time_current_rounded = df_forecast.date_time_future - + df_forecast.date_time_current_rounded + +# Demand Data +## Reading data and formating data-time columns +df_demand = pd.read_csv('data/totaldemand_nsw.csv', names = + ['date_time', 'total_demand', 'region_id'], skiprows = 1) +df_demand.date_time = pd.to_datetime(df_demand.date_time, + format = "%d/%m/%Y %H:%M") + + +# Forecast temperature data +df_weather_forecast = pd.read_csv('data/forecast_temperatre.csv') + +df_weather_forecast = df_weather_forecast.rename( + {'forecast dt iso': 'date_time_current_utc', + 'slice dt iso': 'date_time_future_utc', + 'temperature': 'temperature_future_forecast', + 'humidity': 'humidity_future_forecast', + 'rain': 'rain_future_forecast', + 'wind_speed': 'wind_speed_future_forecast'}, axis = 1) + +df_weather_forecast["date_time_current_rounded"] = + pd.to_datetime(df_weather_forecast.date_time_current_utc, + format = "%Y-%m-%d %H:%M:%S +0000 UTC") + pd.Timedelta(hours = 10) +df_weather_forecast["date_time_future"] = + pd.to_datetime(df_weather_forecast.date_time_future_utc, + format = "%Y-%m-%d %H:%M:%S +0000 UTC") + pd.Timedelta(hours = 10) + +df_weather_forecast = + df_weather_forecast[['date_time_current_rounded', 'date_time_future', + 'temperature_future_forecast', 'humidity_future_forecast', + 'rain_future_forecast', 'wind_speed_future_forecast']] + +``` + + +Merging the data + +```{python mergedata, eval = FALSE} + +#Merging Datasets +df_all = pd.merge(df_forecast, df_demand[["date_time", "total_demand"]], + left_on = "date_time_future", right_on = "date_time").drop( + columns = "date_time") + +df_all = pd.merge(df_all, + df_temperature[["date_time_30m", "temperature"]], + left_on = "date_time_future", right_on = "date_time_30m") +df_all = df_all.drop( + columns = ["date_time_30m", "region_id"]).rename( + {"temperature": "temperature_future"}, axis = 1) + +df_all = pd.merge(df_all, + df_temperature[["date_time_30m", "temperature"]], + left_on = "date_time_current_rounded", right_on = "date_time_30m") +df_all = df_all.drop(columns = + "date_time_30m").rename({"temperature": "temperature_current"}, + axis = 1) + +df_all = pd.merge(df_all, df_weather_forecast, on = + ["date_time_current_rounded", "date_time_future"], how = 'left') + + + + +``` + +Format data. + +```{python, eval=FALSE} + +df_all["forecast_interval"] = df_all.date_time_future - + df_all.date_time_current_rounded +df_all["forecast_error"] = df_all.total_demand - + df_all.forecast_demand +df_all["forecast_error_relative"] = + df_all.forecast_error/df_all.total_demand + +df_all["date_time_future_month"] = df_all.date_time_future.dt.month +df_all["date_time_future_year"] = df_all.date_time_future.dt.year +df_all["date_time_future_weekday"] = df_all.date_time_future.dt.dayofweek +df_all["date_time_future_hour"] = df_all.date_time_future.dt.hour + +df_all["week_day_name"] = df_all.date_time_future.dt.day_name() + +df_all["isSaturday"] = df_all.week_day_name.apply( + lambda x: 1 if x == 'Saturday' else 0) +df_all["isSunday"] = df_all.week_day_name.apply( + lambda x: 1 if x == 'Sunday' else 0) + +df_all["isDecember"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 12 else 0) +df_all["isJanuary"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 1 else 0) +df_all["isFebruary"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 2 else 0) +df_all["isNovember"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 11 else 0) + +``` + +\newpage + +## Appendix B: Models {-} + +### B1. AEMO Model {.unlisted .unnumbered} + +Import packages + +```{python, eval=FALSE} + +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import statsmodels.api as sm + +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +from matplotlib.pyplot import figure + +``` + + +```{python, eval = FALSE} + +delta = 24 + +df_lag = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) +df_lag_temp = +df_lag.copy()[ + ["forecast_error", "forecast_error_relative", "date_time_future"] + ].rename({"forecast_error" : "forecast_error_24h_ago", + "forecast_error_relative": "forecast_error_relative_24h_ago", + "date_time_future": "date_time_future_24h_ago"}, axis = 1) +df_lag["date_time_current_24h_ago"] = + df_lag.date_time_current - pd.DateOffset(hours = 24) +df_lag["date_time_future_24h_ago"] = + df_lag.date_time_future - pd.DateOffset(hours = 24) + +df_lag = df_lag.loc[ + df_lag.date_time_future_24h_ago >= min(df_lag.date_time_future)] +df_lag = pd.merge(df_lag, df_lag_temp, + on = "date_time_future_24h_ago", how = 'left') +df_lag = df_lag.loc[df_lag.forecast_error_relative_24h_ago.notna()] + +train_test_split = 0.7 +split_int = int(train_test_split * len(df_lag)) +df_lag_train, df_lag_test = df_lag[:split_int], df_lag[split_int:] + +``` + +```{python, eval = FALSE} + +mse = mean_squared_error(df_lag_test.forecast_demand, + df_lag_test.total_demand) +mape = mean_absolute_percentage_error(df_lag_test.forecast_demand, + df_lag_test.total_demand) + +print(f"Existing model MSE = {round(mse)}") +print(f"Existing model MAPE = {round(100*mape,2)}%") + +``` + +### B2. Linear Regression {.unlisted .unnumbered} + +Import packages + +```{python, eval = FALSE} + +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import statsmodels.api as sm +import warnings + +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +from statsmodels.tsa.stattools import adfuller +from matplotlib.pyplot import figure +from statsmodels.graphics.api import qqplot + +``` + +**Linear Regression Model 1** + +```{python, eval = FALSE} + +x_columns = ["forecast_error_24h_ago"] +x = sm.add_constant(df_lag_train[x_columns]) +x = sm.add_constant(x) +y = np.array(df_lag_train.forecast_error) + +model = sm.OLS(y, x) +results = model.fit() +print(results.summary()) + +df_lag_test["lm_forecast_error_pred"] = + results.predict(sm.add_constant(df_lag_test[x_columns])) +df_lag_test["lm_forecast_demand_new"] = + df_lag_test.forecast_demand + df_lag_test.lm_forecast_error_pred + +mse_lm1 = mean_squared_error(df_lag_test.lm_forecast_demand_new, + df_lag_test.total_demand) +mape_lm1 = mean_absolute_percentage_error( + df_lag_test.lm_forecast_demand_new,df_lag_test.total_demand) + +print(f"\nNew model MSE = {round(mse_lm1)}") +print(f"New model MAPE = {round(100*mape_lm1,3)}%") + +``` + +**Linear Regression Model 2** + +```{python, eval=FALSE} + +x_columns = ["forecast_error_24h_ago", "Temperature", "Humidity", + "Wind_speed", "Rain", "isSaturday", "isSunday", "isDecember", + "isJanuary", "isNovember"] +x = sm.add_constant(df_lag_train[x_columns]) +x = sm.add_constant(x) +y = np.array(df_lag_train.forecast_error) + +model = sm.OLS(y, x) +results = model.fit() +print(results.summary()) + +df_lag_test["lm2_forecast_error_pred"] = + results.predict(sm.add_constant(df_lag_test[x_columns])) +df_lag_test["lm2_forecast_demand_new"] = df_lag_test.forecast_demand + + df_lag_test.lm2_forecast_error_pred + +mse_lm2 = mean_squared_error( + df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand) +mape_lm2 = mean_absolute_percentage_error( + df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand) + +print(f"\nNew model MSE = {round(mse_lm2)}") +print(f"New model MAPE = {round(100*mape_lm2,2)}%") + +``` + +### B2. SARIMA {.unlisted .unnumbered} + +Import packages + +```{python, eval = FALSE} + +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import statsmodels.api as sm +import warnings + +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +from statsmodels.tsa.stattools import adfuller +from matplotlib.pyplot import figure +from sklearn.linear_model import LinearRegression +from statsmodels.tsa.arima.model import ARIMA +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error + +``` + +\noindent SARIMA models considered: + +* order=(1,0,0), seasonal_order=(0, 0, 0, 0) +* order=(1,0,1), seasonal_order=(0, 0, 0, 0) +* order=(7,0,1), seasonal_order=(0, 0, 0, 0) +* order=(7,0,7), seasonal_order=(0, 0, 0, 0) +* order=(1,0,0), seasonal_order=(1, 0, 1, 7) +* order=(6,0,2), seasonal_order=(1, 0, 1, 7) +* order=(6,0,2), seasonal_order=(1, 0, 2, 7) +* order=(6,0,1), seasonal_order=(2, 0, 1, 7) +* order=(2,0,1), seasonal_order=(2, 0, 1, 7) + +\noindent Model evaluation and tuning + +```{python, eval=FALSE} + +sarimas = pd.DataFrame({"order":[(1,0,0), (1,0,1), (7,0,1), (7,0,7), + (1,0,0), (6,0,2), (6,0,2), (6,0,1), + (2,0,1)], + + "seasonal_order": [(0, 0, 0, 0), (0, 0, 0, 0), + (0, 0, 0, 0), (0, 0, 0, 0), + (1, 0, 1, 7), (1, 0, 1, 7), + (1, 0, 2, 7), (2, 0, 1, 7), + (2, 0, 1, 7)]}) +sarimas = sarimas.reset_index().rename({"index": "id"}, axis = 1) + +columns = ["sarima_id", "hour_of_day", "order", "seasonal_order", + "ljung_val", "ljung_p", "jb_val", "jb_p", "hetro_val", "hetro_p", + "skew", "kurtosis", "aic", "bic", "n_observations", "mse_pre", + "mse_post", "mape"] +sarima_tune = pd.DataFrame(columns = columns) + +period_id = 24 + +hours_all = [0, 4, 8, 12, 16, 20] + +for hour_of_day in hours_all: + for index, row in sarimas.iterrows(): + order = row["order"] + seasonal_order = row["seasonal_order"] + sarima_id = row["id"] + + df_all_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + # Model fit + model = ARIMA(df_all_delta.forecast_error, order = order, + seasonal_order = seasonal_order) + model_fit = model.fit() + df_all_delta["predicted_forecast_error"] = model_fit.fittedvalues + df_all_delta["new_forecast"] = df_all_delta.forecast_demand + + df_all_delta.predicted_forecast_error + + # Model Evaluation (MSE) + mse_pre = mean_squared_error(df_all_delta.total_demand, + df_all_delta.forecast_demand) + mse_post = mean_squared_error(df_all_delta.total_demand, + df_all_delta.new_forecast) + mape = mean_absolute_percentage_error(df_all_delta.total_demand, + df_all_delta.new_forecast) + + # Model Evaluation (Crit values) + stat_tests = pd.read_html(model_fit.summary().tables[2].as_html(), + header=None,index_col=0)[0] + ljung_val, ljung_p = stat_tests[1].iloc[0], stat_tests[1].iloc[1], + jb_val, jb_p = stat_tests[3].iloc[0], stat_tests[3].iloc[1], + hetro_val, hetro_p = stat_tests[1].iloc[2], stat_tests[1].iloc[3], + skew, kurtosis = stat_tests[3].iloc[2], stat_tests[3].iloc[3] + + # Model Evaluation (AIC, BIC) + stat_tests = pd.read_html(model_fit.summary().tables[0].as_html(), + header=None,index_col=0)[0] + aic, bic = stat_tests[3].iloc[2], stat_tests[3].iloc[3] + n_observations = stat_tests[3].iloc[0] + sarima_tune = sarima_tune.append(pd.DataFrame([[sarima_id, + hour_of_day, order, seasonal_order, ljung_val, ljung_p, + jb_val, jb_p, hetro_val, hetro_p, skew, kurtosis, aic, + bic, n_observations, mse_pre, mse_post, mape]], + columns=columns), ignore_index=True) + +``` + +```{python, eval = FALSE} +sarima_tune["mse_improvement"] = round(100*(sarima_tune.mse_pre - + sarima_tune.mse_post)/sarima_tune.mse_pre) +sarima_tune = pd.merge(sarima_tune, sarimas, on = + ["order", "seasonal_order"], how = "left").sort_values("id") + +plot = sarima_tune.groupby(["order", "seasonal_order", "hour_of_day"], + as_index = False).mean() +sns.lineplot(data = plot, x = 'hour_of_day', y = 'mape', hue = 'id', + palette = 'pastel', alpha = 1, linestyle = '--') +``` + +```{python, eval=FALSE} +sarima_tune["mse_improvement"] = round(100*(sarima_tune.mse_pre - + sarima_tune.mse_post)/sarima_tune.mse_pre) + +plot = sarima_tune.groupby(["order", "seasonal_order", "hour_of_day"], + as_index = False).mean() +sns.lineplot(data = plot, x = 'hour_of_day', y = 'mse_improvement', + hue = 'id', palette = 'pastel', alpha = 1, linestyle = '--') +``` + +\noindent Parameters chosen + +```{python, eval = FALSE} + +period_id = 24 +arima_order = (6,0,2) +arima_season_order = (1, 0, 1, 7) + +train_test_split = 0.7 + +``` + +\noindent textbf{SARIMA Model 1 - Without Exogenous Variables} + +```{python, eval=FALSE} + +df_predict = pd.DataFrame(columns = ["period_id", "date_time_future", + "new_forecast", "forecast_demand", "total_demand"]) + +for hour_of_day in set(df_all.date_time_future_hour): + df_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + # Test/Train split + split_int = int(train_test_split * len(df_delta)) + df_delta_train, df_delta_test = + df_delta[:split_int], df_delta[split_int:] + x_all, x_train, x_test = df_delta.forecast_error, + df_delta_train.forecast_error, df_delta_test.forecast_error + + # Model - Train Data + arima_model_train = ARIMA(x_train, order = arima_order, + seasonal_order = arima_season_order) + arima_mode_train_fit = arima_model_train.fit() + + # Model - Test Data + arima_model_test = ARIMA(x_all, order = arima_order, + seasonal_order = arima_season_order) + arima_model_test_fit = arima_model_test.filter( + arima_mode_train_fit.params) + + # Predicted Values + arima_model_test_predict = + arima_model_test_fit.predict().loc[split_int:] + + # Calculate new forecast + df_delta_test["predicted_forecast_error"] = arima_model_test_predict + df_delta_test["new_forecast"] = df_delta_test.forecast_demand + + df_delta_test.predicted_forecast_error + + # Model evaluation + mse_pre = mean_squared_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mse_post = mean_squared_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + mape_pre = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mape_post = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + + df_predict = pd.concat([df_predict, df_delta_test[["period_id", + "date_time_future", "new_forecast", "forecast_demand", + "total_demand"]]]) + +``` + +\noindent textbf{SARIMA Model 2 - With Exogenous Variables} + +```{python, eval=FALSE} + +df_predict_with_exog = pd.DataFrame(columns = ["period_id", + "date_time_future", "new_forecast", "forecast_demand", + "total_demand"]) +exog_vars = ["Temperature", "Humidity", "Wind_speed", "Rain"] + +for hour_of_day in set(df_all.date_time_future_hour): + df_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + # Test/Train split + split_int = int(train_test_split * len(df_delta)) + df_delta_train, df_delta_test = + df_delta[:split_int], df_delta[split_int:] + x_all, x_train, x_test = + df_delta.forecast_error, df_delta_train.forecast_error, + df_delta_test.forecast_error + exog_all, exog_train, exog_test = + df_delta[exog_vars], df_delta_train[exog_vars], + df_delta_test[exog_vars] + + # Model - Train Data + arima_model_train = ARIMA(x_train, exog = exog_train, + order = arima_order, seasonal_order = arima_season_order) + arima_mode_train_fit = arima_model_train.fit() + + # Model - Test Data + arima_model_test = ARIMA(x_all, exog = exog_all, + order = arima_order, + seasonal_order = arima_season_order) + arima_model_test_fit = + arima_model_test.filter(arima_mode_train_fit.params) + + # Predicted Values + arima_model_test_predict = + arima_model_test_fit.predict().loc[split_int:] + + # Calculate new forecast + df_delta_test["predicted_forecast_error"] = arima_model_test_predict + df_delta_test["new_forecast"] = df_delta_test.forecast_demand + + df_delta_test.predicted_forecast_error + + # Model evaluation + mse_pre = mean_squared_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mse_post = mean_squared_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + mape_pre = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mape_post = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + + df_predict_with_exog = pd.concat([df_predict_with_exog, + df_delta_test[["period_id", "date_time_future", + "new_forecast", "forecast_demand", "total_demand"]]]) + +``` + +Model evaluation + +```{python, eval=FALSE} + +df_predict["forecast_error_old"] = + df_predict.total_demand - df_predict.forecast_demand +df_predict["forecast_error_new"] = + df_predict.total_demand - df_predict.new_forecast +df_predict_with_exog["forecast_error_new"] = + df_predict_with_exog.total_demand - df_predict_with_exog.new_forecast + +mse_pre = mean_squared_error( + df_predict.total_demand, df_predict.forecast_demand) +mse_sarima = mean_squared_error( + df_predict.total_demand, df_predict.new_forecast) +mse_sarima_with_exog = mean_squared_error( + df_predict_with_exog.total_demand, + df_predict_with_exog.new_forecast) + +mape_pre = mean_absolute_percentage_error( + df_predict.total_demand, df_predict.forecast_demand) +mape_sarima = mean_absolute_percentage_error( + df_predict.total_demand, df_predict.new_forecast) +mape_sarima_with_exog = mean_absolute_percentage_error( + df_predict_with_exog.total_demand, + df_predict_with_exog.new_forecast) +``` + +### B3. Random Forest {.unlisted .unnumbered} + +Import packages + +```{python, eval = FALSE} + +import pandas as pd +from sklearn.ensemble import RandomForestRegressor +from sklearn.metrics import mean_absolute_error, mean_squared_error +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +``` + +\noindent Data Processing + +```{python, eval=FALSE} + +# Filter for PERIODID 24 and sort +df_sliced = df[df["period_id"] == 24].copy() +df_sliced = df_sliced.sort_values("date_time_future") +df_sliced = df_sliced.dropna() + +# Get forecast error +df_sliced['forecast_error'] = df_sliced['total_demand'] - + df_sliced['forecast_demand'] +df_sliced['forecast_error_lag24h'] = df_sliced.sort_values( + 'date_time_current_rounded')['forecast_error'].shift(24) + +# Check for NaN counts in key columns +print("\nNaN counts in key columns:") +for col in ['demand_lag_24h', 'demand_lag_48h', 'demand_lag_7d', + 'forecast_error_lag12h']: + if col in df_sliced.columns: + print(f"{col}: {df_sliced[col].isna().sum()} NaNs") + +``` + +\noindent \textbf{RFMF1 - Random Forest Model 1} + +```{python, eval = FALSE} + +# Define features that would be available at prediction +# time (12 hours ahead) +features = [ + # Basic time features + # Original forecast + 'forecast_demand', + 'forecast_error_lag24h' +] +# Define target +target = 'total_demand' + +# Create the modeling dataframe +model_df = df_sliced[features + [target] + + ['date_time_current_rounded']].copy() + +# Print the shape before dropping missing values +print(f"\nShape before dropping missing values: {model_df.shape}") + +# Drop rows with NaN values +model_df = model_df.dropna() +print(f"Shape after dropping NaN values: {model_df.shape}") + +# If we still have no data, show a clear error and exit +if len(model_df) == 0: + print("ERROR: No data left after dropping NaN values!") + import sys + sys.exit(1) + +# Sort data to ensure temporal order +model_df = model_df.sort_values( + 'date_time_current_rounded').reset_index(drop=True) + +# Split data temporally - use 70-30 split +X = model_df[features] +y = model_df[target] +train_size = 0.7 +split_idx = int(len(model_df) * train_size) + +# Split into train/test +X_train = X.iloc[:split_idx] +y_train = y.iloc[:split_idx] +X_test = X.iloc[split_idx:] +y_test = y.iloc[split_idx:] + +# Train model +model = RandomForestRegressor( + n_estimators=200, + max_depth=10, + min_samples_split=5, + min_samples_leaf=2, + random_state=42, + n_jobs=-1 +) +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) +y_pred_original = X_test["forecast_demand"] +``` + +\noindent Evaluate performance + +```{python, eval=FALSE} +# Evaluate performance +def calculate_metrics(y_true, y_pred): + mse = mean_squared_error(y_true, y_pred) + mape = np.mean( + np.abs((y_true - y_pred) / np.maximum(0.001, y_true))) * 100 + return mse, mape + +# Model metrics +model_mse, model_mape = calculate_metrics(y_test, y_pred) + +# Original forecast metrics +original_mse, original_mape = calculate_metrics(y_test, y_pred_original) + +# Print formatted results +print("\nModel Performance:") +print(f"- MSE: {model_mse:.3f}") +print(f"- MAPE: {model_mape:.3f}%") + +print("\nOriginal Forecast Performance:") +print(f"- MSE: {original_mse:.3f}") +print(f"- MAPE: {original_mape:.3f}%") + +# Calculate improvement percentages +improvement_mse = (1 - model_mse/original_mse) * 100 +improvement_mape = (1 - model_mape/original_mape) * 100 + +print("\nImprovement Over Original Forecast:") +print(f"- MSE: {improvement_mse:.3f}%") +print(f"- MAPE: {improvement_mape:.3f}%") + +# Feature importance +feature_importance = pd.DataFrame( + {'Feature': features, + 'Importance': model.feature_importances_} +).sort_values('Importance', ascending=False) + +print("\nFeature Importance:") +print(feature_importance) + + +``` + +\noindent \textbf{RFMF2 - Random Forest Model 2} + +```{python, eval=FALSE} + +# Define features that would be available at prediction +# time (12 hours ahead) +features = [ + 'forecast_demand', + 'Temperature', + 'Humidity', + 'Wind_speed', + 'Rain', + 'forecast_error_lag24h' +] + + +# Define target +target = 'total_demand' + + +# Print the number of NaN values for each feature +print("\nNaN counts in features:") +for feature in features: + print(f"{feature}: {df_sliced[feature].isna().sum()} NaNs") + +# Create the modeling dataframe +model_df = df_sliced[features + [target] ].copy() +# Drop rows with NaN values +model_df = model_df.dropna() +print(f"Shape after dropping NaN values: {model_df.shape}") + + +# Split data temporally - using 70-30 split +X = model_df[features] +y = model_df[target] +train_size = 0.7 +split_idx = int(len(model_df) * train_size) + +# Split into train/test +X_train = X.iloc[:split_idx] +y_train = y.iloc[:split_idx] +X_test = X.iloc[split_idx:] +y_test = y.iloc[split_idx:] + + +# Train model +model = RandomForestRegressor( + n_estimators=200, + max_depth=10, + min_samples_split=5, + min_samples_leaf=2, + random_state=42, + n_jobs=-1 +) +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) +y_pred_original = X_test["forecast_demand"] +``` + +\noindent Evaluate performance + +```{python, eval=FALSE} + +def calculate_metrics(y_true, y_pred): + mse = mean_squared_error(y_true, y_pred) + mape = np.mean( + np.abs((y_true - y_pred) / np.maximum(0.001, y_true))) * 100 + return mse, mape + +# Model metrics +model_mse, model_mape = calculate_metrics(y_test, y_pred) + +# Original forecast metrics +original_mse, original_mape = calculate_metrics(y_test, y_pred_original) + +# Print formatted results +print("\nModel Performance:") +print(f"- MSE: {model_mse:.3f}") +print(f"- MAPE: {model_mape:.3f}%") + +print("\nOriginal Forecast Performance:") +print(f"- MSE: {original_mse:.3f}") +print(f"- MAPE: {original_mape:.3f}%") + +# Calculate improvement percentages +improvement_mse = (1 - model_mse/original_mse) * 100 +improvement_mape = (1 - model_mape/original_mape) * 100 + +print("\nImprovement Over Original Forecast:") +print(f"- MSE: {improvement_mse:.3f}%") +print(f"- MAPE: {improvement_mape:.3f}%") + +# Feature importance +feature_importance = pd.DataFrame( + {'Feature': features, + 'Importance': model.feature_importances_} +).sort_values('Importance', ascending=False) + +print("\nFeature Importance:") +print(feature_importance) + +``` + +### B4. XGBoost {.unlisted .unnumbered} + +Import packages + +```{python, eval =FALSE} + +import pandas as pd +import numpy as np +from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +import xgboost as xgb +import shap +import matplotlib.pyplot as plt + +``` + +\noindent Data Processing + +```{python, eval=FALSE} + +df['24hrpreverrors'] = df['forecast_error'].shift(24) +df['48hrpreverrors'] = df['forecast_error'].shift(48) +df['7daypreverrors'] = df['forecast_error'].shift(24 * 7) +df['14daypreverrors'] = df['forecast_error'].shift(24 * 14) +# Time-based features +df["Hour"] = df.date_time_future.dt.hour +df["MonthNumb"] = df.date_time_future.dt.month +df["Day of week"] = df.date_time_future.dt.dayofweek + +df = df.dropna() + + + +# Encode Hour as cyclic features +df["hour_sin"] = np.sin(2 * np.pi * df["Hour"] / 24) +df["hour_cos"] = np.cos(2 * np.pi * df["Hour"] / 24) + +# Interaction features +df["hour_x_temp"] = df["Hour"] * df["Temperature"] +df["month_x_temp"] = df["MonthNumb"] * df["Temperature"] +df["hour_x_forecast"] = df["Hour"] * df["forecast_demand"] +df["temp_x_forecast"] = df["Temperature"] * df["forecast_demand"] +df["temp_x_hour_sin"] = df["Temperature"] * df["hour_sin"] +df["temp_x_hour_cos"] = df["Temperature"] * df["hour_cos"] +df["forecast_x_hour_sin"] = df["forecast_demand"] * df["hour_sin"] +df["forecast_x_hour_cos"] = df["forecast_demand"] * df["hour_cos"] +df["forecast_24_hour_cos"] = df["24hrpreverrors"] * df["hour_cos"] +df["forecast_24_hour_sin"] = df["24hrpreverrors"] * df["hour_sin"] + +``` + +\noindent Parameter Selection + +```{python, eval=FALSE} + +features = [ + 'Temperature', 'Humidity', + 'Wind_speed', 'Rain', + 'hour_sin', 'hour_cos', + 'MonthNumb', 'Day of week', + 'forecast_demand', + '24hrpreverrors', + '48hrpreverrors', '7daypreverrors', '14daypreverrors', + 'hour_x_temp', 'month_x_temp', 'hour_x_forecast', 'temp_x_forecast', + 'temp_x_hour_sin', 'temp_x_hour_cos', + 'forecast_x_hour_sin', 'forecast_x_hour_cos', + 'forecast_24_hour_cos', 'forecast_24_hour_sin' + +] + +train_df = df[(df['date_time_future'] >= "2017-10-07 23:00:00") & + (df['date_time_future'] <= "2020-03-05 23:00:00")] +test_df = df[(df['date_time_future'] > "2020-03-06 23:00:00") & + (df['date_time_future'] <= "2021-03-17 23:00:00")] + +# Prepare train/test split sets +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, + test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, + test_df['forecast_demand']) * 100 + +# TimeSeriesSplit to respect time order +tscv = TimeSeriesSplit(n_splits=3) + +# Parameter grid for randomized search +param_dist = { + 'n_estimators': [100, 150, 200, 250], + 'max_depth': [3, 4, 5], + 'learning_rate': [0.01, 0.03, 0.05, 0.1], + 'subsample': [0.7, 0.8, 1.0], + 'colsample_bytree': [0.7, 0.8, 1.0] +} + +# Create base model +xgb_model = xgb.XGBRegressor( + objective='reg:squarederror', + tree_method='hist', + random_state=42 +) + +# Randomized search +random_search = RandomizedSearchCV( + estimator=xgb_model, + param_distributions=param_dist, + n_iter=2000, + scoring='neg_mean_absolute_percentage_error', + cv=tscv, + verbose=1, + n_jobs=-1, + random_state=42 +) + +# Run the search +random_search.fit(X_train, y_train) + +# Use the best model +model = random_search.best_estimator_ + +# Optional: Print best parameters +print("Best Parameters:", random_search.best_params_) + +###Output learning_rate=0.1, n_estimators=150, max_depth=3, +### subsample=0.8 + +``` + +\noindent **Tuned Model** + +```{python, eval=FALSE} + +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, + test_df['forecast_demand']) * 100 + +# Model creation, taken from fine tuning +xgb_model = xgb.XGBRegressor(objective='reg:squarederror', + tree_method='hist', random_state=42) + +model = xgb.XGBRegressor( + objective='reg:squarederror', + learning_rate=0.1, + n_estimators=150, + max_depth=3, + subsample=0.8, + random_state=42 +) + +model.fit(X_train, y_train) + + +# Predict and evaluate +y_pred = model.predict(X_test) +model_mse = mean_squared_error(y_test, y_pred) +model_mape = mean_absolute_percentage_error(y_test, y_pred) * 100 +``` + +Evaluate Performance + +```{python, eval=FALSE} +# Results +print(f"Original Forecast MSE: {original_mse:.2f}") +print(f"Original Forecast MAPE: {original_mape:.3f}%") +print(f"XGBoost Tuned Model MSE: {model_mse:.2f}") +print(f"XGBoost Tuned Model MAPE: {model_mape:.3f}%") + + +# Explain model predictions using SHAP +explainer = shap.Explainer(model, X_test) +shap_values = explainer(X_test) +shap_df = pd.DataFrame(shap_values.values, columns=X_test.columns) + +# Forecast demand skews the plot so hide it +filtered_shap_values = shap_df.drop(columns=["forecast_demand"]) +filtered_X_test = X_test.drop(columns=["forecast_demand"]) + +shap.summary_plot( + filtered_shap_values.values, + features=filtered_X_test, + feature_names=filtered_X_test.columns +) + +``` + +## Appendix C: Plots {-} + +Packages used for plotting data. + +```{python appendixBpackages, eval = FALSE} + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +``` + +### Figure \@ref(fig:yeardemand) {.unlisted .unnumbered} + +```{python, eval = FALSE} + +df_demand_14d = df_demand[['date_time','total_demand']].copy() + +df_demand_14d['dem_14d'] = + df_demand.total_demand.rolling(window=672).mean() + +plt.figure(figsize=(6, 4)) +plt.plot(df_demand_14d['date_time'], df_demand_14d['dem_14d'], + label='14-Day Rolling Avg', color='blue') +plt.xlabel('Year') +plt.ylabel('Total Demand (MW)') +plt.legend() +plt.show() + +``` + + +### Figure \@ref(fig:monthdemand) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +df_demand_month = df_demand[['date_time','total_demand']].copy() + +df_demand_month['month'] = df_demand_month.date_time.dt.month +df_demand_month['month_name'] = + df_demand_month.date_time.dt.month_name().str[:3] + +plt.figure(figsize = (6,4)) +sns.boxplot( + data = df_demand_month.groupby( + "date_time", as_index = False).first().sort_values("month"), + x = 'month_name', y = "total_demand", hue = 'month', + palette = 'Blues', showfliers = False, legend = False) +plt.xlabel('Month') +plt.ylabel('Total Demand (MW)') +plt.grid(alpha = 0.5) +plt.show() + + +``` + + +### Figure \@ref(fig:weekdemand) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +df_demand_weekday = df_demand[['date_time','total_demand']].copy() + +df_demand_weekday['weekday'] = df_demand_weekday.date_time.dt.day_of_week +df_demand_weekday['weekday_name'] = + df_demand_weekday.date_time.dt.day_name().str[:3] + +plt.figure(figsize = (6,4)) +sns.boxplot(data = df_demand_weekday.groupby("date_time", + as_index = False).first().sort_values("weekday"), + x = 'weekday_name', y = "total_demand", + hue = 'weekday', palette = 'Blues', showfliers = False, + legend = False) +plt.grid(alpha = 0.5) +plt.xlabel('Day of the Week') +plt.ylabel('Total Demand (MW)') +plt.show() + +``` + + +### Figure \@ref(fig:hourdemand) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +df_hour = df_all.copy() +df_hour["date_time_future_hour"] = df_hour.date_time_future.dt.hour +df_hour = df_hour.sort_values("date_time_future_hour") + + + +plt.figure(figsize = (12,6)) +sns.boxplot(data = df_hour.groupby( + "date_time_future", as_index = False).first().sort_values( + "date_time_future_hour"), + x="date_time_future_hour", y = "total_demand", + palette = 'Blues', showfliers = False) +plt.grid(alpha = 0.5) +plt.title("Hour vs Total Demand"); + +time_decomposition_error_plots(df = df_hour, + x = "date_time_future_hour", time_interval = "Hour", + show_outliers = False, forecast_interval = 12, + show_relative_error_all = True, + show_relative_error_interval = True) + +``` + + +### Figure \@ref(fig:forecastdem) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +forecast_df['forecast_hours'] = (forecast_df['DATETIME'] - + forecast_df['LASTCHANGED']).dt.total_seconds() / 3600 + + +merged_df = forecast_df.merge( + actual_df, + on=['DATETIME'], + how='inner' +) + + +hours = [6, 12, 18, 24] +dfs = { + h: merged_df[ + merged_df['forecast_hours'].round() == h].sample(n=3000, + random_state=42) + for h in hours +} + + +fig, axes = plt.subplots(2, 2, figsize=(8, 6)) +for ax, h in zip(axes.flat, hours): + sns.regplot( + data=dfs[h], + x='TOTALDEMAND', + y='FORECASTDEMAND', + line_kws={'color': 'red'}, + ax=ax + ) + ax.set_title(f'{h}-Hour Ahead Forecast') + ax.set_xlabel('Actual Demand') + ax.set_ylabel('Forecasted Demand') + ax.axhline(y = 9000, + color = 'green') +plt.legend(['Correlation points', 'Trendline','', + 'Forecasted = 9000']) +plt.tight_layout() +plt.show() + +``` + +### Figure \@ref(fig:weatherdemand) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +df_corr_demand = df_all[['total_demand', + 'temperature_future_forecast','humidity_future_forecast', + 'rain_future_forecast','wind_speed_future_forecast']] + +df_corr_demand = df_corr_demand.rename( + columns={'temperature_future_forecast': 'Temperature Forecast', + 'humidity_future_forecast': 'Humidity Forecast', + 'rain_future_forecast': 'Rain Forecast', + 'wind_speed_future_forecast': 'Wind Speed Forecast'}) + +correlation_demand = df_corr_demand.corr() +correlationsD = correlation_demand['total_demand'].drop('total_demand') + +plt.figure(figsize=(10, 6)) +correlationsD.sort_values().plot(kind='barh', + color=plt.cm.coolwarm(np.abs(correlationsD)/max(abs(correlationsD)))) +plt.xlabel('Correlation Coefficient') +plt.axvline(x=0, color='k', linestyle='-', alpha=0.3) +plt.grid(axis='x', alpha=0.3) +plt.tight_layout() +plt.show() + +``` + + +### Figure \@ref(fig:tempcorr) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +hourly_temp = temperature_df.groupby( + ['HOUR', 'LOCATION'])['TEMPERATURE'].mean().reset_index() +print(f"Aggregated temperature data: {len(hourly_temp)} rows") + +merged_df = pd.merge( + demand_df, + hourly_temp, + left_on='HOUR', + right_on='HOUR', + how='inner' +) + + +plt.figure(figsize=(10, 6)) +plt.scatter(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], + alpha=0.5) +plt.title('Relationship between Temperature and Electricity Demand') +plt.xlabel('Temperature (°C)') +plt.ylabel('Total Demand (MW)') +plt.grid(True, alpha=0.3) + +# Add trend line +z = np.polyfit(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], 2) +p = np.poly1d(z) +temp_range = np.linspace(merged_df['TEMPERATURE'].min(), + merged_df['TEMPERATURE'].max(), 100) +plt.plot(temp_range, p(temp_range), "r--", linewidth=2) + +plt.savefig('temperature_vs_demand_scatter.png') + +``` + + +### Figure \@ref(fig:errorvtemp) {.unlisted .unnumbered} + +```{python, eval=FALSE} + + +interval = 60*60 #sets the interval in seconds +df_forecast["forecast_interval"] = df_forecast.date_time_prediction - + df_forecast.date_time_forecast +df_forecast.forecast_interval = df_forecast.forecast_interval.apply( + lambda x: x.total_seconds()/interval) + + +interval_min, interval_max = 23 , 25 #sets a window for forecast periods +df_forecast_near24hour = + df_forecast.loc[(df_forecast.forecast_interval > interval_min) & + (df_forecast.forecast_interval < interval_max)] +df_forecast_near24hour["date_time_forecast_rounded"] = + df_forecast_near24hour.date_time_forecast.apply( + lambda x: x.round(freq='30min')) +df_forecast_near24hour_1instance = + df_forecast_near24hour.loc[ + df_forecast_near24hour.groupby( + "date_time_forecast_rounded")["forecast_interval"].idxmax()] + + +df_forecast_near24hour_1instance_with_demand = + pd.merge(df_forecast_near24hour_1instance, + df_demand, left_on = "date_time_forecast_rounded", + right_on = "date_time") +df_forecast_near24hour_1instance_with_demand["forecast_error"] = + df_forecast_near24hour_1instance_with_demand.total_demand - + df_forecast_near24hour_1instance_with_demand.forecast_demand + +df_forecast_near24hour_1instance_with_demand_temperature = + pd.merge(df_forecast_near24hour_1instance_with_demand, + df_temperature, left_on = "date_time_forecast_rounded", + right_on = "date_time") +df_forecast_near24hour_1instance_with_demand_temperature[ + "forecast_error_relative"] = + df_forecast_near24hour_1instance_with_demand_temperature.forecast_error/ + df_forecast_near24hour_1instance_with_demand_temperature.total_demand + +df_plot = df_forecast_near24hour_1instance_with_demand_temperature[[ + "temperature", "forecast_error", "forecast_error_relative"]].copy() +df_plot.temperature = df_plot.temperature.round() + +plt.figure(figsize = (12,7)) +sns.boxplot(data=df_plot, x="temperature", y="forecast_error", + fliersize = 1) +plt.axhline(0, color='r', alpha = 0.2) +plt.xticks(rotation = 90); +plt.title("Accuracy of forecasting 24h into the future") + +``` + + +### Figure \@ref(fig:relerrorvtemp) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +plt.figure(figsize = (12,7)) +sns.boxplot(data=df_plot, x="temperature", + y="forecast_error_relative", fliersize = 1) +plt.axhline(0, color='r', alpha = 0.2) +plt.xticks(rotation = 90); +plt.ylabel("Forecast Error as Portion of Actual Demand") + +``` + + +### Figure \@ref(fig:acfErrors) \& Figure \@ref(fig:pacfErrors) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +for i, delta in enumerate([12, 24, 36, 48]): + df_all_delta = df_all.loc[ + df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) + delta_24h_later = 48 - delta + previous_lag = 48 + + x = df_all_delta.forecast_error_relative[ + previous_lag:len(df_all_delta)] + y = df_all_delta.forecast_error_relative[ + 0:len(df_all_delta)-previous_lag] + +def check_stationarity(series): + + result = adfuller(series.values) + + print('ADF Statistic: %f' % result[0]) + print('p-value: %f' % result[1]) + print('Critical Values:') + for key, value in result[4].items(): + print('\t%s: %.3f' % (key, value)) + + if (result[1] <= 0.05) & (result[4]['5%'] > result[0]): + print("\u001b[32mStationary\u001b[0m") + else: + print("\x1b[31mNon-stationary\x1b[0m") + +fig1, ax1 = plt.subplots(2,2, figsize = (18, 18)) +fig2, ax2 = plt.subplots(2,2, figsize = (18, 18)) + +i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]} + +for i, period_id in enumerate([12, 24, 36, 48]): + print(f"Forecast Interval = {round(period_id/2)}") + + df_all_delta = df_all.loc[df_all.period_id == + period_id].sort_values( + "date_time_future").reset_index(drop = True) + + check_stationarity(df_all_delta.forecast_error_relative) + + plot_acf(df_all_delta.forecast_error_relative, lags = 100, + ax = ax1[i_subplot[i][0]][i_subplot[i][1]]) + ax1[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Forecast Interval = + {round(period_id/2)}h') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_ylim(0,1) + + plot_pacf(df_all_delta.forecast_error_relative, lags = 100, + ax = ax2[i_subplot[i][0]][i_subplot[i][1]]) + ax2[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Forecast Interval = + {round(period_id/2)}h') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_ylim(-0.5,1) + +#fig1.suptitle('Autocorrelation') +#fig2.suptitle('Partial Autocorrelation') +plt.show() + + +``` + + +### Figure \@ref(fig:laggedError) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +delta = 24 + +df_all_delta = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) + +x = df_all_delta.forecast_error_relative[delta:len(df_all_delta)] +y = df_all_delta.forecast_error_relative[0:len(df_all_delta)-delta] + +plt.subplots(2,2, figsize = (18, 18)) + +for i, delta in enumerate([12, 24, 36, 48]): + df_all_delta = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) + delta_24h_later = 48 - delta + previous_lag = 48 + + x = df_all_delta.forecast_error_relative[ + previous_lag:len(df_all_delta)] + y = df_all_delta.forecast_error_relative[ + 0:len(df_all_delta)-previous_lag] + + #plt.figure(figsize = (12, 9)) + plt.subplot(2,2,i+1) + plt.plot(np.array(x), np.array(y), '.', alpha = 0.3) + #plt.plot(0,0, 'r.') + plt.xlim(-0.15, 0.15) + plt.ylim(-0.15, 0.15) + plt.grid(alpha = 0.5) + plt.xlabel('Relative Forecast Error at Time = t') + plt.ylabel('Relative Forecast Error at Time = t - 24h') + +``` + + +### Figure \@ref(fig:ACF) \& Figure \@ref(fig:PACF) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +df_all["forecast_error_relative"] = + df_all.forecast_error/df_all.total_demand + +df_all["date_time_future_month"] = df_all.date_time_future.dt.month +df_all["date_time_future_year"] = df_all.date_time_future.dt.year +df_all["date_time_future_weekday"] = df_all.date_time_future.dt.dayofweek +df_all["date_time_future_yearTime"] = + df_all.date_time_future_year.apply( + lambda x: pd.DateOffset(years=x-2000)) +df_all["date_time_future_hour"] = df_all.date_time_future.dt.hour + +def check_stationarity(series): + + result = adfuller(series.values) + + print('ADF Statistic: %f' % result[0]) + print('p-value: %f' % result[1]) + print('Critical Values:') + for key, value in result[4].items(): + print('\t%s: %.3f' % (key, value)) + + if (result[1] <= 0.05) & (result[4]['5%'] > result[0]): + print("\u001b[32mStationary\u001b[0m") + else: + print("\x1b[31mNon-stationary\x1b[0m") + + +period_id = 24 + +fig1, ax1 = plt.subplots(2,2, figsize = (18, 10)) +fig2, ax2 = plt.subplots(2,2, figsize = (18, 10)) + +i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]} + +for i, hour_of_day in enumerate([4, 10, 16, 22]): + print(f"Hour of Day = {hour_of_day}") + df_all_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + check_stationarity(df_all_delta.forecast_error_relative) + + plot_acf(df_all_delta.forecast_error_relative, lags = 28, + ax = ax1[i_subplot[i][0]][i_subplot[i][1]]) + ax1[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_title( + f'Hour of Day = {hour_of_day}') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_ylim(0,1) + + plot_pacf(df_all_delta.forecast_error_relative, + lags = 28, ax = ax2[i_subplot[i][0]][i_subplot[i][1]]) + ax2[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_title( + f'Hour of Day = {hour_of_day}') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_ylim(-0.1,1) + +``` + + +### Figure \@ref(fig:MSEsarima) \& Figure \@ref(fig:MAPEsarima){.unlisted .unnumbered} + +```{python, eval=FALSE} + +sarima_tune["mse_improvement"] = + round(100*(sarima_tune.mse_pre - + sarima_tune.mse_post)/sarima_tune.mse_pre) +sarima_tune = pd.merge(sarima_tune, sarimas, + on = ["order", "seasonal_order"], + how = "left").sort_values("id") + +plot = sarima_tune.groupby( + ["order", "seasonal_order", "hour_of_day"], + as_index = False).mean() +sns.lineplot(data = plot, x = 'hour_of_day', y = 'mape', + hue = 'id', palette = 'pastel', alpha = 1, linestyle = '--') + +``` + + +### Figure \@ref(fig:ForestModel1) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +sample = np.random.choice(len(y_test), 100, replace=False) +x_axis = range(len(sample)) + +plt.figure(figsize=(14, 6)) +sns.lineplot(x=x_axis, y=y_test.iloc[sample], + label='Actual Demand', color='black') +sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], + label='Original Forecast', linestyle='--') +sns.lineplot(x=x_axis, y=y_pred[sample], + label='Model Predictions', linestyle='--') +plt.title("Model vs Original Forecast Performance") +plt.ylabel("Demand") +plt.show() + +``` + +### Figure \@ref(fig:ForestModel2) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +sample = np.random.choice(len(y_test), 100, replace=False) +x_axis = range(len(sample)) + +plt.figure(figsize=(14, 6)) +sns.lineplot(x=x_axis, y=y_test.iloc[sample], + label='Actual Demand', color='black') +sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], + label='Original Forecast', linestyle='--') +sns.lineplot(x=x_axis, y=y_pred[sample], + label='Model Predictions', linestyle='--') +plt.title("Model vs Original Forecast Performance") +plt.ylabel("Demand") +plt.show() + +``` + +### Figure \@ref(fig:MAPEXGBoost) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +output_df = test_df.copy() +output_df["xgb_prediction"] = y_pred + +export_cols = ["date_time_future", "total_demand", + "forecast_demand", "xgb_prediction"] +output_df[export_cols].to_csv("finalresultsxgboost.csv", index=False) + + +# Calculate absolute percentage error per row +output_df["abs_pct_error"] = np.abs((output_df["total_demand"] - + output_df["xgb_prediction"]) / output_df["total_demand"]) * 100 + +# Extract hour from datetime +output_df["Hour"] = pd.to_datetime(output_df["date_time_future"]).dt.hour + +# Group by hour and calculate mean error +hourly_error = + output_df.groupby("Hour")["abs_pct_error"].mean().reset_index() + +# Plot +plt.figure(figsize=(10, 5)) +plt.plot(hourly_error["Hour"], hourly_error["abs_pct_error"], marker='o') +plt.title("MAPE by Hour of Day") +plt.xlabel("Hour of Day") +plt.ylabel("MAPE") +plt.grid(True) +plt.xticks(range(0, 24)) +plt.tight_layout() +plt.show() + +``` + + +### Figure \@ref(fig:Cpmparison) {.unlisted .unnumbered} + +```{python, eval=FALSE} + +lm_results = pd.read_csv("data/results_LM.csv") +lm_results["forecast_error"] = lm_results.total_demand - + lm_results.lm_prediction + +sarima_results = pd.read_csv("data/results_SARIMA.csv") +sarima_results["forecast_error"] = sarima_results.total_demand - + sarima_results.sarima_prediction + +xgboost_results = pd.read_csv("data/results_XGBoost.csv") +xgboost_results["forecast_error"] = xgboost_results.total_demand - + xgboost_results.xgb_prediction + +decisionT_results = pd.read_csv('data/results_DecisionTree.csv') +decisionT_results["forecast_error"] = + decisionT_results.total_demand - decisionT_results.model_prediction + +results_all = {"Linear": lm_results, + "SARIMA": sarima_results, + "XGBoost": xgboost_results, + "Decision Tree": decisionT_results} + +colors = ['#1f77b4', '#ff7f0e', 'g', '#7f7f7f'] + +plt.subplots(1, 2, figsize = (16,7)) + +plt.subplot(1,2,1) +for i, model in enumerate(results_all): + model_result = results_all[model] + sns.kdeplot(model_result.forecast_error, label = model, + color = colors[i], alpha = 0.8) + +sns.kdeplot(lm_results.total_demand - + lm_results.forecast_demand, label = "AEMO", color = 'r', ls = '--') +plt.xlim(-1200, 1200); +plt.ylim(0, 0.0025) +plt.xlabel('Forecast Error') +plt.grid() +plt.legend() +plt.title("Distribution of Error"); + +plt.subplot(1,2,2) +for i, model in enumerate(results_all): + model_result = results_all[model] + sns.kdeplot(abs(model_result.forecast_error), + label = model, color = colors[i], alpha = 0.8) + +sns.kdeplot(abs(lm_results.total_demand - + lm_results.forecast_demand), label = "AEMO", color = 'r', + ls = '--') +plt.xlim(0, 1000); +plt.ylim(0, 0.0046) +plt.grid() +plt.legend() +plt.xlabel('abs(Forecast Error)') +plt.title("Distribution of Absolute Error"); + + + +``` + diff --git a/report/Group-D-Report-Final.md b/report/Group-D-Report-Final.md new file mode 100644 index 000000000..1dc6a4028 --- /dev/null +++ b/report/Group-D-Report-Final.md @@ -0,0 +1,2784 @@ +--- +title: "Hybrid Model Approaches to Improve Short-Term Energy Demand Forecasts in New South Wales, Australia " +author: +- 'David Valido Ramos (z5516338), ' +- 'Katelyn Kemp (z5459347), ' +- 'Nick Mutton (z5549371), ' +- 'Senarath Seelanatha (z5595581), ' +- 'Shanjay Perinpanathan (z5339723), ' +- 'Waseem Alashqar (z5514810).' +date: "21/04/2025" +Abstract: "Electricity demand forecasting is a difficult problem every country faces. In this paper, we attempt to utilise the concept of hybrid models to improve the energy forecast of AEMO, the Australian body that manages power systems and markets, to predict energy demand in NSW. It was found that historical forecast errors and weather variables had some correlation with forecasting errors, therefore were included in the models. SARIMA, Random Forest, and XGBoost models were tested to determine the best fit for correcting AEMO forecasting bias and reducing overall energy demand forecast error. We argue the hybrid modification enables us to correctly factor relationships not supported by AEMO’s original model. All hybrid models tested provided some reduction in the overall forecast error and supported the hybrid model process." +output: + bookdown::pdf_document2: + template: template.tex + latex_engine: xelatex + md_extensions: +raw_attribute + keep_md: true + keep_tex: true + pandoc_args: + - --top-level-division="chapter" + - --bibliography="references.bib" + toc: true + toc_depth: 1 + number_sections: true + fig_caption: yes +Team: Group D +session: Hexamester 2, 2025 +coursecode: ZZSC9020 +bibliography: references.bib +csl: university-of-south-wales-harvard.csl +--- + + + +# Introduction {.label:s-intro} + +Energy forecasts play a crucial role in planning and maintaining the energy sector. Ensuring forecast accuracy helps to manage imbalances in energy production and consumption, reduce power system costs, and improve operational safety (Mystakidis et al., 2024). Energy demand management is also linked with self sufficiency and cost effectiveness that facilitate sustainable economic development (Suganthi and Samiel, 2012). Energy forecasting therefore has a broad impact on a wide variety of stakeholders including residential customers, power generators, retailers, traders, industrial and commercial customers, system operators, and financial investors (Ghalehkhondabi et al., 2016). + +\bigskip + +There are many risks in inaccurate energy forecasting. Over forecasting has cost and resource implications for providers, as well as environmental impacts. Under forecasting can cause outages, as well as having down the stream increased costs from inconsistent supply (Suganthi and Samuel, 2012). Shortages are also linked to political instability (Rakpho and Yamaka, 2021). + +\bigskip + +The Australian Energy Market Operator (AEMO) is responsible for managing Australia’s electricity and gas systems and markets to ensure Australians have access to reliable, affordable and secure energy. AEMO performs a wide range of functions, however one of their key roles is to balance electricity supply and demand through dispatching electricity generation based on forecasts updated every 5 minutes. It is therefore critical that their electricity demand forecasting is accurate to reduce the risks associated with over, or under supply of electricity to the market. While the AEMO short-term electricity forecast is generally quite accurate, it is valuable to understand where the forecast may be underperforming to consider how accuracy could be improved. + +\bigskip + +**The goal of this report is to identify variables that may contribute to errors in AEMO’s electricity demand forecasts, with the aim of using these insights to improve forecast accuracy.** + +\bigskip + +The report will consider a 12-hour interval with a step-ahead forecast. This is considered a ‘pre-dispatch’ interval based on AEMO’s definitions and is important for operational planning, therefore its accuracy is critical. + +\clearpage + +# Literature Review + +## Forecasting electricity demand background + +In today’s context of near-constant energy consumption, the task of energy forecasting has become increasingly complex. Given the absence of a universally applicable forecasting method, the selection of an appropriate technique is typically guided by the nature of the available data and the specific objectives of the forecasting exercise (Pinheiro, Madeira, & Francisco, 2023). Additionally, the forecast interval, which often reflects the purpose of the forecast, plays a large role in determining the suitability of different modeling approaches. + +\bigskip + +Forecasting models are typically categorised into short-, medium-, and long-term, and while there is not a unanimous definition of what constitutes these time periods, researchers generally agree that short-term is a few minutes up to a few days (Ahmad and Chen, 2018) or two weeks (Klyuev et al., 2022), medium-term as one month to one year, and long-term as one year to ten years (Ahmad and Chen, 2018). AEMO defines its short term forecast as up to 7 days ahead (AEMO, 2023). + +\bigskip + +Short-term intervals tend to require the greatest accuracy as they support a wide variety of operational planning, or network management activities including scheduling, planning of power generation, cost optimisation and guaranteeing continuous electricity supply (Sanhudo, Rodrigues and Filho, 2021). Short-term forecast methods can be broadly categorised into two categories – mathematical algorithms such as time-series analysis and logistic regression, and artificial intelligence (AI) algorithms such as machine learning, deep learning and ensemble learning models (Deng et al., 2022). For short-term forecasting, AI methods are becoming more popular as they can consider the non-linear nature of power demand. Short term forecasting is also generally more interested in the accuracy of the forecast rather than the interpretability of the results which makes these ‘black box’ approaches appropriate (Phyo and Byun, 2021). Other studies have found that machine learning models tend to outperform traditional models such as ARIMA in short-term forecasting (Divina et al., 2019). + +\bigskip + +Medium- and long-term forecasting supports the planning and maintenance of the electrical network such as smart grid eco-systems (Ahmad & Chen, 2018). Furthermore, long-term forecasting is more strategic and is necessary for the development of energy systems, planning capital construction at production or infrastructure facilities (Klyuev et al., 2022). These forecast intervals typically use econometric models, system dynamics, and grey prediction, with a focus on policy adjustments, economic indicators (such as GDP and CPI), and population trends (Koukaras et al., 2024). + +## Weather in forecasting electricity demand + +Temperature is a primary driver of electricity demand, shaping heating and cooling loads that dictate energy consumption. Research consistently identifies it as the dominant weather factor in electricity demand prediction, especially during peak periods. Liu et al. (2021) demonstrate that extreme temperatures lead to increased residential electricity consumption, finding that for each additional day in which the mean temperature exceeds 30 °C, there is an 16.8% increase in monthly residential electricity consumption. Similarly, for each additional day below -6 °C there is a 6% increase in monthly residential electricity consumption. This underscores temperature’s critical role in accurate demand forecasting, as it directly influences consumption patterns. + +\bigskip + +Extreme temperatures can lead to significant errors in electricity demand forecasts, often underestimating demand. During Winter Storm Uri in Texas in February 2021 (Añel, 2024), minimum extreme cold temperatures of –34 °C and high winds of 260 km/h impacted 170 million people. Due to this extreme weather event, electricity demand unexpectedly increased from 40 GW to over 70 GW, resulting in blackouts that affected more than 4 million people. The economic cost of the power outages and disruption has been estimated between 26.1 and 130 billion U.S. dollars. + +\bigskip + +Other weather variables, particularly humidity and "feels like" temperature, enhance forecasting accuracy. Maia-Silva et al. (2020) found that using humidity-related measures, such as dew point and heat index, improves prediction accuracy, especially in high-energy-consuming regions, with improvements up to 8-9%. This highlights the need to consider composite weather indices, as air temperature alone underestimates demand. + +## Historical Forecasting Error Incorporation + +Besides temperature and other weather components, historical measurements of energy demand or forecasted energy demand are highly reliable factors for predicting future energy demand (Singh and Yassine, 2018). Historical energy demand is important for capturing seasonal effects in different time horizons (day, week, month, season etc). However, historical energy forecasts (and by extension their differentials) are valuable because, in addition to seasonal effects, they capture bias and allow for corrections to the future forecast. Historical forecast factors are so influential that there is evidence that it can create reasonable forecasts without additional weather variables (Boroojeni et al., 2017). + +## Modelling electricity demand + +As previously stated, short-term energy models can be effectively categorised into two groups: classical statistical techniques, and machine learning or AI techniques. Traditional statistical and econometric models tend to be explainable and interpretable. While often less accurate, these models are widely used in energy demand forecasting and include methods such as regression (Papalexopoulos and Hesterberg, 1990) (Ertuğrul, Tekin and Tekin, 2020) and time-series such as ARIMA (Tarmanini et al., 2023) (Ediger and Akar, 2007). They also have natural extrapolations to medium-to-long term models, that are also econometric-based due to their relationship with longitudinal factors such as policy changes, modifications to the energy grid, or economic factors (such as GDP and population) (Ardakani and Ardehali, 2014). While a machine learning model, decision tree methods also provide interpretability in energy demand forecasting (Kopyt et al., 2024) (Wang et al., 2018). + +\bigskip + +Black box machine learning models provide a greater focus on model accuracy rather than interpretability. Some common models used in energy demand forecasting include Neural Networks (Manno, Martelli and Amaldi, 2022) (Kuo and Huang, 2018) (Pao, 2009), Support Vector Machines (Ahmad et al., 2014) (Ahmad et al., 2020), and ensemble methods, such as Random Forests (Divina et al., 2019) and XGBoost (Abbasi et al., 2019). + +\bigskip + +Divina et al. (2019) studied short-term energy consumption forecasting in smart buildings using several models such as linear regression, auto-regressive integrated moving average (ARIMA), artificial Neural Networks (ANNs) and ensemble methods such as random forests (RF) and extreme gradient boosting (XGBoost). They measured the performance of these models using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). They found that the best performing models were machine based approaches, and more so ensemble methods such as RF, GBM and XGBoost. On the other hand, ARIMA was the worst performing method that was tested. Further, the optimal historical window was found to be 10 days where accuracy improves up to this point, but does not improve much beyond this. Tarmanini et al. (2023) considered ARIMA and Artificial neural network (ANN) models to forecast daily electricity load in Ireland. The study found that both ARIMA and ANN produced more error in winter than in other seasons. Despite this, the ANN method performed better in terms of accuracy due to it better coping with non-linear data, but suggest a hybrid approach may provide more accurate results. + +\bigskip + +Other studies have also concluded that the best performing models tend to be hybrid models which use a combination of explainable and/or black-box methods, such as NN–ARIMA or CNN-LTSM due to their stability and potential to reduce overfitting (Deng et al. 2022). For example, Suganthi and Samuel (2012) compared various approaches to energy demand forecasting and found often hybrid approaches such as linking ARIMA models with neural networks often produce more accurate results. The superiority of hybrid models for energy forecasts are due to corrections of the original forecast output in the second modelling component (Savić, Selakov and Milošević, 2014). + +\bigskip + +One important method used in hybrid modelling is residual error forecasting. Andronikos, Tzelepi and Tefas (2023) proposed a residual error learning methodology for electricity demand forecasting which involved training a model on actual load values, then calculating the residual errors which would subsequently be used as targets to train a second model. The final prediction of forecast load would then be the sum of the first model’s prediction and the second model’s prediction. The authors found that if the errors have an underlying structure, the residual error forecasting method will improve forecasting accuracy. + +\bigskip + +A common method used in many hybrid studies which also aligns closely with modelling residuals is to decompose time series data into trend and residual components and model these components separately with appropriate methods. The forecasts from each component are then summed together for the final forecast. Amara et al. (2019) used decomposition to extract the temperature-related component that makes up electricity demand and then analysed and forecasted the residual component. The two forecasts were then summed to produce the final forecast. This allowed for understanding of periodicity in the residuals and to improve the overall forecast accuracy. Zhang et al (2022) considered the Australian electricity market in their study using a decomposition-hybrid approach. They first extracted a trend component from the original electricity load, then obtained the nonlinear component by subtracting the trend component from the original electricity load. The two components were forecast separately and then added together to make up the final forecast. Their proposed model improved the forecasting accuracy against all comparison models. Another approach to hybrid modelling considered by Pao (2009) was a two step approach where a linear model was built and the results of this inputted into a neural network model to capture both linear and non-linear relationships in the data. This showed to produce superior predictions to a linear model alone. + +\bigskip + +The approach proposed in this report is based on a hybrid approach where the AEMO forecast will act as the initial model and a new model will be built considering the errors from that model in an attempt to improve the overall forecast accuracy. + +## AEMO forecasting methodology + +The AEMO load Forecasting Methodology (AEMO, 2023) details the organisation’s approach to forecasting electricity demand. With particular relevance to this report, AEMO pre-dispatch forecasts are short-term electricity demand forecasts that include intervals up to 40 hours. One of the important uses of pre-dispatch forecasting is to support operational planning that ensures electricity reliability and security of the network. The key inputs into the forecast include: + +- Historical demand (such as recent load patterns) + +- Weather forecast variables (particularly those that describe the temperature profile) + +- Calendar variables (e.g. weekday or weekend, public or school holiday, daylight savings) + +- Solar and wind generation forecasts. + +\bigskip + +There is very little manual intervention for these forecasts, with AEMO’s Demand Forecasting System (DFS) generating forecasts automatically through a combination of statistical and machine learning models, every half hour. + +\bigskip + +The pre-dispatch load forecasting error threshold for NSW is 150 MW based on historical peak demand for NSW and previous forecasting performance. The load forecast is reviewed whenever the forecast error is greater than the threshold for two consecutive 30-minute periods, therefore at an overall level the forecast is already quite accurate. + +# Material and Methods + +Figure \@ref(fig:modelDiagram) shows the overall structure of this project designed to address the research question. The process began with data collection and pre-processing, including calculating the forecast error. Exploratory data analysis was conducted to understand relationships between different variables and the forecast error. The data were then split into training and testing samples for models to be built, fine-tuned and compared. The remaining sections of this report detail the steps undertaken in the modelling as well as analysis and presentation of the results. + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MaterialMethods} \caption{Project structure followed to address the research question}(\#fig:modelDiagram) +\end{figure} + + +## Software + +Python was the primary software used for data analysis and modelling based on its flexibility in data visualisations and ability to execute machine learning models. + +\bigskip + +To ensure reproducibility, RMarkdown was used to prepare the final report. Power BI was also used in initial data exploration to understand high-level trends in the data. A Github repository was used to store data, code and working documents. The repository can be found here: https://github.com/unswnick/project. All relevant code for this project can be found in the Appendices. Appendix A contains data processing code, Appendix B contains Modelling code, and Appendix C contains code for plots. + +\bigskip + +A summary of software used as part of the project is summarised in Table \@ref(tab:tab1). +\bigskip + +\begin{table}[H] +\caption{Summary of software used} +\begin{center} +\begin{tabular}{|l|l|p{19em}|} +\hline +\textbf{Software} & \textbf{Library(s)} & \textbf{Purpose} \\ +\hline +\multirow{4}{4em}{Python} & Pandas & Reading, manipulating, cleaning and analysing datasets. \\ +\cline{2-3} +& Numpy & Manipulating data and mathematical \newline calculations. \\ +\cline{2-3} +& Matplotlib, Seaborn & Visualising data to understand trends and \newline patterns. \\ +\cline{2-3} +& Scikit-learn & Implementing and evaluating machine learning algorithms. \\ +\hline +PowerBI & - & Summarising and visualising data. \\ +\hline +RMarkdown & - & Writing final report. \\ +\hline +Github & - & Repository for project documents \\ +\hline +\end{tabular} +\end{center} +(\#tab:tab1) +\end{table} + +## Description of the Data + +Table \@ref(tab:tab2) describes the data that was used in analysis. In addition to the data files provided by the client, historical weather forecasts including temperature, humidity and wind speed were sourced from OpenWeather (OpenWeather, 2025), a global company specialising in environmental data products. Forecast weather data rather than actual weather data was used as an input to ensure the forecast models were realistic. + +\begin{table}[H] +\caption{Datasets used in this project and their properties} +\centering +\begin{tabular}{|p{13em}|p{20em}|} +\hline +\textbf{Data} & \textbf{Description} \\ +\hline +\textbf{Electricity demand}\newline Use for both training and testing models. & Electricity demand from 2010 to 2021. Well-structured and low complexity with no duplicates and no null values.\newline Variables: Date-time, totalDemand, regionID\newline +Format: CSV, Storage: Github, Size: 6 Mb, Rows: 196,513 \\ +\hline +\textbf{Forecast demand} \newline Used as a baseline forecast model and improve on. & Provides forecasted demand data from 2010 to 2021. Well-structured with no null values. It is high complexity due to uneven time increments and duplicate rows. \newline Variables: Date-time, forecastDemand, totalDemand, regionID, preDispatchSeqNo, periodID, lastChange\newline Format: CSV, Storage: Github, Size: 722 Mb, Rows: 10,906,019 \\ +\hline +\textbf{Forecast weather indicators}\newline Exogenous variables included in modelling. & Provides previous forecast weather data for Bankstown from October 7 2017. Well-structured with no null values. \newline Variables: Date-time, temperature, humidity, wind speed, rain\newline Format: CSV, Storage: GitHub, Sharepoint/Teams, Size: 1327.1 MB, Rows: 10854100 \\ +\hline +\end{tabular} +(\#tab:tab2) +\end{table} + +## Data Cleaning + +Data was found to be complete for Electricity Demand data Some forecast data were missing for forecast intervals >12 hours. To ensure complete data was used, and to reduce computational complexity, the forecast models were trained and tested on 12 hour forecast intervals only. There were no missing values in the Forecast Weather Indicators data, however the available data begins on October 7 2017. Consequently, the relevant data used to train and test the forecast model was between October 7 2017 and 17 March 2021 with a 12 hour forecast interval. No further missing values were present in the data. + +\bigskip + +Outliers were not removed from the data to ensure data completeness and to avoid introducing bias through exclusions. Further advice from industry experts would be required to determine which outliers, if any, should be removed based on appropriate criteria. + +\bigskip + +Additional data cleaning steps performed on all datasets are detailed below: + +1. Date/time variables were formatted consistently (i.e. d/m/y H:M) + +2. Date/time variables were rounded to the nearest 30 minute increment to provide consistent 30-minute intervals + +3. Duplicate date/time rows were removed to ensure each date/time row was unique + +\bigskip + +After each dataset was cleaned and checked, they were merged into one clean dataset, joined on the unique date/time variable. + +## Data pre-processing + +Outlined below are the steps undertaken to pre-process the data: + +1. **Feature extraction** – The following features were extracted from date/time variables: + + Hour_of_day + + Month_of_year + + Day_of_week + +2. **Label enconding** – Hour_of_day, Day_of_week and Month_of_year variables were one-hot-encoded into binary variables + +3. **Feature engineering** – The following new features were created: + + + Forecast interval (date/time future – date/time current) + + Forecast error (total demand – forecast demand) + + 24-hour Forecast Error (Forecast error from 24 hours ago) + + 48-hour Forecast Error (Forecast error from 48 hours ago) + + 72-hour Forecast Error (Forecast error from 72 hours ago) + + 7-day Forecast Error (Forecast error from 7 days ago) + + 14-day Forecast Error (Forecast error from 14 days ago) + + Relative error (Forecast error / total demand) + + Hour × Temperature (Hour * Temperature) + + Hour (Sine) (hour_sin) — sin(2π × Hour / 24) + + Hour (Cosine) (hour_cos) — cos(2π × Hour / 24) + + Month × Temperature (MonthNumb * Temperature) + + Hour × Forecast Demand (Hour * forecast_demand) + + Temperature × Forecast Demand (Temperature * forecast_demand) + + Temperature × Hour (Sine) (Temperature * hour_sin) + + Temperature × Hour (Cosine) (Temperature * hour_cos) + + Forecast Demand × Hour (Sine) (forecast_demand * hour_sin) + + Forecast Demand × Hour (Cosine) (forecast_demand * hour_cos) + + 24-hour Forecast Error × Hour (Cosine) (24hrpreverrors * hour_cos) + + 24-hour Forecast Error × Hour (Sine) (24hrpreverrors * hour_sin) + +4. **Splitting the data** - As a final step in pre-processing, the data were split into 70% training 7 October 2017 – 5 March 2020) and 30% testing (6 March 2020 – 17 March 2021). This split allowed for a large number of data to be trained on, and a full year to test which captured all seasonal effects. The same split was used across the models. + +Note that modelling methods chosen did not require normalisation of the data. + +## Assumptions + +- AEMO’s forecast data is released every 5 minutes, therefore forecast data for the 12 hour interval is available to use in the model + +- Temperature/weather forecasts are available for 12 hours into the future. + +- Bankstown weather variables are reasonable representations of weather conditions across New South Wales. + +## Modelling Methods + +The following methods were in this study: + +* Linear Regression: Baseline model for improving forecasts due to its simple implementation and interpretability. + +* SARIMA: EDA identified autocorrelation between forecast errors. Due to the seasonal nature of electricity demand, SARIMA modelling was conducted. + +* Decision Trees: EDA identified non-linearity between electricity demand and its explanatory variables. As such, decisions trees were implemented to explore simpler non-linear behaviors. + +* XGBoost: Implemented to explore non-linear behaviors using advanced techniques. + +These modelling methodologies are described below. + +\bigskip + +\noindent \textbf{ARIMA} + +\bigskip + +\noindent Auto Regressive Integrated Moving Average (ARIMA) is a time series forecasting model. Besides being well-researched and more readily explainable compared to machine learning models, its algorithm specifications make it suitable for energy demand forecasting. The model consists of three main components: + +\bigskip + +Auto Regression: The model utilises lagged observations or previous time points. Due to the weather conditions of previous days having a direct influence on future weather, previous time points are relevant for forecasting. In addition, energy demand also exhibits seasonality that can be captured by previous inputs. + +\bigskip + +Differencing (Integration): Energy and weather demands over different time horizons exhibit slight trend. Raw observations are differenced to make statistical properties (such as mean or variance) stabilised over time. + +\bigskip + +Moving average: Smooths variance by modelling a moving average of lagged variables against point residuals. This reduces noise in highly variable factors susceptible to measurement error like weather. + +\bigskip + +Seasonal Auto Regressive Intergrated Moving Average (SARIMA) is an extension of the ARIMA model. SARIMA is designed to support seasonality in time series data. It can be modified to incorporate seasonality in different time horizons such as weekly, monthly, or quarterly time frames. The model parameters are the same as ARIMA with the inclusion of seasonal variants to control for seasonal effects: seasonal autoregressive order, seasonal differencing order, and seasonal moving average order. + +\bigskip + +\noindent \textbf{Decision Trees} + +\bigskip + +\noindent Decision Trees are a type of explainable machine learning model. They are trained by recursively dividing the dataset into subsets using entropy (a measure of impurity or randomness in the dataset) and optimise for information gain. The impurity is in context to the target variable. When a subset of data is comprised of an entire class, it is considered pure. It is interpretable because the model construction can be read as a series of conditional IF statements to achieve certain outputs. + +\bigskip + +A Random Forest is a collection of generated Decision Trees. The generation formula is consistent across each decision tree, the difference being each tree is generated from a different bootstrap sample. The prediction outputs for regression tasks, such as energy demand forecasting, is an average of all decision tree outputs. Random Forests lose the ability of decision trees to be interpretable, the benefit however, is improved accuracy and robustness. + +\bigskip + +\noindent \textbf{XGBoost} + +\bigskip + +\noindent XGBoost, short for extreme gradient boosting, is a gradient descent machine learning method. Its formulation is by use of a loss function to measure the difference between predicted and actual values and a regularization term to penalize complex models. + +\bigskip + +It functions by building decision trees sequentially. Each tree is trained to predict the residuals from previous trees. Each tree split mechanism follows the process of regular decision tree training. Each tree's contribution to the final prediction is weighted by a learning rate. It generally outperforms regular decision tree models due to its internal corrections of error and feature selection. Its construction makes it suitable for regression tasks such as energy demand forecasting. + +# Exploratory Data Analysis + +This section presents an exploratory analysis of the temperature, forecasted demand, and actual electricity demand data. Exploratory data analysis (EDA) explored how demand responds to temperature variations and where forecast discrepancies are most pronounced. The data is manipulated and visualised with Python. + +\bigskip + +We begin the analysis by focusing on the individual distributions and characteristics of each dataset. This stage provides context on the seasonal variability of the data. + + + +## Electricity Demand + + +Electricity demand shows a cyclical pattern with a downward trend when observing it throughout the years (Figure \@ref(fig:yeardemand)). This may be due to more households investing in embedded generation to supplement their electricity supply. + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/demandvtime} \caption{Electricity Demand vs Time}(\#fig:yeardemand) +\end{figure} + + +Electricity demand is higher during winter and summer months (Figure \@ref(fig:monthdemand)). This is likely due to higher consumption of electricity to power heating and cooling appliances. + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/demandMonth} \caption{Total Demand by Month}(\#fig:monthdemand) +\end{figure} + + +Demand was observed to be greater in weekdays than weekends (Figure \@ref(fig:weekdemand)). This may be due to many businesses closing during weekends. + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/WeekDemand} \caption{Total Demand by Day of the Week}(\#fig:weekdemand) +\end{figure} + + +Electricity demand is relatively high between 8am and 11pm (Figure \@ref(fig:hourdemand)), likely due to the human sleeping cycle. + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/demandHour} \caption{Total Demand by Hour of Day}(\#fig:hourdemand) +\end{figure} + + +## Forecast Electricity Demand + +This dataset contains electricity demand forecasts made every 30 minutes. Each time a forecast is made, it includes 48 predictions—one for each half-hour period from 30 minutes ahead up to 24 hours ahead. + +\bigskip + +The scatter plot of electricity demand forecasts vs actual electricity demand across different prediction time periods (Figure \@ref(fig:forecastdem)), reveals lower correlation as both the prediction time period and actual electricity demand increase. + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/forecastdemscatter} \caption{Scatter plots of Forecast Demand vs Total Demand by Lag interval}(\#fig:forecastdem) +\end{figure} + + +## Weather Variables vs Electricity Demand + +In this next section, we will examine if and how weather affects both the electricity demand and its forecast. + +\bigskip + +The correlation of relevant weather variables with electricity demand shows weak correlation across all variables, with humidity having the highest correlation and rain having the lowest (Figure \@ref(fig:weatherdemand)). + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/WeatherVDemand} \caption{Correlation of Weather variables with Electricity Demand}(\#fig:weatherdemand) +\end{figure} + + + + + + +The plot of temperature against electricity demand reveals a distinct U-shaped correlation. This pattern reflects energy usage behaviour in response to extreme temperatures (Figure \@ref(fig:tempcorr)). The lowest demand levels generally occur in temperate conditions where neither heating nor cooling is heavily used. + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.35\textheight]{images/tempvsdemand} \caption{Scatter plot of Electricity demand vs Temperature}(\#fig:tempcorr) +\end{figure} + + +## Forecast Error of Electricity Demand + + +In this section, we will explore whether forecast inaccuracies are correlated with known variables which contribute to electricity demand. Forecast error was defined as actual demand less forecast demand. + +\bigskip + + + + +Figure \@ref(fig:errorvtemp) shows that forecast error increases with temperature for temperatures greater than ~29°C. This suggests the current forecasting model may lack information regarding forecasted temperatures. The trend also occurs for normalised demand (Figure \@ref(fig:relerrorvtemp)). + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/forecastErrorTemp} \caption{Forecast Error vs Temperature Forecast}(\#fig:errorvtemp) +\end{figure} + + + + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/PortionErrorTemp} \caption{Normalised forecast error vs temperature forecast}(\#fig:relerrorvtemp) +\end{figure} + + +### Time Series Analysis + +Time series analysis of the forecast error was conducted to understand whether errors persisted with time. It was conducted for 6, 12, 18 and 24-hour forecasts. + +\bigskip + +Augmented Dickey-Fuller test (ADF Test) was conducted. It showed significant evidence for stationary forecast errors (Table \@ref(tab:tab3)). + +\begin{table}[H] +\caption{ADF tests conducted for 6, 12, 18 and 24-hour forecasts} +\centering +\begin{tabular}{||c||c||} +\hline +Hour of day = 6 & Hour of Day = 18 \\ +\hline +ADF Statistic: -33.863838 & ADF Statistic: -31.926828 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.430 & 1\%: -3.430 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\hline +Hour of Day = 12 & Hour of Day = 24 \\ +\hline +ADF Statistic: -33.407029 & ADF Statistic: -29.030458 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.430 & 1\%: -3.430 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\end{tabular} +(\#tab:tab3) +\end{table} + +Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots were generated to understand the relationship between forecast errors and lagged versions of itself over successive time lags (Figure \@ref(fig:acfErrors), Figure \@ref(fig:pacfErrors)). PACF plots showed that forecast errors were significantly partially correlated with the most recent forecasts and ones made 24 and 48 hours prior. The partial correlation of the 24-hour lagged forecast error was of note due its greater significance than the 48-hour lag and its availability when forecasting. + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.65\textheight]{images/ACFForecastErrors} \caption{ACF of forecast errors}(\#fig:acfErrors) +\end{figure} + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.6\textheight]{images/PACFForecastErrors} \caption{PACF of forecast errors}(\#fig:pacfErrors) +\end{figure} + + +Scatterplots and correlations for forecast errors and its 24-hour lagged error can be seen in Figure \@ref(fig:laggedError). + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.6\textheight]{images/ForecastErrorCorrelations} \caption{Scatterplots and correlations for forecast errors and its 24-hour lagged errors}(\#fig:laggedError) +\end{figure} + +## Summary of Key Findings + +- **U-shaped relationship between temperature and electricity demand:** Electricity demand increases during both extreme cold and extreme heat conditions, with the lowest demand observed during temperate conditions. This pattern is consistent with expected heating and cooling behavior and is evident in both actual and forecasted demand data. + +- **Forecasting models capture seasonal trends:** Forecasted electricity demand shows a similar U-shaped relationship with temperature, indicating that the models are aligned with seasonal usage patterns. + +- **Forecast error increases non-linearly with temperature, especially during extreme heat:** Forecast accuracy deteriorates significantly at higher temperatures, suggesting that current models underperform during periods of extreme heat. In comparison, performance during extreme cold is better, though still less accurate than under mild conditions. + +- **Forecast errors are autocorrelated with past errors:** Forecast errors may be modelled by understanding historical forecast errors. Of note, the 24-hour lagged forecast error may be used for improving forecasts. + +- **The forecast has the potential to be improved:** These findings highlight some correlations between forecast errors, temperature and time variables such as season which may indicate the model could be improved by modelling forecast errors. + +# Analysis and Results + +## Performance measures + +Two common performance measures were chosen to calculate prediction accuracy and compare models. Mean square error (MSE) and mean absolute percentage error (MAPE) were chosen to be the most appropriate measures. MSE penalises large errors which is useful to assess when the aim is to reduce large errors. Furthermore, MAPE provides an easily interpretable and comparable result. The measures are described below: + +\bigskip + +MSE is the average of the squared difference between actual demand and forecasted demand. + +\begin{equation*} +\text{MSE} = \frac{1}{n}\sum^{n}_{i=1}{(Y_i-{\hat{Y}_{i}})^2} +\end{equation*} + +MAPE takes the absolute value of the difference between the actual demand and forecast demand expresses it as a percentage of actual demand, and takes the average of this. + +\begin{equation*} +\text{MAPE} = \frac{1}{n}\sum^{n}_{i=1}{\frac{\lvert A_{i}-F_{i}\rvert}{A_{i}} * 100} +\end{equation*} + +For both measures, a smaller value represents higher accuracy, and a better performing model. + +\bigskip + +MSE and MAPE values for the original forecast can be seen below. + + + +\begin{align*} +\text{Existing model MSE} &= 55159 \\ +\text{Existing model MAPE} &= 2.19\% \\ +\end{align*} + + +## Linear Regression + +Forecast error was defined as actual demand less forecast demand (Equation \@ref(eq:oneA)). + + +\begin{equation} +\varepsilon_t = y_t - \hat{y}_{t} (\#eq:oneA) +\end{equation} + + +Two linear regression models were trained for predicting the forecast error (Equation \@ref(eq:oneB)). The predicted forecast error was then used to update the forecast (Equation \@ref(eq:oneC)). The aim of the models was to reduce the updated forecast error (i.e. $\varepsilon^{*}_t < \varepsilon$). + + +\begin{equation} +\varepsilon_t = \hat{\varepsilon}_t(...) + \varepsilon^{*}_t (\#eq:oneB) +\end{equation} + + +\begin{equation} +\hat{y}^{*}_{t} = \hat{y}_t + \hat{\varepsilon}_{t}(...) + \varepsilon^{*}_t (\#eq:oneC) +\end{equation} + + +### Model Construction +\bigskip +**Linear Regression - Model 1** +\bigskip + +\noindent The first model used only the 24-hour lag forecast error for predicting the forecast error (Equation \@ref(eq:oneD)). Hence, it was a simple autoregressive model. + + +\begin{equation} +\varepsilon^{\text{(LR1)}}_t = \theta_{0} + \theta_{1}\varepsilon_{t-24h} (\#eq:oneD) +\end{equation} + + +The model summary can be seen in Table \@ref(tab:tab4). It showed that all variables are significant at the 0.05 significance level. +\bigskip + + + + + +\begin{table}[H] +\caption{Linear Regression Model 1, OLS Regression Results} +\centering +\begin{tabular}{lr|lr} +\hline +\hline +\multicolumn{4}{c}{OLS Regression Results} \\ +\hline +\hline +Dep. Variable: & y & R-squared: & 0.113 \\ +Model: & OLS & Adj. R-squared: & 0.113 \\ +Method: & Least Squares & F-statistic: & 2696. \\ +Date: & Sun, 20 Apr 2025 & Prob (F-statistic): & 0.00 \\ +Time: & 11:19:06 & Log-Likelihood: & -1.4233e+05 \\ +No. Observations: & 21064 & AIC: & 2.847e+05 \\ +Df Residuals: & 21062 & BIC: & 2.847e+05 \\ +Df Model: & 1 & & \\ +Covariance Type: & nonrobust & & \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrrrrrr} + & coef & std err & t & P>|t| & [0.025 & 0.975] \\ +\hline +const & 10.5613 & 1.438 & 7.346 & 0.000 & 7.743 & 13.379 \\ +forecast\_error & 0.3369 & 0.006 & 51.927 & 0.000 & 0.324 & 0.350 \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrlr} +Omnibus: & 2737.590 & Durbin-Watson: & 0.201 \\ +Prob(Omnibus): & 0.000 & Jarque-Bera (JB): & 23155.543 \\ +Skew: & -0.344 & Prob(JB): & 0.00 \\ +Kurtosis: & 8.090 & Cond. No. & 222.\\ +\hline +\hline +\end{tabular} +(\#tab:tab4) +\end{table} + +\noindent \textbf{Linear Regression - Model 2} + +\bigskip +\noindent The second model used the 24-hour lag forecast error and all possible explanatory variables for the forecast error identified in EDA (Equation \@ref(eq:oneE)). + + + +\begin{equation} +\begin{split} +\varepsilon^{(LR2)}_{t} = &\theta_0 + \theta_1\varepsilon_{t-24h} +\theta_{2}forecastTemperature_t + \\ +& \theta_{3}forecastHumidity_t + \theta_{4}forecastWind_t + \theta_{5}forecastRain_t + \\ +& \theta_{6}isSaturday_t + \theta_{7}isSunday_t + \theta_{8}isJanuary_t + \\ +& \theta_{9}isNovember_t + \theta_{10}isDecember_t +\end{split} +(\#eq:oneE) +\end{equation} + + +The model summary can be seen in Table \@ref(tab:tab5). It showed that all variables, except \textit{forecastRain}, are significant at the 0.05 significance level. + +\begin{table}[H] +\centering +\caption{Linear Regression Model 2, OLS Regression Results} +\begin{tabular}{lr|lr} +\hline +\hline +\multicolumn{4}{c}{OLS Regression Results} \\ +\hline +\hline +Dep. Variable: & y & R-squared: & 0.124 \\ +Model: & OLS & Adj. R-squared: & 0.124 \\ +Method: & Least Squares & F-statistic: & 298.6 \\ +Date: & Sun, 20 Apr 2025 & Prob (F-statistic): & 0.00 \\ +Time: & 11:19:08 & Log-Likelihood: & -1.4220e+05 \\ +No. Observations: & 21064 & AIC: & 2.844e+05 \\ +Df Residuals: & 21053 & BIC: & 2.845e+05 \\ +Df Model: & 10 & & \\ +Covariance Type: & nonrobust & & \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrrrrrr} + & coef & std err & t & P>|t| & [0.025 & 0.975] \\ +\hline +const & 10.5613 & 1.438 & 7.346 & 0.000 & 7.743 & 13.379 \\ +forecast\_error & 0.3369 & 0.006 & 51.927 & 0.000 & 0.324 & 0.350 \\ +Temperature & -1.1963 & 0.296 & -4.040 & 0.000 & -1.777 & -0.616 \\ +Humidity & 0.9220 & 0.094 & 9.811 & 0.000 & 0.738 & 1.106 \\ +Wind\_speed & 6.7613 & 0.927 & 7.296 & 0.000 & 4.945 & 8.578 \\ +Rain & -3.8983 & 2.807 & -1.389 & 0.165 & -9.399 & 1.603 \\ +isSaturday & 29.1366 & 4.143 & 7.033 & 0.000 & 21.016 & 37.257 \\ +isSunday & 8.2777 & 4.149 & 1.995 & 0.046 & 0.145 & 16.410 \\ +isDecember & 20.6354 & 4.986 & 4.139 & 0.000 & 10.863 & 30.408 \\ +isJanuary & 13.2394 & 5.233 & 2.530 & 0.011 & 2.982 & 23.497 \\ +isNovember & 10.3352 & 4.832 & 2.139 & 0.032 & 0.865 & 19.805 \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrlr} +Omnibus: & 2701.773 & Durbin-Watson: & 0.203 \\ +Prob(Omnibus): & 0.000 & Jarque-Bera (JB): & 23691.264 \\ +Skew: & -0.315 & Prob(JB): & 0.00 \\ +Kurtosis: & 8.157 & Cond. No. & 1.67e+03\\ +\hline +\hline +\end{tabular} +(\#tab:tab5) +\end{table} + + + +### Model Performance + +The predicted forecast error was then used to update the forecast error (Equation \@ref(eq:oneC). Model evaluation (MSE and MAPE) can be seen below. + +\begin{multicols}{2} +\noindent\textbf{LRegression Model 1 Performance} \\ +- MSE: 49348 \\ +- MAPE: 2.06\% \\ + + +\columnbreak + + +\noindent\textbf{LRegression Model 2 Performance} \\ +- MSE: 49718\\ +- MAPE: 2.078\%\\ +\end{multicols} + +Model 1 performed better as it minimised both MAPE and MSE values. + +## S-ARIMA + +Two SARIMA model collections were trained for predicting the forecast error (Equation \@ref(eq:oneB). The predicted forecast error was then used to update the forecast (Equation \@ref(eq:oneC)). The aim of the models was to reduce the updated forecast error (i.e. $\varepsilon^{*}_{t} < \varepsilon_t$). + +\bigskip + +A model collection contained a SARIMA model for each hour of the day. This reduced overall computation time, while allowing hour of day to be an explanatory variable (note, training the model on all data was not feasible due to limited computing power). The large data size should allow for data segmentation to have minimal impact on model training. + +\bigskip + +EDA, conducted earlier, showed that forecast errors are partially correlated with lagged values of itself in 24-hour intervals. As such, SARIMA modelling only considered lags of 24-hours. + +### Parameter Selection + +ADF tests conducted showed significant evidence for stationary forecast errors, after segmentation by hour of day (Table \@ref(tab:tab6)). As such no differencing (d, D) was considered for SARIMA modelling. + + + + +\begin{table}[H] +\caption{ADF tests for 4, 10, 16, 22-hour forecasts} +\centering +\begin{tabular}{||c||c||} +\hline +Hour of day = 4 & Hour of Day = 10 \\ +\hline +ADF Statistic: -5.875132 & ADF Statistic: -7.680207 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.432 & 1\%: -3.432 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\hline +Hour of Day = 16 & Hour of Day = 22 \\ +\hline +ADF Statistic: -9.601104 & ADF Statistic: -7.968233 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.432 & 1\%: -3.432 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\end{tabular} +(\#tab:tab6) +\end{table} + + +\bigskip + +ACF and PACF plots were generated to assist in SARIMA parameters selection (Figure \@ref(fig:ACF), Figure \@ref(fig:PACF)). The PACF plot showed that, generally, forecast errors are partially correlated with the first lagged term, followed by the next six lagged term, then 1-week and 2-week lags. As such, auto-regressed parameters (p) considered were 1, 2, 6 and 7, and the auto-regressed seasonal parameters (P) considered were 1 and 2. The seasonality parameter (s) was set at 7 for a weekly seasonality. + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.35\textheight]{images/Stationarity1} \caption{ACF of forecast errors with 24-hour lags}(\#fig:ACF) +\end{figure} + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.35\textheight]{images/Stationarity2} \caption{PACF of forecast errors with 24-hour lags}(\#fig:PACF) +\end{figure} + + + +\bigskip + +Moving average parameters considered (q, Q) were 0, 1 and 2. A summary of model parameters can be seen in Table \@ref(tab:tab7). + +\bigskip + +\begin{table}[H] +\centering +\caption{SARIMA Model parameters} +\begin{tabular}{|c|ccc|cccc|} +\hline +\multirow{2}{*}{\textbf{ID}} & \multicolumn{3}{c|}{\textbf{ARIMA Order}} & \multicolumn{4}{c|}{\textbf{Seasonal Order}} \\ \cline{2-8} + & \multicolumn{1}{c|}{\textbf{p}} & \multicolumn{1}{c|}{\textbf{d}} & \textbf{q} & \multicolumn{1}{c|}{\textbf{p}} & \multicolumn{1}{c|}{\textbf{D}} & \multicolumn{1}{c|}{\textbf{Q}} & \textbf{s} \\ \hline +0 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 0 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +1 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +2 & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +3 & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{0} & 7 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +4 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 0 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +5 & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{0} & 2 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +6 & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{0} & 2 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{2} & 7 \\ \hline +7 & \multicolumn{1}{c|}{8} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +8 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +\end{tabular} +(\#tab:tab7) +\end{table} + +\bigskip + +MSE and MAPE values were generated for each model in Table \@ref(tab:tab7) (Figure \@ref(fig:MSEsarima), Figure \@ref(fig:MAPEsarima)). Models 5, 6 and 7 equally improved MSE and minimised MAPE values. Model 5 was selected as it was the least complex of the three. + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MSEarima} \caption{MSE improvement for each SARIMA model}(\#fig:MSEsarima) +\end{figure} + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MAPEtuning} \caption{MAPE for each SARIMA model}(\#fig:MAPEsarima) +\end{figure} + + +### Model Construction + +**SARIMA - Model 1** + +\bigskip +The first model used only lagged versions of the forecast error for predicting the forecast error (Equation \@ref(eq:sarima1)). + +\begin{equation} +\varepsilon^{\text{(SARIMA1)}}_{t} = \text{SARIMA}(6,0,2)(1,0,1,7) +(\#eq:sarima1) +\end{equation} +\ +\noindent \textbf{SARIMA - Model 2} + +\bigskip + +The second model used lagged versions of the forecast error and exogenous data (forecast temperature, humidity, wind and rainfall) for predicting the forecast error (Equation \@ref(eq:sarima2)). + +\begin{equation} +\begin{split} +\varepsilon^{\text(SARIMA2)}_t =& \text(SARIMA)(6,0,2)(1,0,1,7) + \\ +& \theta_{1}forecastTemperature_t + \\ +& \theta_{2}forecastHumidity_t + \theta_{3}forecastWind_t + \\ +& \theta_{4}forecastRain_t +\end{split} +(\#eq:sarima2) +\end{equation} + +### Model Performance + +The predicted forecast error was then used to update the forecast error (Equation \@ref(eq:oneC)). Model evaluation (MSE and MAPE) can be seen below. + + + +\begin{multicols}{3} +\noindent\textbf{Old Model}\\ +\textbf{Performance}\\ +- MSE: 55078.33198 \\ +- MAPE: 2.178\% \\ + + +\columnbreak + + +\noindent\textbf{SARIMA Model} \\ +\textbf{(no Exog) Performance} \\ +- MSE: 49519.31974\\ +- MAPE: 2.049\%\\ + +\columnbreak + + +\noindent\textbf{SARIMA Model (with} \\ +\textbf{Exog) Performance} \\ +- MSE: 49165.32932 \\ +- MAPE: 2.082\% \\ +\end{multicols} + +Model 1 performed better as it minimised MAPE, which was given greater importance. + +## Random Forest +Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the average prediction of the individual trees. + +The Random Forest model discussed aims to reduce the demand forecast error by predicting demand directly rather than predicting the error and then updating the original forecast. + +### Model Construction + +**RFMF1 - Model 1** +The first model used the lag of the forecast error. The accuracy of predictions improved slightly in this model. +\bigskip + +\noindent\textbf{RFMF1 - Model 2} +The second model used the lag of the forecast error and included weather variables (forecast temperature, wind speed, humidity and rain). The model predictions improved in this model (Figure \@ref(fig:ForestModel2)). + + +### Parameter Selection (Fine Tuning) + +Fine tuning was done by utilising Grid Search on the Random Forest Model. + +\bigskip + +By trialing many parameters combinations, the following combination was found to be the best performing. + + +\begin{align*} +n\_estimators&=200\\ +max\_depth&=10\\ +min\_samples\_split&=5\\ +min\_samples\_leaf&=2\\ +\end{align*} + + +\noindent Where $n\_estimator$ is the number of trees, $max\_depth$ is the maximum depth of each individual tree, $min\_samples\_split$ is the minimum number of samples required to split an internal node and, $min\_samples\_leaf$ is the minimum number of samples required to be at a leaf node. + +### Model Performance + +Setting up models with the values above had the following results (Figure \@ref(fig:ForestModel1), Figure \@ref(fig:ForestModel2)): + + +\begin{multicols}{2} +\noindent\textbf{RFMF1 Performance} \\ +- MSE: 51971.086 \\ +- MAPE: 2.111% \\ + + +\columnbreak + + +\noindent\textbf{RFMF2 Performance} \\ +- MSE: 51395.334\\ +- MAPE: 2.095%\\ +\end{multicols} + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/ForestModel1} \caption{Line Plot of RFMF1's Performance vs Original Forecast Model Performance}(\#fig:ForestModel1) +\end{figure} + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/ForestModel2} \caption{Line Plot of RFMF2's Performance vs Original Forecast Model Performance}(\#fig:ForestModel2) +\end{figure} + +## XGBoost + +Extreme Gradient Boosting (XGBoost) is a machine learning algorithm that utilises gradient boosting decision trees that generates fast and effective models used for forecasting, classification and regression problems. + +\bigskip + +As discussed above, forecasting has been seen to improve when incorporating the new weather forecast values combined with previous errors. The overall aim being to reduce the demand forecast error. + +\bigskip + +The XGBoost model discussed aims to reduce the demand forecast error by predicting demand directly rather than predicting the error and then updating the original forecast. + +### Model Construction + +The base model involved using forecasted temperature, humidity, wind speed and rain, combining that with the hour, month and the day of week. Taking the model to the next level involved including the previous forecasted demand and the previous forecast error from 24 hours, 48 hours, 7 days and 14 days ago. + +\bigskip + +Assessing where a base model performs worse based on hour of day yields the following. + +\bigskip + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MAPEXGBoost} \caption{MAPE by Hour of the Day}(\#fig:MAPEXGBoost) +\end{figure} + +\bigskip + +Taking this error as a wave format, the model improved when variables were combined with a sin or cos wave. Specifically, combining hour with sin/cos wave and then multiplying by forecasted temperature improved the model. + +### Parameter Selection (Fine Tuning) + +Fine tuning is especially important for XGBoost and a grid search was utilised to find the highest performing combination from a wide distribution of parameters. The following was found to be the most effective combination of parameters. + +\begin{center} +\begin{align*} +learning\_rate &= 0.1,\\ +n\_estimators &= 150,\\ +max\_depth &= 3,\\ +subsample &= 0.8 +\end{align*} +\end{center} + +### Model Performance + +Utilising the above parameters gave accuracy scores of + +\begin{align*} +\text{MSE} &= 46526.86 \\ +\text{MAPE} &= 2.042\% +\end{align*} + +### Combining variables + +Introducing new variables as functions of other variables boosted the performance of XGBoost. Whilst in theory introducing variables such as Temperature * Humidity could improve the model, introducing them created unnecessary complexity that reduced the accuracy of the model. It could also been seen that XGBoost took these into account inside the algorithm. + + +## Model Comparison + +Comparison of all models tested against the baseline AEMO model (Table \@ref(tab:tab8)). The XG Boost model produced the highest level of accuracy of the models considered. This is further evident when observing prediction error distribution (Figure \@ref(fig:Cpmparison)). + + +\begin{table}[H] +\caption{Models compared by MSE and MAPE} +\centering +\begin{tabular}{|ll|cc|} +\hline +\multicolumn{2}{|l|}{\multirow{2}{*}{}} & \multicolumn{2}{c|}{\textbf{Measure}} \\ \cline{3-4} +\multicolumn{2}{|l|}{} & \multicolumn{1}{c|}{MSE} & MAPE \\ \hline +\multicolumn{1}{|l|}{\multirow{5}{*}{\textbf{Model}}} & {\textbf{AEMO}} & \multicolumn{1}{c|}{{55,159}} & {2.190\%} \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{Linear Regression} & \multicolumn{1}{c|}{49,718} & 2.080\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{SARIMA} & \multicolumn{1}{c|}{49,519} & 2.049\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{XGBoost} & \multicolumn{1}{c|}{46,526} & 2.042\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{Random Forest} & \multicolumn{1}{c|}{51,395} & 2.095\% \\ \hline +\end{tabular} +(\#tab:tab8) +\end{table} + + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/Comparison} \caption{MSE Distribution}(\#fig:Cpmparison) +\end{figure} + + +## Summary of Key Findings + +- **All models outperformed AEMO’s model in terms of MSE and MAPE.** This indicates that there are likely underlying patterns in the AEMO residuals that are not currently captured in their model, therefore their model could be improved. This also validates forecasting methodologies which aims to use forecasting errors in modelling to further improve forecasting accuracy. + +- **XGBoost performed best compared with linear regression, SARIMA and Decision trees.** This may indicate that some underlying patterns in AEMO’s forecast errors are likely non-linear, and therefore best forecasted by a black box method that can handle non-linear relationships. + +- **SARIMA also performed strongly**, indicating seasonality in the forecast error. This reinforces EDA findings where correlations existed between lagged forecast errors and current forecast errors. The SARIMA model also performed quite strongly based on MSE and MAPE. Furthermore, the SARIMA model which only included the lagged error performed the strongest, which provides further evidence of this relationship. + +- **Both machine learning models performed better when including weather related variables.** This may indicate a non-linear relationship between the forecast error (at least partly) and weather indicators (note, SARIMA would be limited in its ability to capture non-linearity). + +\newpage + +# Discussion + +## Interpretation of results + +The results of this project demonstrate the validity of using forecast errors in modelling to further improve electricity demand predictions. This is particularly important for short-term energy demand forecasting which relies on the precision of forecasts to balance electricity demand and supply rather than requiring an interpretable model. + +\bigskip + +While XGBoost was the best performing model for the datasets considered, this may not be the case for all forecasts, depending on the underlying patterns in the forecast errors. If patterns in forecast errors are linear, traditional models such as ARIMA may perform better to improve the forecast. On the other hand, this project demonstrated that non-linear trends were present in the forecast errors which allowed the XGBoost model to be the better performing model. + +\bigskip + +Both machine learning methods performed better when incorporating weather indicator variables which may point to different models capturing different types of relationships (e.g. SARIMA capturing the effect of the lagged error and XGBoost capturing effects of weather-related variables). This could indicate that modelling the forecast errors of the best model (XGBoost) with a different model (e.g. SARIMA) has the potential to produce even more accurate results. + +## Implications for energy planning + +Future short-term electricity forecasts should consider how residuals could be used to further improve forecasting accuracy. This could involve decomposing the data in the first instance as others have done and discussed in the literature review, or incorporating residuals from an initial forecasted model in a subsequent model. Improving forecast accuracy will likely have the benefit of greater efficiency in managing electricity generation. + +\bigskip + +It should be noted that the method described in this report has proven useful to increase accuracy of forecast predictions which is essential for short-term forecasting, however may not be appropriate for longer-term forecasting where interpretability of the model is more important. + +## Limitations, challenges, and further research + +While this project was able to improve on the AEMO forecast by modelling the forecast error, there is the potential that this could have been achieved more efficiently if a detailed AEMO forecasting methodology was available. This could have provided a better idea of what information or trends could be missing from the original forecast, and therefore which method would have been most useful to model the residuals. + +\bigskip + +This study only considered 12-hour interval due to the complexity of including several intervals and computational burden. Future research could consider longer or shorter forecast intervals to test the impact of modelling forecasting errors to improve accuracy for different intervals. Further research could also consider forecasting model errors using multiple models to capture the different patterns of errors. + +# Conclusion and Further Issues {} + +Hybrid models are known to improve the accuracy of electricity demand forecasts. This report took the concepts of a hybrid model to improve AEMO’s short-term electricity demand forecast by incorporating AEMO forecast errors in a subsequent model to produce a more accurate prediction. The report found that for all models tested, the accuracy of the short-term forecast improved. This is valuable to industry as balancing the supply and demand of energy requires highly accurate forecasting. While this report only considered a 12-hour forecast interval, future studies could investigate different forecast intervals or multiple-iteration error-corrections to improve the accuracy of energy demand forecasts. + +\newpage + +# References {-} + +\begin{hangparas}{.25in}{1} +Abbasi, R.A., Javaid, N., Ghuman, M.N.J., Khan, Z.A., Ur Rehman, S. \& Amanullah (2019). ‘Short Term Load Forecasting Using XGBoost’, \textit{Advances in Intelligent Systems and Computing}, pp.1120–1131. doi:\url{https://doi.org/10.1007/978-3-030-15035-8_108}. + +AEMO. (2023). Load forecasting. Available at: \url{https://aemo.com.au/-/media/files/electricity/nem/security_and_reliability/power_system_ops/procedures/so_op_3710-load-forecasting.pdf?la=en} [Accessed 24 Mar. 2025]. + +Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H. \& Saidur, R. (2014). ‘A review on applications of ANN and SVM for building electrical energy consumption forecasting’, \textit{Renewable and Sustainable Energy Reviews}, [online] 33, pp.102–109. doi:\url{https://doi.org/10.1016/j.rser.2014.01.069}. + +Ahmad, T. \& Chen, H. (2018). ‘Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment’, \textit{Energy}, 160, pp.1008–1020. doi:\url{https://doi.org/10.1016/j.energy.2018.07.084}. + +Ahmad, W., Ayub, N., Ali, T., Irfan, M., Awais, M., Shiraz, M. \& Glowacz, A. (2020). ‘Towards short term electricity load forecasting using improved support vector machine and extreme learning machine’, \textit{Energies}, 13(11), p.2907. doi:\url{https://doi.org/10.3390/en13112907}. + +Amara, F., Agbossou, K., Dubé, Y., Kelouwani, S., Cardenas, A. \& Hosseini, S.S. (2019). ‘A residual load modeling approach for household short-term load forecasting application’, \textit{Energy and Buildings}, 187, pp.132–143. doi:\url{https://doi.org/10.1016/j.enbuild.2019.01.009}. + +Andronikos, A., Tzelepi, M. \& Tefas, A. (2023). ‘Residual Error Learning for Electricity Demand Forecasting’, In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C. \& Pimenidis, E. (eds) \textit{Engineering Applications of Neural Networks}. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. doi:\url{https://doi.org/10.1007/978-3-031-34204-2_33}. + +Añel, J.A., Pérez-Souto, C., Bayo-Besteiro, S., Prieto-Godino, L., Bloomfield, H., Troccoli, A. \& Laura (2024). ‘Extreme weather events and the energy sector in 2021’, \textit{Weather Climate and Society}. doi:\url{https://doi.org/10.1175/wcas-d-23-0115.1}. + +Ardakani, F.J. \& Ardehali, M.M. (2014). ‘Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types’, \textit{Energy}, 65, pp.452–461. doi:\url{https://doi.org/10.1016/j.energy.2013.12.031}. + +Boroojeni, K.G., Amini, M.H., Bahrami, S., Iyengar, S.S., Sarwat, A.I. \& Karabasoglu, O. (2017). ‘A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon’, \textit{Electric Power Systems Research}, 142, pp.58–73. doi:\url{https://doi.org/10.1016/j.epsr.2016.08.031}. + +Deng, X., Ye, A., Zhong, J., Xu, D., Yang, W., Song, Z., Zhang, Z., Guo, J., Wang, T., Tian, Y., Pan, H., Zhang, Z., Wang, H., Wu, C., Shao, J. \& Chen, X. (2022). ‘Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model’, \textit{Energy Reports}, 8, pp.8661–8674. doi:\url{https://doi.org/10.1016/j.egyr.2022.06.072}. + +Divina, F., García Torres, M., Goméz Vela, F.A. \& Vázquez Noguera, J.L. (2019). ‘A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings’, \textit{Energies}, 12(10), p.1934. doi:\url{https://doi.org/10.3390/en12101934}. + +Ediger, V.Ş. \& Akar, S. (2007). ‘ARIMA forecasting of primary energy demand by fuel in Turkey’, \textit{Energy Policy}, 35(3), pp.1701–1708. doi: \url{https://doi.org/10.1016/j.enpol.2006.05.009}. + +Ertuğrul, Ö.F., Tekin, H. \& Tekin, R. (2020). ‘A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid’, \textit{Electrical Engineering}, 103, pp: 717-728. doi:\url{https://doi.org/10.1007/s00202-020-01114-3}. + +Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R. \& Young, W.A. (2016). ‘An overview of energy demand forecasting methods published in 2005–2015’, \textit{Energy Systems}, 8(2), pp.411–447. doi:\url{https://doi.org/10.1007/s12667-016-0203-y}. + +Klyuev, R.V., Morgoev, I.D., Morgoeva, A.D., Gavrina, O.A., Martyushev, N.V., Efremenkov, E.A. \& Mengxu, Q. (2022). ‘Methods of Forecasting Electric Energy Consumption: A Literature Review’, \textit{Energies}, 15(23), p.8919. doi:\url{https://doi.org/10.3390/en15238919}. + +Kopyt, M., Piotrowski, P. \& Baczyński, D. (2024). ‘Short-Term Energy Generation Forecasts at a Wind Farm—A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models’, \textit{Energies}, 17(23), p.6194. doi:\url{https://doi.org/10.3390/en17236194}. + +Koukaras, P., Mustapha, A., Mystakidis, A. \& Tjortjis, C. (2024). ‘Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models’, \textit{Energies}, 17(6), p.1450. doi:\url{https://doi.org/10.3390/en17061450}. + +Kuo, P.-H. \& Huang, C.-J. (2018). ‘A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting’, \textit{Energies}, 11(1), p.213. doi:\url{https://doi.org/10.3390/en11010213}. + +Liu, X.-Q., Zhang, C., Zhou, Y. \& Liao, H. (2021). ‘Temperature change and electricity consumption of the group living: A case study of college students’, \textit{Science of The Total Environment}, 781, p.146574. doi:\url{https://doi.org/10.1016/j.scitotenv.2021.146574}. + +Maia-Silva, D., Kumar, R. \& Nateghi, R. (2020). ‘The critical role of humidity in modeling summer electricity demand across the United States’, \textit{Nature Communications}, 11, p.1686. doi:\url{https://doi.org/10.1038/s41467-020-15393-8}. + +Manno, A., Martelli, E. and Amaldi, E. (2022). ‘A Shallow Neural Network Approach for the Short-Term Forecast of Hourly Energy Consumption’, \textit{Energies}, [online] 15(3), pp.958. doi:\url{https://doi.org/10.3390/en15030958}. + +Marco G. Pinheiro, Sara C. Madeira, Alexandre P. Francisco, (2023). ‘Short-term electricity load forecasting—A systematic approach from system level to secondary substations’, \textit{Applied Energy}, 332, pp.120493, ISSN 0306-2619, doi:\url{https://doi.org/10.1016/j.apenergy.2022.120493}. + +Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D. and Tjortjis, C. (2024). ‘Energy Forecasting: A Comprehensive Review of Techniques and Technologies’, \textit{Energies}, [online] 17(7), pp.1662. doi:\url{https://doi.org/10.3390/en17071662}. + +OpenWeather. (2025). \textit{Custom Weather Products.} [Online] Available at: \url{https://home.openweathermap.org/marketplace} [Accessed 29 Mar. 2025] + +Pao, H.T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear models. \textit{Energy}, 34(10), pp.1438–1446. doi:\url{https://doi.org/10.1016/j.energy.2009.04.026}. + +Papalexopoulos, A.D. and Hesterberg, T.C. (1990). ‘A regression-based approach to short-term system load forecasting’, \textit{IEEE Transactions on Power Systems}, 5(4), pp.1535–1547. doi:\url{https://doi.org/10.1109/59.99410}. + +Phyo, P.P. and Byun, Y.-C. (2021). ‘Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction’, \textit{Symmetry}, 13(10), pp.1942. doi:\url{https://doi.org/10.3390/sym13101942}. + +Rakpho, P. and Yamaka, W. (2021). ‘The forecasting power of economic policy uncertainty for energy demand and supply’, \textit{Energy Reports}, 7, pp.338–343. doi:\url{https://doi.org/10.1016/j.egyr.2021.06.059}. + +Sanhudo, L., Rodrigues, J. and Filho, Ê.V. (2021). ‘Multivariate time series clustering and forecasting for building energy analysis: Application to weather data quality control’, \textit{Journal of Building Engineering}, 35, pp.101996. doi:\url{https://doi.org/10.1016/j.jobe.2020.101996}. + +Savić, S., Selakov, A. and Milošević, D. (2014). ‘Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas’, \textit{Natural Hazards}, 73(2), pp.373–387. doi:\url{https://doi.org/10.1007/s11069-014-1074-y}. + +Singh, S. and Yassine, A. (2018). ‘Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting’, \textit{Energies}, 11(2), pp.452. doi:\url{https://doi.org/10.3390/en11020452}. + +Suganthi, L. and Samuel, A.A. (2012). ‘Energy models for demand forecasting—A review’, \textit{Renewable and Sustainable Energy Reviews}, 16(2), pp.1223–1240. doi:\url{https://doi.org/10.1016/j.rser.2011.08.014}. + +Tarmanini, C., Sarma, N., Gezegin, C. and Ozgonenel, O. (2023). ‘Short term load forecasting based on ARIMA and ANN approaches’, \textit{Energy Reports}, 9, pp.550–557. doi:\url{https://doi.org/10.1016/j.egyr.2023.01.060}. + +Wang, J., Li, P., Ran, R., Che, Y. and Zhou, Y. (2018). ‘A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree’, \textit{Applied Sciences}, 8(5), pp.689. doi:\url{https://doi.org/10.3390/app8050689}. + +Zhang, S., Guo, Q., Smyth, R. and Yao, Y. (2022). ‘Extreme temperatures and residential electricity consumption: Evidence from Chinese households’, \textit{Energy Economics}, pp.105890. doi:\url{https://doi.org/10.1016/j.eneco.2022.105890}. +\end{hangparas} + +\newpage + +# Appendix {-} + +\appendix + +## Appendix A: Data Processing {-} + +Packages used for data cleaning + + +``` python +import pandas as pd +import numpy as np + +pd.options.mode.chained_assignment = None +``` + +Importing the data + + +``` python +# Forecast Data +## Reading data and formating data-time columns +df_forecast = pd.read_csv('data/forecastdemand_nsw.csv', names = + ['id', 'region_id', 'period_id', 'forecast_demand', + 'date_time_current', 'date_time_future'], skiprows = 1) +df_forecast.date_time_current = pd.to_datetime( + df_forecast.date_time_current, format = "%Y-%m-%d %H:%M:%S") +df_forecast.date_time_future = pd.to_datetime( + df_forecast.date_time_future, format = "%Y-%m-%d %H:%M:%S") + +## Using 'period_id' to round 'current time' +df_forecast["date_time_current_rounded"] = df_forecast.period_id.apply( + lambda x: pd.Timedelta(hours = x/2)) +df_forecast.date_time_current_rounded = df_forecast.date_time_future - + df_forecast.date_time_current_rounded + +# Demand Data +## Reading data and formating data-time columns +df_demand = pd.read_csv('data/totaldemand_nsw.csv', names = + ['date_time', 'total_demand', 'region_id'], skiprows = 1) +df_demand.date_time = pd.to_datetime(df_demand.date_time, + format = "%d/%m/%Y %H:%M") + + +# Forecast temperature data +df_weather_forecast = pd.read_csv('data/forecast_temperatre.csv') + +df_weather_forecast = df_weather_forecast.rename( + {'forecast dt iso': 'date_time_current_utc', + 'slice dt iso': 'date_time_future_utc', + 'temperature': 'temperature_future_forecast', + 'humidity': 'humidity_future_forecast', + 'rain': 'rain_future_forecast', + 'wind_speed': 'wind_speed_future_forecast'}, axis = 1) + +df_weather_forecast["date_time_current_rounded"] = + pd.to_datetime(df_weather_forecast.date_time_current_utc, + format = "%Y-%m-%d %H:%M:%S +0000 UTC") + pd.Timedelta(hours = 10) +df_weather_forecast["date_time_future"] = + pd.to_datetime(df_weather_forecast.date_time_future_utc, + format = "%Y-%m-%d %H:%M:%S +0000 UTC") + pd.Timedelta(hours = 10) + +df_weather_forecast = + df_weather_forecast[['date_time_current_rounded', 'date_time_future', + 'temperature_future_forecast', 'humidity_future_forecast', + 'rain_future_forecast', 'wind_speed_future_forecast']] +``` + + +Merging the data + + +``` python +#Merging Datasets +df_all = pd.merge(df_forecast, df_demand[["date_time", "total_demand"]], + left_on = "date_time_future", right_on = "date_time").drop( + columns = "date_time") + +df_all = pd.merge(df_all, + df_temperature[["date_time_30m", "temperature"]], + left_on = "date_time_future", right_on = "date_time_30m") +df_all = df_all.drop( + columns = ["date_time_30m", "region_id"]).rename( + {"temperature": "temperature_future"}, axis = 1) + +df_all = pd.merge(df_all, + df_temperature[["date_time_30m", "temperature"]], + left_on = "date_time_current_rounded", right_on = "date_time_30m") +df_all = df_all.drop(columns = + "date_time_30m").rename({"temperature": "temperature_current"}, + axis = 1) + +df_all = pd.merge(df_all, df_weather_forecast, on = + ["date_time_current_rounded", "date_time_future"], how = 'left') +``` + +Format data. + + +``` python +df_all["forecast_interval"] = df_all.date_time_future - + df_all.date_time_current_rounded +df_all["forecast_error"] = df_all.total_demand - + df_all.forecast_demand +df_all["forecast_error_relative"] = + df_all.forecast_error/df_all.total_demand + +df_all["date_time_future_month"] = df_all.date_time_future.dt.month +df_all["date_time_future_year"] = df_all.date_time_future.dt.year +df_all["date_time_future_weekday"] = df_all.date_time_future.dt.dayofweek +df_all["date_time_future_hour"] = df_all.date_time_future.dt.hour + +df_all["week_day_name"] = df_all.date_time_future.dt.day_name() + +df_all["isSaturday"] = df_all.week_day_name.apply( + lambda x: 1 if x == 'Saturday' else 0) +df_all["isSunday"] = df_all.week_day_name.apply( + lambda x: 1 if x == 'Sunday' else 0) + +df_all["isDecember"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 12 else 0) +df_all["isJanuary"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 1 else 0) +df_all["isFebruary"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 2 else 0) +df_all["isNovember"] = df_all.date_time_future_month.apply( + lambda x: 1 if x == 11 else 0) +``` + +\newpage + +## Appendix B: Models {-} + +### B1. AEMO Model {.unlisted .unnumbered} + +Import packages + + +``` python +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import statsmodels.api as sm + +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +from matplotlib.pyplot import figure +``` + + + +``` python +delta = 24 + +df_lag = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) +df_lag_temp = +df_lag.copy()[ + ["forecast_error", "forecast_error_relative", "date_time_future"] + ].rename({"forecast_error" : "forecast_error_24h_ago", + "forecast_error_relative": "forecast_error_relative_24h_ago", + "date_time_future": "date_time_future_24h_ago"}, axis = 1) +df_lag["date_time_current_24h_ago"] = + df_lag.date_time_current - pd.DateOffset(hours = 24) +df_lag["date_time_future_24h_ago"] = + df_lag.date_time_future - pd.DateOffset(hours = 24) + +df_lag = df_lag.loc[ + df_lag.date_time_future_24h_ago >= min(df_lag.date_time_future)] +df_lag = pd.merge(df_lag, df_lag_temp, + on = "date_time_future_24h_ago", how = 'left') +df_lag = df_lag.loc[df_lag.forecast_error_relative_24h_ago.notna()] + +train_test_split = 0.7 +split_int = int(train_test_split * len(df_lag)) +df_lag_train, df_lag_test = df_lag[:split_int], df_lag[split_int:] +``` + + +``` python +mse = mean_squared_error(df_lag_test.forecast_demand, + df_lag_test.total_demand) +mape = mean_absolute_percentage_error(df_lag_test.forecast_demand, + df_lag_test.total_demand) + +print(f"Existing model MSE = {round(mse)}") +print(f"Existing model MAPE = {round(100*mape,2)}%") +``` + +### B2. Linear Regression {.unlisted .unnumbered} + +Import packages + + +``` python +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import statsmodels.api as sm +import warnings + +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +from statsmodels.tsa.stattools import adfuller +from matplotlib.pyplot import figure +from statsmodels.graphics.api import qqplot +``` + +**Linear Regression Model 1** + + +``` python +x_columns = ["forecast_error_24h_ago"] +x = sm.add_constant(df_lag_train[x_columns]) +x = sm.add_constant(x) +y = np.array(df_lag_train.forecast_error) + +model = sm.OLS(y, x) +results = model.fit() +print(results.summary()) + +df_lag_test["lm_forecast_error_pred"] = + results.predict(sm.add_constant(df_lag_test[x_columns])) +df_lag_test["lm_forecast_demand_new"] = + df_lag_test.forecast_demand + df_lag_test.lm_forecast_error_pred + +mse_lm1 = mean_squared_error(df_lag_test.lm_forecast_demand_new, + df_lag_test.total_demand) +mape_lm1 = mean_absolute_percentage_error( + df_lag_test.lm_forecast_demand_new,df_lag_test.total_demand) + +print(f"\nNew model MSE = {round(mse_lm1)}") +print(f"New model MAPE = {round(100*mape_lm1,3)}%") +``` + +**Linear Regression Model 2** + + +``` python +x_columns = ["forecast_error_24h_ago", "Temperature", "Humidity", + "Wind_speed", "Rain", "isSaturday", "isSunday", "isDecember", + "isJanuary", "isNovember"] +x = sm.add_constant(df_lag_train[x_columns]) +x = sm.add_constant(x) +y = np.array(df_lag_train.forecast_error) + +model = sm.OLS(y, x) +results = model.fit() +print(results.summary()) + +df_lag_test["lm2_forecast_error_pred"] = + results.predict(sm.add_constant(df_lag_test[x_columns])) +df_lag_test["lm2_forecast_demand_new"] = df_lag_test.forecast_demand + + df_lag_test.lm2_forecast_error_pred + +mse_lm2 = mean_squared_error( + df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand) +mape_lm2 = mean_absolute_percentage_error( + df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand) + +print(f"\nNew model MSE = {round(mse_lm2)}") +print(f"New model MAPE = {round(100*mape_lm2,2)}%") +``` + +### B2. SARIMA {.unlisted .unnumbered} + +Import packages + + +``` python +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +import statsmodels.api as sm +import warnings + +from statsmodels.graphics.tsaplots import plot_acf, plot_pacf +from statsmodels.tsa.stattools import adfuller +from matplotlib.pyplot import figure +from sklearn.linear_model import LinearRegression +from statsmodels.tsa.arima.model import ARIMA +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +``` + +\noindent SARIMA models considered: + +* order=(1,0,0), seasonal_order=(0, 0, 0, 0) +* order=(1,0,1), seasonal_order=(0, 0, 0, 0) +* order=(7,0,1), seasonal_order=(0, 0, 0, 0) +* order=(7,0,7), seasonal_order=(0, 0, 0, 0) +* order=(1,0,0), seasonal_order=(1, 0, 1, 7) +* order=(6,0,2), seasonal_order=(1, 0, 1, 7) +* order=(6,0,2), seasonal_order=(1, 0, 2, 7) +* order=(6,0,1), seasonal_order=(2, 0, 1, 7) +* order=(2,0,1), seasonal_order=(2, 0, 1, 7) + +\noindent Model evaluation and tuning + + +``` python +sarimas = pd.DataFrame({"order":[(1,0,0), (1,0,1), (7,0,1), (7,0,7), + (1,0,0), (6,0,2), (6,0,2), (6,0,1), + (2,0,1)], + + "seasonal_order": [(0, 0, 0, 0), (0, 0, 0, 0), + (0, 0, 0, 0), (0, 0, 0, 0), + (1, 0, 1, 7), (1, 0, 1, 7), + (1, 0, 2, 7), (2, 0, 1, 7), + (2, 0, 1, 7)]}) +sarimas = sarimas.reset_index().rename({"index": "id"}, axis = 1) + +columns = ["sarima_id", "hour_of_day", "order", "seasonal_order", + "ljung_val", "ljung_p", "jb_val", "jb_p", "hetro_val", "hetro_p", + "skew", "kurtosis", "aic", "bic", "n_observations", "mse_pre", + "mse_post", "mape"] +sarima_tune = pd.DataFrame(columns = columns) + +period_id = 24 + +hours_all = [0, 4, 8, 12, 16, 20] + +for hour_of_day in hours_all: + for index, row in sarimas.iterrows(): + order = row["order"] + seasonal_order = row["seasonal_order"] + sarima_id = row["id"] + + df_all_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + # Model fit + model = ARIMA(df_all_delta.forecast_error, order = order, + seasonal_order = seasonal_order) + model_fit = model.fit() + df_all_delta["predicted_forecast_error"] = model_fit.fittedvalues + df_all_delta["new_forecast"] = df_all_delta.forecast_demand + + df_all_delta.predicted_forecast_error + + # Model Evaluation (MSE) + mse_pre = mean_squared_error(df_all_delta.total_demand, + df_all_delta.forecast_demand) + mse_post = mean_squared_error(df_all_delta.total_demand, + df_all_delta.new_forecast) + mape = mean_absolute_percentage_error(df_all_delta.total_demand, + df_all_delta.new_forecast) + + # Model Evaluation (Crit values) + stat_tests = pd.read_html(model_fit.summary().tables[2].as_html(), + header=None,index_col=0)[0] + ljung_val, ljung_p = stat_tests[1].iloc[0], stat_tests[1].iloc[1], + jb_val, jb_p = stat_tests[3].iloc[0], stat_tests[3].iloc[1], + hetro_val, hetro_p = stat_tests[1].iloc[2], stat_tests[1].iloc[3], + skew, kurtosis = stat_tests[3].iloc[2], stat_tests[3].iloc[3] + + # Model Evaluation (AIC, BIC) + stat_tests = pd.read_html(model_fit.summary().tables[0].as_html(), + header=None,index_col=0)[0] + aic, bic = stat_tests[3].iloc[2], stat_tests[3].iloc[3] + n_observations = stat_tests[3].iloc[0] + sarima_tune = sarima_tune.append(pd.DataFrame([[sarima_id, + hour_of_day, order, seasonal_order, ljung_val, ljung_p, + jb_val, jb_p, hetro_val, hetro_p, skew, kurtosis, aic, + bic, n_observations, mse_pre, mse_post, mape]], + columns=columns), ignore_index=True) +``` + + +``` python +sarima_tune["mse_improvement"] = round(100*(sarima_tune.mse_pre - + sarima_tune.mse_post)/sarima_tune.mse_pre) +sarima_tune = pd.merge(sarima_tune, sarimas, on = + ["order", "seasonal_order"], how = "left").sort_values("id") + +plot = sarima_tune.groupby(["order", "seasonal_order", "hour_of_day"], + as_index = False).mean() +sns.lineplot(data = plot, x = 'hour_of_day', y = 'mape', hue = 'id', + palette = 'pastel', alpha = 1, linestyle = '--') +``` + + +``` python +sarima_tune["mse_improvement"] = round(100*(sarima_tune.mse_pre - + sarima_tune.mse_post)/sarima_tune.mse_pre) + +plot = sarima_tune.groupby(["order", "seasonal_order", "hour_of_day"], + as_index = False).mean() +sns.lineplot(data = plot, x = 'hour_of_day', y = 'mse_improvement', + hue = 'id', palette = 'pastel', alpha = 1, linestyle = '--') +``` + +\noindent Parameters chosen + + +``` python +period_id = 24 +arima_order = (6,0,2) +arima_season_order = (1, 0, 1, 7) + +train_test_split = 0.7 +``` + +\noindent textbf{SARIMA Model 1 - Without Exogenous Variables} + + +``` python +df_predict = pd.DataFrame(columns = ["period_id", "date_time_future", + "new_forecast", "forecast_demand", "total_demand"]) + +for hour_of_day in set(df_all.date_time_future_hour): + df_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + # Test/Train split + split_int = int(train_test_split * len(df_delta)) + df_delta_train, df_delta_test = + df_delta[:split_int], df_delta[split_int:] + x_all, x_train, x_test = df_delta.forecast_error, + df_delta_train.forecast_error, df_delta_test.forecast_error + + # Model - Train Data + arima_model_train = ARIMA(x_train, order = arima_order, + seasonal_order = arima_season_order) + arima_mode_train_fit = arima_model_train.fit() + + # Model - Test Data + arima_model_test = ARIMA(x_all, order = arima_order, + seasonal_order = arima_season_order) + arima_model_test_fit = arima_model_test.filter( + arima_mode_train_fit.params) + + # Predicted Values + arima_model_test_predict = + arima_model_test_fit.predict().loc[split_int:] + + # Calculate new forecast + df_delta_test["predicted_forecast_error"] = arima_model_test_predict + df_delta_test["new_forecast"] = df_delta_test.forecast_demand + + df_delta_test.predicted_forecast_error + + # Model evaluation + mse_pre = mean_squared_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mse_post = mean_squared_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + mape_pre = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mape_post = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + + df_predict = pd.concat([df_predict, df_delta_test[["period_id", + "date_time_future", "new_forecast", "forecast_demand", + "total_demand"]]]) +``` + +\noindent textbf{SARIMA Model 2 - With Exogenous Variables} + + +``` python +df_predict_with_exog = pd.DataFrame(columns = ["period_id", + "date_time_future", "new_forecast", "forecast_demand", + "total_demand"]) +exog_vars = ["Temperature", "Humidity", "Wind_speed", "Rain"] + +for hour_of_day in set(df_all.date_time_future_hour): + df_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + # Test/Train split + split_int = int(train_test_split * len(df_delta)) + df_delta_train, df_delta_test = + df_delta[:split_int], df_delta[split_int:] + x_all, x_train, x_test = + df_delta.forecast_error, df_delta_train.forecast_error, + df_delta_test.forecast_error + exog_all, exog_train, exog_test = + df_delta[exog_vars], df_delta_train[exog_vars], + df_delta_test[exog_vars] + + # Model - Train Data + arima_model_train = ARIMA(x_train, exog = exog_train, + order = arima_order, seasonal_order = arima_season_order) + arima_mode_train_fit = arima_model_train.fit() + + # Model - Test Data + arima_model_test = ARIMA(x_all, exog = exog_all, + order = arima_order, + seasonal_order = arima_season_order) + arima_model_test_fit = + arima_model_test.filter(arima_mode_train_fit.params) + + # Predicted Values + arima_model_test_predict = + arima_model_test_fit.predict().loc[split_int:] + + # Calculate new forecast + df_delta_test["predicted_forecast_error"] = arima_model_test_predict + df_delta_test["new_forecast"] = df_delta_test.forecast_demand + + df_delta_test.predicted_forecast_error + + # Model evaluation + mse_pre = mean_squared_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mse_post = mean_squared_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + mape_pre = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.forecast_demand) + mape_post = mean_absolute_percentage_error(df_delta_test.total_demand, + df_delta_test.new_forecast) + + df_predict_with_exog = pd.concat([df_predict_with_exog, + df_delta_test[["period_id", "date_time_future", + "new_forecast", "forecast_demand", "total_demand"]]]) +``` + +Model evaluation + + +``` python +df_predict["forecast_error_old"] = + df_predict.total_demand - df_predict.forecast_demand +df_predict["forecast_error_new"] = + df_predict.total_demand - df_predict.new_forecast +df_predict_with_exog["forecast_error_new"] = + df_predict_with_exog.total_demand - df_predict_with_exog.new_forecast + +mse_pre = mean_squared_error( + df_predict.total_demand, df_predict.forecast_demand) +mse_sarima = mean_squared_error( + df_predict.total_demand, df_predict.new_forecast) +mse_sarima_with_exog = mean_squared_error( + df_predict_with_exog.total_demand, + df_predict_with_exog.new_forecast) + +mape_pre = mean_absolute_percentage_error( + df_predict.total_demand, df_predict.forecast_demand) +mape_sarima = mean_absolute_percentage_error( + df_predict.total_demand, df_predict.new_forecast) +mape_sarima_with_exog = mean_absolute_percentage_error( + df_predict_with_exog.total_demand, + df_predict_with_exog.new_forecast) +``` + +### B3. Random Forest {.unlisted .unnumbered} + +Import packages + + +``` python +import pandas as pd +from sklearn.ensemble import RandomForestRegressor +from sklearn.metrics import mean_absolute_error, mean_squared_error +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +``` + +\noindent Data Processing + + +``` python +# Filter for PERIODID 24 and sort +df_sliced = df[df["period_id"] == 24].copy() +df_sliced = df_sliced.sort_values("date_time_future") +df_sliced = df_sliced.dropna() + +# Get forecast error +df_sliced['forecast_error'] = df_sliced['total_demand'] - + df_sliced['forecast_demand'] +df_sliced['forecast_error_lag24h'] = df_sliced.sort_values( + 'date_time_current_rounded')['forecast_error'].shift(24) + +# Check for NaN counts in key columns +print("\nNaN counts in key columns:") +for col in ['demand_lag_24h', 'demand_lag_48h', 'demand_lag_7d', + 'forecast_error_lag12h']: + if col in df_sliced.columns: + print(f"{col}: {df_sliced[col].isna().sum()} NaNs") +``` + +\noindent \textbf{RFMF1 - Random Forest Model 1} + + +``` python +# Define features that would be available at prediction +# time (12 hours ahead) +features = [ + # Basic time features + # Original forecast + 'forecast_demand', + 'forecast_error_lag24h' +] +# Define target +target = 'total_demand' + +# Create the modeling dataframe +model_df = df_sliced[features + [target] + + ['date_time_current_rounded']].copy() + +# Print the shape before dropping missing values +print(f"\nShape before dropping missing values: {model_df.shape}") + +# Drop rows with NaN values +model_df = model_df.dropna() +print(f"Shape after dropping NaN values: {model_df.shape}") + +# If we still have no data, show a clear error and exit +if len(model_df) == 0: + print("ERROR: No data left after dropping NaN values!") + import sys + sys.exit(1) + +# Sort data to ensure temporal order +model_df = model_df.sort_values( + 'date_time_current_rounded').reset_index(drop=True) + +# Split data temporally - use 70-30 split +X = model_df[features] +y = model_df[target] +train_size = 0.7 +split_idx = int(len(model_df) * train_size) + +# Split into train/test +X_train = X.iloc[:split_idx] +y_train = y.iloc[:split_idx] +X_test = X.iloc[split_idx:] +y_test = y.iloc[split_idx:] + +# Train model +model = RandomForestRegressor( + n_estimators=200, + max_depth=10, + min_samples_split=5, + min_samples_leaf=2, + random_state=42, + n_jobs=-1 +) +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) +y_pred_original = X_test["forecast_demand"] +``` + +\noindent Evaluate performance + + +``` python +# Evaluate performance +def calculate_metrics(y_true, y_pred): + mse = mean_squared_error(y_true, y_pred) + mape = np.mean( + np.abs((y_true - y_pred) / np.maximum(0.001, y_true))) * 100 + return mse, mape + +# Model metrics +model_mse, model_mape = calculate_metrics(y_test, y_pred) + +# Original forecast metrics +original_mse, original_mape = calculate_metrics(y_test, y_pred_original) + +# Print formatted results +print("\nModel Performance:") +print(f"- MSE: {model_mse:.3f}") +print(f"- MAPE: {model_mape:.3f}%") + +print("\nOriginal Forecast Performance:") +print(f"- MSE: {original_mse:.3f}") +print(f"- MAPE: {original_mape:.3f}%") + +# Calculate improvement percentages +improvement_mse = (1 - model_mse/original_mse) * 100 +improvement_mape = (1 - model_mape/original_mape) * 100 + +print("\nImprovement Over Original Forecast:") +print(f"- MSE: {improvement_mse:.3f}%") +print(f"- MAPE: {improvement_mape:.3f}%") + +# Feature importance +feature_importance = pd.DataFrame( + {'Feature': features, + 'Importance': model.feature_importances_} +).sort_values('Importance', ascending=False) + +print("\nFeature Importance:") +print(feature_importance) +``` + +\noindent \textbf{RFMF2 - Random Forest Model 2} + + +``` python +# Define features that would be available at prediction +# time (12 hours ahead) +features = [ + 'forecast_demand', + 'Temperature', + 'Humidity', + 'Wind_speed', + 'Rain', + 'forecast_error_lag24h' +] + + +# Define target +target = 'total_demand' + + +# Print the number of NaN values for each feature +print("\nNaN counts in features:") +for feature in features: + print(f"{feature}: {df_sliced[feature].isna().sum()} NaNs") + +# Create the modeling dataframe +model_df = df_sliced[features + [target] ].copy() +# Drop rows with NaN values +model_df = model_df.dropna() +print(f"Shape after dropping NaN values: {model_df.shape}") + + +# Split data temporally - using 70-30 split +X = model_df[features] +y = model_df[target] +train_size = 0.7 +split_idx = int(len(model_df) * train_size) + +# Split into train/test +X_train = X.iloc[:split_idx] +y_train = y.iloc[:split_idx] +X_test = X.iloc[split_idx:] +y_test = y.iloc[split_idx:] + + +# Train model +model = RandomForestRegressor( + n_estimators=200, + max_depth=10, + min_samples_split=5, + min_samples_leaf=2, + random_state=42, + n_jobs=-1 +) +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) +y_pred_original = X_test["forecast_demand"] +``` + +\noindent Evaluate performance + + +``` python +def calculate_metrics(y_true, y_pred): + mse = mean_squared_error(y_true, y_pred) + mape = np.mean( + np.abs((y_true - y_pred) / np.maximum(0.001, y_true))) * 100 + return mse, mape + +# Model metrics +model_mse, model_mape = calculate_metrics(y_test, y_pred) + +# Original forecast metrics +original_mse, original_mape = calculate_metrics(y_test, y_pred_original) + +# Print formatted results +print("\nModel Performance:") +print(f"- MSE: {model_mse:.3f}") +print(f"- MAPE: {model_mape:.3f}%") + +print("\nOriginal Forecast Performance:") +print(f"- MSE: {original_mse:.3f}") +print(f"- MAPE: {original_mape:.3f}%") + +# Calculate improvement percentages +improvement_mse = (1 - model_mse/original_mse) * 100 +improvement_mape = (1 - model_mape/original_mape) * 100 + +print("\nImprovement Over Original Forecast:") +print(f"- MSE: {improvement_mse:.3f}%") +print(f"- MAPE: {improvement_mape:.3f}%") + +# Feature importance +feature_importance = pd.DataFrame( + {'Feature': features, + 'Importance': model.feature_importances_} +).sort_values('Importance', ascending=False) + +print("\nFeature Importance:") +print(feature_importance) +``` + +### B4. XGBoost {.unlisted .unnumbered} + +Import packages + + +``` python +import pandas as pd +import numpy as np +from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit +from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_percentage_error +import xgboost as xgb +import shap +import matplotlib.pyplot as plt +``` + +\noindent Data Processing + + +``` python +df['24hrpreverrors'] = df['forecast_error'].shift(24) +df['48hrpreverrors'] = df['forecast_error'].shift(48) +df['7daypreverrors'] = df['forecast_error'].shift(24 * 7) +df['14daypreverrors'] = df['forecast_error'].shift(24 * 14) +# Time-based features +df["Hour"] = df.date_time_future.dt.hour +df["MonthNumb"] = df.date_time_future.dt.month +df["Day of week"] = df.date_time_future.dt.dayofweek + +df = df.dropna() + + + +# Encode Hour as cyclic features +df["hour_sin"] = np.sin(2 * np.pi * df["Hour"] / 24) +df["hour_cos"] = np.cos(2 * np.pi * df["Hour"] / 24) + +# Interaction features +df["hour_x_temp"] = df["Hour"] * df["Temperature"] +df["month_x_temp"] = df["MonthNumb"] * df["Temperature"] +df["hour_x_forecast"] = df["Hour"] * df["forecast_demand"] +df["temp_x_forecast"] = df["Temperature"] * df["forecast_demand"] +df["temp_x_hour_sin"] = df["Temperature"] * df["hour_sin"] +df["temp_x_hour_cos"] = df["Temperature"] * df["hour_cos"] +df["forecast_x_hour_sin"] = df["forecast_demand"] * df["hour_sin"] +df["forecast_x_hour_cos"] = df["forecast_demand"] * df["hour_cos"] +df["forecast_24_hour_cos"] = df["24hrpreverrors"] * df["hour_cos"] +df["forecast_24_hour_sin"] = df["24hrpreverrors"] * df["hour_sin"] +``` + +\noindent Parameter Selection + + +``` python +features = [ + 'Temperature', 'Humidity', + 'Wind_speed', 'Rain', + 'hour_sin', 'hour_cos', + 'MonthNumb', 'Day of week', + 'forecast_demand', + '24hrpreverrors', + '48hrpreverrors', '7daypreverrors', '14daypreverrors', + 'hour_x_temp', 'month_x_temp', 'hour_x_forecast', 'temp_x_forecast', + 'temp_x_hour_sin', 'temp_x_hour_cos', + 'forecast_x_hour_sin', 'forecast_x_hour_cos', + 'forecast_24_hour_cos', 'forecast_24_hour_sin' + +] + +train_df = df[(df['date_time_future'] >= "2017-10-07 23:00:00") & + (df['date_time_future'] <= "2020-03-05 23:00:00")] +test_df = df[(df['date_time_future'] > "2020-03-06 23:00:00") & + (df['date_time_future'] <= "2021-03-17 23:00:00")] + +# Prepare train/test split sets +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, + test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, + test_df['forecast_demand']) * 100 + +# TimeSeriesSplit to respect time order +tscv = TimeSeriesSplit(n_splits=3) + +# Parameter grid for randomized search +param_dist = { + 'n_estimators': [100, 150, 200, 250], + 'max_depth': [3, 4, 5], + 'learning_rate': [0.01, 0.03, 0.05, 0.1], + 'subsample': [0.7, 0.8, 1.0], + 'colsample_bytree': [0.7, 0.8, 1.0] +} + +# Create base model +xgb_model = xgb.XGBRegressor( + objective='reg:squarederror', + tree_method='hist', + random_state=42 +) + +# Randomized search +random_search = RandomizedSearchCV( + estimator=xgb_model, + param_distributions=param_dist, + n_iter=2000, + scoring='neg_mean_absolute_percentage_error', + cv=tscv, + verbose=1, + n_jobs=-1, + random_state=42 +) + +# Run the search +random_search.fit(X_train, y_train) + +# Use the best model +model = random_search.best_estimator_ + +# Optional: Print best parameters +print("Best Parameters:", random_search.best_params_) + +###Output learning_rate=0.1, n_estimators=150, max_depth=3, +### subsample=0.8 +``` + +\noindent **Tuned Model** + + +``` python +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, + test_df['forecast_demand']) * 100 + +# Model creation, taken from fine tuning +xgb_model = xgb.XGBRegressor(objective='reg:squarederror', + tree_method='hist', random_state=42) + +model = xgb.XGBRegressor( + objective='reg:squarederror', + learning_rate=0.1, + n_estimators=150, + max_depth=3, + subsample=0.8, + random_state=42 +) + +model.fit(X_train, y_train) + + +# Predict and evaluate +y_pred = model.predict(X_test) +model_mse = mean_squared_error(y_test, y_pred) +model_mape = mean_absolute_percentage_error(y_test, y_pred) * 100 +``` + +Evaluate Performance + + +``` python +# Results +print(f"Original Forecast MSE: {original_mse:.2f}") +print(f"Original Forecast MAPE: {original_mape:.3f}%") +print(f"XGBoost Tuned Model MSE: {model_mse:.2f}") +print(f"XGBoost Tuned Model MAPE: {model_mape:.3f}%") + + +# Explain model predictions using SHAP +explainer = shap.Explainer(model, X_test) +shap_values = explainer(X_test) +shap_df = pd.DataFrame(shap_values.values, columns=X_test.columns) + +# Forecast demand skews the plot so hide it +filtered_shap_values = shap_df.drop(columns=["forecast_demand"]) +filtered_X_test = X_test.drop(columns=["forecast_demand"]) + +shap.summary_plot( + filtered_shap_values.values, + features=filtered_X_test, + feature_names=filtered_X_test.columns +) +``` + +## Appendix C: Plots {-} + +Packages used for plotting data. + + +``` python +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +``` + +### Figure \@ref(fig:yeardemand) {.unlisted .unnumbered} + + +``` python +df_demand_14d = df_demand[['date_time','total_demand']].copy() + +df_demand_14d['dem_14d'] = + df_demand.total_demand.rolling(window=672).mean() + +plt.figure(figsize=(6, 4)) +plt.plot(df_demand_14d['date_time'], df_demand_14d['dem_14d'], + label='14-Day Rolling Avg', color='blue') +plt.xlabel('Year') +plt.ylabel('Total Demand (MW)') +plt.legend() +plt.show() +``` + + +### Figure \@ref(fig:monthdemand) {.unlisted .unnumbered} + + +``` python +df_demand_month = df_demand[['date_time','total_demand']].copy() + +df_demand_month['month'] = df_demand_month.date_time.dt.month +df_demand_month['month_name'] = + df_demand_month.date_time.dt.month_name().str[:3] + +plt.figure(figsize = (6,4)) +sns.boxplot( + data = df_demand_month.groupby( + "date_time", as_index = False).first().sort_values("month"), + x = 'month_name', y = "total_demand", hue = 'month', + palette = 'Blues', showfliers = False, legend = False) +plt.xlabel('Month') +plt.ylabel('Total Demand (MW)') +plt.grid(alpha = 0.5) +plt.show() +``` + + +### Figure \@ref(fig:weekdemand) {.unlisted .unnumbered} + + +``` python +df_demand_weekday = df_demand[['date_time','total_demand']].copy() + +df_demand_weekday['weekday'] = df_demand_weekday.date_time.dt.day_of_week +df_demand_weekday['weekday_name'] = + df_demand_weekday.date_time.dt.day_name().str[:3] + +plt.figure(figsize = (6,4)) +sns.boxplot(data = df_demand_weekday.groupby("date_time", + as_index = False).first().sort_values("weekday"), + x = 'weekday_name', y = "total_demand", + hue = 'weekday', palette = 'Blues', showfliers = False, + legend = False) +plt.grid(alpha = 0.5) +plt.xlabel('Day of the Week') +plt.ylabel('Total Demand (MW)') +plt.show() +``` + + +### Figure \@ref(fig:hourdemand) {.unlisted .unnumbered} + + +``` python +df_hour = df_all.copy() +df_hour["date_time_future_hour"] = df_hour.date_time_future.dt.hour +df_hour = df_hour.sort_values("date_time_future_hour") + + + +plt.figure(figsize = (12,6)) +sns.boxplot(data = df_hour.groupby( + "date_time_future", as_index = False).first().sort_values( + "date_time_future_hour"), + x="date_time_future_hour", y = "total_demand", + palette = 'Blues', showfliers = False) +plt.grid(alpha = 0.5) +plt.title("Hour vs Total Demand"); + +time_decomposition_error_plots(df = df_hour, + x = "date_time_future_hour", time_interval = "Hour", + show_outliers = False, forecast_interval = 12, + show_relative_error_all = True, + show_relative_error_interval = True) +``` + + +### Figure \@ref(fig:forecastdem) {.unlisted .unnumbered} + + +``` python +forecast_df['forecast_hours'] = (forecast_df['DATETIME'] - + forecast_df['LASTCHANGED']).dt.total_seconds() / 3600 + + +merged_df = forecast_df.merge( + actual_df, + on=['DATETIME'], + how='inner' +) + + +hours = [6, 12, 18, 24] +dfs = { + h: merged_df[ + merged_df['forecast_hours'].round() == h].sample(n=3000, + random_state=42) + for h in hours +} + + +fig, axes = plt.subplots(2, 2, figsize=(8, 6)) +for ax, h in zip(axes.flat, hours): + sns.regplot( + data=dfs[h], + x='TOTALDEMAND', + y='FORECASTDEMAND', + line_kws={'color': 'red'}, + ax=ax + ) + ax.set_title(f'{h}-Hour Ahead Forecast') + ax.set_xlabel('Actual Demand') + ax.set_ylabel('Forecasted Demand') + ax.axhline(y = 9000, + color = 'green') +plt.legend(['Correlation points', 'Trendline','', + 'Forecasted = 9000']) +plt.tight_layout() +plt.show() +``` + +### Figure \@ref(fig:weatherdemand) {.unlisted .unnumbered} + + +``` python +df_corr_demand = df_all[['total_demand', + 'temperature_future_forecast','humidity_future_forecast', + 'rain_future_forecast','wind_speed_future_forecast']] + +df_corr_demand = df_corr_demand.rename( + columns={'temperature_future_forecast': 'Temperature Forecast', + 'humidity_future_forecast': 'Humidity Forecast', + 'rain_future_forecast': 'Rain Forecast', + 'wind_speed_future_forecast': 'Wind Speed Forecast'}) + +correlation_demand = df_corr_demand.corr() +correlationsD = correlation_demand['total_demand'].drop('total_demand') + +plt.figure(figsize=(10, 6)) +correlationsD.sort_values().plot(kind='barh', + color=plt.cm.coolwarm(np.abs(correlationsD)/max(abs(correlationsD)))) +plt.xlabel('Correlation Coefficient') +plt.axvline(x=0, color='k', linestyle='-', alpha=0.3) +plt.grid(axis='x', alpha=0.3) +plt.tight_layout() +plt.show() +``` + + +### Figure \@ref(fig:tempcorr) {.unlisted .unnumbered} + + +``` python +hourly_temp = temperature_df.groupby( + ['HOUR', 'LOCATION'])['TEMPERATURE'].mean().reset_index() +print(f"Aggregated temperature data: {len(hourly_temp)} rows") + +merged_df = pd.merge( + demand_df, + hourly_temp, + left_on='HOUR', + right_on='HOUR', + how='inner' +) + + +plt.figure(figsize=(10, 6)) +plt.scatter(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], + alpha=0.5) +plt.title('Relationship between Temperature and Electricity Demand') +plt.xlabel('Temperature (°C)') +plt.ylabel('Total Demand (MW)') +plt.grid(True, alpha=0.3) + +# Add trend line +z = np.polyfit(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], 2) +p = np.poly1d(z) +temp_range = np.linspace(merged_df['TEMPERATURE'].min(), + merged_df['TEMPERATURE'].max(), 100) +plt.plot(temp_range, p(temp_range), "r--", linewidth=2) + +plt.savefig('temperature_vs_demand_scatter.png') +``` + + +### Figure \@ref(fig:errorvtemp) {.unlisted .unnumbered} + + +``` python +interval = 60*60 #sets the interval in seconds +df_forecast["forecast_interval"] = df_forecast.date_time_prediction - + df_forecast.date_time_forecast +df_forecast.forecast_interval = df_forecast.forecast_interval.apply( + lambda x: x.total_seconds()/interval) + + +interval_min, interval_max = 23 , 25 #sets a window for forecast periods +df_forecast_near24hour = + df_forecast.loc[(df_forecast.forecast_interval > interval_min) & + (df_forecast.forecast_interval < interval_max)] +df_forecast_near24hour["date_time_forecast_rounded"] = + df_forecast_near24hour.date_time_forecast.apply( + lambda x: x.round(freq='30min')) +df_forecast_near24hour_1instance = + df_forecast_near24hour.loc[ + df_forecast_near24hour.groupby( + "date_time_forecast_rounded")["forecast_interval"].idxmax()] + + +df_forecast_near24hour_1instance_with_demand = + pd.merge(df_forecast_near24hour_1instance, + df_demand, left_on = "date_time_forecast_rounded", + right_on = "date_time") +df_forecast_near24hour_1instance_with_demand["forecast_error"] = + df_forecast_near24hour_1instance_with_demand.total_demand - + df_forecast_near24hour_1instance_with_demand.forecast_demand + +df_forecast_near24hour_1instance_with_demand_temperature = + pd.merge(df_forecast_near24hour_1instance_with_demand, + df_temperature, left_on = "date_time_forecast_rounded", + right_on = "date_time") +df_forecast_near24hour_1instance_with_demand_temperature[ + "forecast_error_relative"] = + df_forecast_near24hour_1instance_with_demand_temperature.forecast_error/ + df_forecast_near24hour_1instance_with_demand_temperature.total_demand + +df_plot = df_forecast_near24hour_1instance_with_demand_temperature[[ + "temperature", "forecast_error", "forecast_error_relative"]].copy() +df_plot.temperature = df_plot.temperature.round() + +plt.figure(figsize = (12,7)) +sns.boxplot(data=df_plot, x="temperature", y="forecast_error", + fliersize = 1) +plt.axhline(0, color='r', alpha = 0.2) +plt.xticks(rotation = 90); +plt.title("Accuracy of forecasting 24h into the future") +``` + + +### Figure \@ref(fig:relerrorvtemp) {.unlisted .unnumbered} + + +``` python +plt.figure(figsize = (12,7)) +sns.boxplot(data=df_plot, x="temperature", + y="forecast_error_relative", fliersize = 1) +plt.axhline(0, color='r', alpha = 0.2) +plt.xticks(rotation = 90); +plt.ylabel("Forecast Error as Portion of Actual Demand") +``` + + +### Figure \@ref(fig:acfErrors) \& Figure \@ref(fig:pacfErrors) {.unlisted .unnumbered} + + +``` python +for i, delta in enumerate([12, 24, 36, 48]): + df_all_delta = df_all.loc[ + df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) + delta_24h_later = 48 - delta + previous_lag = 48 + + x = df_all_delta.forecast_error_relative[ + previous_lag:len(df_all_delta)] + y = df_all_delta.forecast_error_relative[ + 0:len(df_all_delta)-previous_lag] + +def check_stationarity(series): + + result = adfuller(series.values) + + print('ADF Statistic: %f' % result[0]) + print('p-value: %f' % result[1]) + print('Critical Values:') + for key, value in result[4].items(): + print('\t%s: %.3f' % (key, value)) + + if (result[1] <= 0.05) & (result[4]['5%'] > result[0]): + print("\u001b[32mStationary\u001b[0m") + else: + print("\x1b[31mNon-stationary\x1b[0m") + +fig1, ax1 = plt.subplots(2,2, figsize = (18, 18)) +fig2, ax2 = plt.subplots(2,2, figsize = (18, 18)) + +i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]} + +for i, period_id in enumerate([12, 24, 36, 48]): + print(f"Forecast Interval = {round(period_id/2)}") + + df_all_delta = df_all.loc[df_all.period_id == + period_id].sort_values( + "date_time_future").reset_index(drop = True) + + check_stationarity(df_all_delta.forecast_error_relative) + + plot_acf(df_all_delta.forecast_error_relative, lags = 100, + ax = ax1[i_subplot[i][0]][i_subplot[i][1]]) + ax1[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Forecast Interval = + {round(period_id/2)}h') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_ylim(0,1) + + plot_pacf(df_all_delta.forecast_error_relative, lags = 100, + ax = ax2[i_subplot[i][0]][i_subplot[i][1]]) + ax2[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Forecast Interval = + {round(period_id/2)}h') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_ylim(-0.5,1) + +#fig1.suptitle('Autocorrelation') +#fig2.suptitle('Partial Autocorrelation') +plt.show() +``` + + +### Figure \@ref(fig:laggedError) {.unlisted .unnumbered} + + +``` python +delta = 24 + +df_all_delta = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) + +x = df_all_delta.forecast_error_relative[delta:len(df_all_delta)] +y = df_all_delta.forecast_error_relative[0:len(df_all_delta)-delta] + +plt.subplots(2,2, figsize = (18, 18)) + +for i, delta in enumerate([12, 24, 36, 48]): + df_all_delta = df_all.loc[df_all.period_id == delta].sort_values( + "date_time_future").reset_index(drop = True) + delta_24h_later = 48 - delta + previous_lag = 48 + + x = df_all_delta.forecast_error_relative[ + previous_lag:len(df_all_delta)] + y = df_all_delta.forecast_error_relative[ + 0:len(df_all_delta)-previous_lag] + + #plt.figure(figsize = (12, 9)) + plt.subplot(2,2,i+1) + plt.plot(np.array(x), np.array(y), '.', alpha = 0.3) + #plt.plot(0,0, 'r.') + plt.xlim(-0.15, 0.15) + plt.ylim(-0.15, 0.15) + plt.grid(alpha = 0.5) + plt.xlabel('Relative Forecast Error at Time = t') + plt.ylabel('Relative Forecast Error at Time = t - 24h') +``` + + +### Figure \@ref(fig:ACF) \& Figure \@ref(fig:PACF) {.unlisted .unnumbered} + + +``` python +df_all["forecast_error_relative"] = + df_all.forecast_error/df_all.total_demand + +df_all["date_time_future_month"] = df_all.date_time_future.dt.month +df_all["date_time_future_year"] = df_all.date_time_future.dt.year +df_all["date_time_future_weekday"] = df_all.date_time_future.dt.dayofweek +df_all["date_time_future_yearTime"] = + df_all.date_time_future_year.apply( + lambda x: pd.DateOffset(years=x-2000)) +df_all["date_time_future_hour"] = df_all.date_time_future.dt.hour + +def check_stationarity(series): + + result = adfuller(series.values) + + print('ADF Statistic: %f' % result[0]) + print('p-value: %f' % result[1]) + print('Critical Values:') + for key, value in result[4].items(): + print('\t%s: %.3f' % (key, value)) + + if (result[1] <= 0.05) & (result[4]['5%'] > result[0]): + print("\u001b[32mStationary\u001b[0m") + else: + print("\x1b[31mNon-stationary\x1b[0m") + + +period_id = 24 + +fig1, ax1 = plt.subplots(2,2, figsize = (18, 10)) +fig2, ax2 = plt.subplots(2,2, figsize = (18, 10)) + +i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]} + +for i, hour_of_day in enumerate([4, 10, 16, 22]): + print(f"Hour of Day = {hour_of_day}") + df_all_delta = df_all.loc[(df_all.period_id == period_id) & + (df_all.date_time_future_hour == hour_of_day) & + (df_all.date_time_future.dt.minute == 0)].sort_values( + "date_time_future").reset_index(drop = True) + + check_stationarity(df_all_delta.forecast_error_relative) + + plot_acf(df_all_delta.forecast_error_relative, lags = 28, + ax = ax1[i_subplot[i][0]][i_subplot[i][1]]) + ax1[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_title( + f'Hour of Day = {hour_of_day}') + ax1[i_subplot[i][0]][i_subplot[i][1]].set_ylim(0,1) + + plot_pacf(df_all_delta.forecast_error_relative, + lags = 28, ax = ax2[i_subplot[i][0]][i_subplot[i][1]]) + ax2[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_title( + f'Hour of Day = {hour_of_day}') + ax2[i_subplot[i][0]][i_subplot[i][1]].set_ylim(-0.1,1) +``` + + +### Figure \@ref(fig:MSEsarima) \& Figure \@ref(fig:MAPEsarima){.unlisted .unnumbered} + + +``` python +sarima_tune["mse_improvement"] = + round(100*(sarima_tune.mse_pre - + sarima_tune.mse_post)/sarima_tune.mse_pre) +sarima_tune = pd.merge(sarima_tune, sarimas, + on = ["order", "seasonal_order"], + how = "left").sort_values("id") + +plot = sarima_tune.groupby( + ["order", "seasonal_order", "hour_of_day"], + as_index = False).mean() +sns.lineplot(data = plot, x = 'hour_of_day', y = 'mape', + hue = 'id', palette = 'pastel', alpha = 1, linestyle = '--') +``` + + +### Figure \@ref(fig:ForestModel1) {.unlisted .unnumbered} + + +``` python +sample = np.random.choice(len(y_test), 100, replace=False) +x_axis = range(len(sample)) + +plt.figure(figsize=(14, 6)) +sns.lineplot(x=x_axis, y=y_test.iloc[sample], + label='Actual Demand', color='black') +sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], + label='Original Forecast', linestyle='--') +sns.lineplot(x=x_axis, y=y_pred[sample], + label='Model Predictions', linestyle='--') +plt.title("Model vs Original Forecast Performance") +plt.ylabel("Demand") +plt.show() +``` + +### Figure \@ref(fig:ForestModel2) {.unlisted .unnumbered} + + +``` python +sample = np.random.choice(len(y_test), 100, replace=False) +x_axis = range(len(sample)) + +plt.figure(figsize=(14, 6)) +sns.lineplot(x=x_axis, y=y_test.iloc[sample], + label='Actual Demand', color='black') +sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], + label='Original Forecast', linestyle='--') +sns.lineplot(x=x_axis, y=y_pred[sample], + label='Model Predictions', linestyle='--') +plt.title("Model vs Original Forecast Performance") +plt.ylabel("Demand") +plt.show() +``` + +### Figure \@ref(fig:MAPEXGBoost) {.unlisted .unnumbered} + + +``` python +output_df = test_df.copy() +output_df["xgb_prediction"] = y_pred + +export_cols = ["date_time_future", "total_demand", + "forecast_demand", "xgb_prediction"] +output_df[export_cols].to_csv("finalresultsxgboost.csv", index=False) + + +# Calculate absolute percentage error per row +output_df["abs_pct_error"] = np.abs((output_df["total_demand"] - + output_df["xgb_prediction"]) / output_df["total_demand"]) * 100 + +# Extract hour from datetime +output_df["Hour"] = pd.to_datetime(output_df["date_time_future"]).dt.hour + +# Group by hour and calculate mean error +hourly_error = + output_df.groupby("Hour")["abs_pct_error"].mean().reset_index() + +# Plot +plt.figure(figsize=(10, 5)) +plt.plot(hourly_error["Hour"], hourly_error["abs_pct_error"], marker='o') +plt.title("MAPE by Hour of Day") +plt.xlabel("Hour of Day") +plt.ylabel("MAPE") +plt.grid(True) +plt.xticks(range(0, 24)) +plt.tight_layout() +plt.show() +``` + + +### Figure \@ref(fig:Cpmparison) {.unlisted .unnumbered} + + +``` python +lm_results = pd.read_csv("data/results_LM.csv") +lm_results["forecast_error"] = lm_results.total_demand - + lm_results.lm_prediction + +sarima_results = pd.read_csv("data/results_SARIMA.csv") +sarima_results["forecast_error"] = sarima_results.total_demand - + sarima_results.sarima_prediction + +xgboost_results = pd.read_csv("data/results_XGBoost.csv") +xgboost_results["forecast_error"] = xgboost_results.total_demand - + xgboost_results.xgb_prediction + +decisionT_results = pd.read_csv('data/results_DecisionTree.csv') +decisionT_results["forecast_error"] = + decisionT_results.total_demand - decisionT_results.model_prediction + +results_all = {"Linear": lm_results, + "SARIMA": sarima_results, + "XGBoost": xgboost_results, + "Decision Tree": decisionT_results} + +colors = ['#1f77b4', '#ff7f0e', 'g', '#7f7f7f'] + +plt.subplots(1, 2, figsize = (16,7)) + +plt.subplot(1,2,1) +for i, model in enumerate(results_all): + model_result = results_all[model] + sns.kdeplot(model_result.forecast_error, label = model, + color = colors[i], alpha = 0.8) + +sns.kdeplot(lm_results.total_demand - + lm_results.forecast_demand, label = "AEMO", color = 'r', ls = '--') +plt.xlim(-1200, 1200); +plt.ylim(0, 0.0025) +plt.xlabel('Forecast Error') +plt.grid() +plt.legend() +plt.title("Distribution of Error"); + +plt.subplot(1,2,2) +for i, model in enumerate(results_all): + model_result = results_all[model] + sns.kdeplot(abs(model_result.forecast_error), + label = model, color = colors[i], alpha = 0.8) + +sns.kdeplot(abs(lm_results.total_demand - + lm_results.forecast_demand), label = "AEMO", color = 'r', + ls = '--') +plt.xlim(0, 1000); +plt.ylim(0, 0.0046) +plt.grid() +plt.legend() +plt.xlabel('abs(Forecast Error)') +plt.title("Distribution of Absolute Error"); +``` + diff --git a/report/Group-D-Report-Final.pdf b/report/Group-D-Report-Final.pdf new file mode 100644 index 000000000..d7e70b78e Binary files /dev/null and b/report/Group-D-Report-Final.pdf differ diff --git a/report/Group-D-Report-Final.tex b/report/Group-D-Report-Final.tex new file mode 100644 index 000000000..accb70426 --- /dev/null +++ b/report/Group-D-Report-Final.tex @@ -0,0 +1,2870 @@ +\documentclass[mstat,12pt]{unswthesis} + +\usepackage{color} +\usepackage{fancyvrb} +\newcommand{\VerbBar}{|} +\newcommand{\VERB}{\Verb[commandchars=\\\{\}]} +\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}} +% Add ',fontsize=\small' for more characters per line +\usepackage{framed} +\definecolor{shadecolor}{RGB}{248,248,248} +\newenvironment{Shaded}{\begin{snugshade}}{\end{snugshade}} +\newcommand{\AlertTok}[1]{\textcolor[rgb]{0.94,0.16,0.16}{#1}} +\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textbf{\textit{#1}}}} +\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.13,0.29,0.53}{#1}} +\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.00,0.00,0.81}{#1}} +\newcommand{\BuiltInTok}[1]{#1} +\newcommand{\CharTok}[1]{\textcolor[rgb]{0.31,0.60,0.02}{#1}} +\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textit{#1}}} +\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textbf{\textit{#1}}}} +\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{#1}} +\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.13,0.29,0.53}{\textbf{#1}}} +\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.13,0.29,0.53}{#1}} +\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.00,0.00,0.81}{#1}} +\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textbf{\textit{#1}}}} +\newcommand{\ErrorTok}[1]{\textcolor[rgb]{0.64,0.00,0.00}{\textbf{#1}}} +\newcommand{\ExtensionTok}[1]{#1} +\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.00,0.00,0.81}{#1}} +\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.13,0.29,0.53}{\textbf{#1}}} +\newcommand{\ImportTok}[1]{#1} +\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textbf{\textit{#1}}}} +\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.13,0.29,0.53}{\textbf{#1}}} +\newcommand{\NormalTok}[1]{#1} +\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.81,0.36,0.00}{\textbf{#1}}} +\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{#1}} +\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textit{#1}}} +\newcommand{\RegionMarkerTok}[1]{#1} +\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.81,0.36,0.00}{\textbf{#1}}} +\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.31,0.60,0.02}{#1}} +\newcommand{\StringTok}[1]{\textcolor[rgb]{0.31,0.60,0.02}{#1}} +\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.00,0.00,0.00}{#1}} +\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.31,0.60,0.02}{#1}} +\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.56,0.35,0.01}{\textbf{\textit{#1}}}} + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% OK...Now we get to some actual input. The first part sets up +% the title etc that will appear on the front page +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\title{Hybrid Model Approaches to Improve Short-Term Energy Demand Forecasts in New South Wales, Australia} + +\authornameonly{David Valido Ramos (z5516338), Katelyn Kemp (z5459347), Nick Mutton (z5549371), Senarath Seelanatha (z5595581), Shanjay Perinpanathan (z5339723), Waseem Alashqar (z5514810). } + +\author{\Authornameonly} + +\copyrightfalse +\figurespagefalse +\tablespagefalse + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% And now the document begins +% The \beforepreface and \afterpreface commands puts the +% contents page etc in +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +\input{header.tex} + + +\begin{document} + +\beforepreface + +\prefacesection{Abstract} + +Electricity demand forecasting is a difficult problem every country faces. In this paper, we attempt to utilise the concept of hybrid models to improve the energy forecast of AEMO, the Australian body that manages power systems and markets, to predict energy demand in NSW. It was found that historical forecast errors and weather variables had some correlation with forecasting errors, therefore were included in the models. SARIMA, Random Forest, and XGBoost models were tested to determine the best fit for correcting AEMO forecasting bias and reducing overall energy demand forecast error. We argue the hybrid modification enables us to correctly factor relationships not supported by AEMO's original model. All hybrid models tested provided some reduction in the overall forecast error and supported the hybrid model process. + +\afterpreface + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% Now we can start on the first chapter +% Within chapters we have sections, subsections and so forth +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%\afterpage{\blankpage} + + +\chapter{Introduction}\label{introduction} + +Energy forecasts play a crucial role in planning and maintaining the energy sector. Ensuring forecast accuracy helps to manage imbalances in energy production and consumption, reduce power system costs, and improve operational safety (Mystakidis et al., 2024). Energy demand management is also linked with self sufficiency and cost effectiveness that facilitate sustainable economic development (Suganthi and Samiel, 2012). Energy forecasting therefore has a broad impact on a wide variety of stakeholders including residential customers, power generators, retailers, traders, industrial and commercial customers, system operators, and financial investors (Ghalehkhondabi et al., 2016). + +\bigskip + +There are many risks in inaccurate energy forecasting. Over forecasting has cost and resource implications for providers, as well as environmental impacts. Under forecasting can cause outages, as well as having down the stream increased costs from inconsistent supply (Suganthi and Samuel, 2012). Shortages are also linked to political instability (Rakpho and Yamaka, 2021). + +\bigskip + +The Australian Energy Market Operator (AEMO) is responsible for managing Australia's electricity and gas systems and markets to ensure Australians have access to reliable, affordable and secure energy. AEMO performs a wide range of functions, however one of their key roles is to balance electricity supply and demand through dispatching electricity generation based on forecasts updated every 5 minutes. It is therefore critical that their electricity demand forecasting is accurate to reduce the risks associated with over, or under supply of electricity to the market. While the AEMO short-term electricity forecast is generally quite accurate, it is valuable to understand where the forecast may be underperforming to consider how accuracy could be improved. + +\bigskip + +\textbf{The goal of this report is to identify variables that may contribute to errors in AEMO's electricity demand forecasts, with the aim of using these insights to improve forecast accuracy.} + +\bigskip + +The report will consider a 12-hour interval with a step-ahead forecast. This is considered a `pre-dispatch' interval based on AEMO's definitions and is important for operational planning, therefore its accuracy is critical. + +\clearpage + +\chapter{Literature Review}\label{literature-review} + +\section{Forecasting electricity demand background}\label{forecasting-electricity-demand-background} + +In today's context of near-constant energy consumption, the task of energy forecasting has become increasingly complex. Given the absence of a universally applicable forecasting method, the selection of an appropriate technique is typically guided by the nature of the available data and the specific objectives of the forecasting exercise (Pinheiro, Madeira, \& Francisco, 2023). Additionally, the forecast interval, which often reflects the purpose of the forecast, plays a large role in determining the suitability of different modeling approaches. + +\bigskip + +Forecasting models are typically categorised into short-, medium-, and long-term, and while there is not a unanimous definition of what constitutes these time periods, researchers generally agree that short-term is a few minutes up to a few days (Ahmad and Chen, 2018) or two weeks (Klyuev et al., 2022), medium-term as one month to one year, and long-term as one year to ten years (Ahmad and Chen, 2018). AEMO defines its short term forecast as up to 7 days ahead (AEMO, 2023). + +\bigskip + +Short-term intervals tend to require the greatest accuracy as they support a wide variety of operational planning, or network management activities including scheduling, planning of power generation, cost optimisation and guaranteeing continuous electricity supply (Sanhudo, Rodrigues and Filho, 2021). Short-term forecast methods can be broadly categorised into two categories -- mathematical algorithms such as time-series analysis and logistic regression, and artificial intelligence (AI) algorithms such as machine learning, deep learning and ensemble learning models (Deng et al., 2022). For short-term forecasting, AI methods are becoming more popular as they can consider the non-linear nature of power demand. Short term forecasting is also generally more interested in the accuracy of the forecast rather than the interpretability of the results which makes these `black box' approaches appropriate (Phyo and Byun, 2021). Other studies have found that machine learning models tend to outperform traditional models such as ARIMA in short-term forecasting (Divina et al., 2019). + +\bigskip + +Medium- and long-term forecasting supports the planning and maintenance of the electrical network such as smart grid eco-systems (Ahmad \& Chen, 2018). Furthermore, long-term forecasting is more strategic and is necessary for the development of energy systems, planning capital construction at production or infrastructure facilities (Klyuev et al., 2022). These forecast intervals typically use econometric models, system dynamics, and grey prediction, with a focus on policy adjustments, economic indicators (such as GDP and CPI), and population trends (Koukaras et al., 2024). + +\section{Weather in forecasting electricity demand}\label{weather-in-forecasting-electricity-demand} + +Temperature is a primary driver of electricity demand, shaping heating and cooling loads that dictate energy consumption. Research consistently identifies it as the dominant weather factor in electricity demand prediction, especially during peak periods. Liu et al.~(2021) demonstrate that extreme temperatures lead to increased residential electricity consumption, finding that for each additional day in which the mean temperature exceeds 30 °C, there is an 16.8\% increase in monthly residential electricity consumption. Similarly, for each additional day below -6 °C there is a 6\% increase in monthly residential electricity consumption. This underscores temperature's critical role in accurate demand forecasting, as it directly influences consumption patterns. + +\bigskip + +Extreme temperatures can lead to significant errors in electricity demand forecasts, often underestimating demand. During Winter Storm Uri in Texas in February 2021 (Añel, 2024), minimum extreme cold temperatures of --34 °C and high winds of 260 km/h impacted 170 million people. Due to this extreme weather event, electricity demand unexpectedly increased from 40 GW to over 70 GW, resulting in blackouts that affected more than 4 million people. The economic cost of the power outages and disruption has been estimated between 26.1 and 130 billion U.S. dollars. + +\bigskip + +Other weather variables, particularly humidity and ``feels like'' temperature, enhance forecasting accuracy. Maia-Silva et al.~(2020) found that using humidity-related measures, such as dew point and heat index, improves prediction accuracy, especially in high-energy-consuming regions, with improvements up to 8-9\%. This highlights the need to consider composite weather indices, as air temperature alone underestimates demand. + +\section{Historical Forecasting Error Incorporation}\label{historical-forecasting-error-incorporation} + +Besides temperature and other weather components, historical measurements of energy demand or forecasted energy demand are highly reliable factors for predicting future energy demand (Singh and Yassine, 2018). Historical energy demand is important for capturing seasonal effects in different time horizons (day, week, month, season etc). However, historical energy forecasts (and by extension their differentials) are valuable because, in addition to seasonal effects, they capture bias and allow for corrections to the future forecast. Historical forecast factors are so influential that there is evidence that it can create reasonable forecasts without additional weather variables (Boroojeni et al., 2017). + +\section{Modelling electricity demand}\label{modelling-electricity-demand} + +As previously stated, short-term energy models can be effectively categorised into two groups: classical statistical techniques, and machine learning or AI techniques. Traditional statistical and econometric models tend to be explainable and interpretable. While often less accurate, these models are widely used in energy demand forecasting and include methods such as regression (Papalexopoulos and Hesterberg, 1990) (Ertuğrul, Tekin and Tekin, 2020) and time-series such as ARIMA (Tarmanini et al., 2023) (Ediger and Akar, 2007). They also have natural extrapolations to medium-to-long term models, that are also econometric-based due to their relationship with longitudinal factors such as policy changes, modifications to the energy grid, or economic factors (such as GDP and population) (Ardakani and Ardehali, 2014). While a machine learning model, decision tree methods also provide interpretability in energy demand forecasting (Kopyt et al., 2024) (Wang et al., 2018). + +\bigskip + +Black box machine learning models provide a greater focus on model accuracy rather than interpretability. Some common models used in energy demand forecasting include Neural Networks (Manno, Martelli and Amaldi, 2022) (Kuo and Huang, 2018) (Pao, 2009), Support Vector Machines (Ahmad et al., 2014) (Ahmad et al., 2020), and ensemble methods, such as Random Forests (Divina et al., 2019) and XGBoost (Abbasi et al., 2019). + +\bigskip + +Divina et al.~(2019) studied short-term energy consumption forecasting in smart buildings using several models such as linear regression, auto-regressive integrated moving average (ARIMA), artificial Neural Networks (ANNs) and ensemble methods such as random forests (RF) and extreme gradient boosting (XGBoost). They measured the performance of these models using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). They found that the best performing models were machine based approaches, and more so ensemble methods such as RF, GBM and XGBoost. On the other hand, ARIMA was the worst performing method that was tested. Further, the optimal historical window was found to be 10 days where accuracy improves up to this point, but does not improve much beyond this. Tarmanini et al.~(2023) considered ARIMA and Artificial neural network (ANN) models to forecast daily electricity load in Ireland. The study found that both ARIMA and ANN produced more error in winter than in other seasons. Despite this, the ANN method performed better in terms of accuracy due to it better coping with non-linear data, but suggest a hybrid approach may provide more accurate results. + +\bigskip + +Other studies have also concluded that the best performing models tend to be hybrid models which use a combination of explainable and/or black-box methods, such as NN--ARIMA or CNN-LTSM due to their stability and potential to reduce overfitting (Deng et al.~2022). For example, Suganthi and Samuel (2012) compared various approaches to energy demand forecasting and found often hybrid approaches such as linking ARIMA models with neural networks often produce more accurate results. The superiority of hybrid models for energy forecasts are due to corrections of the original forecast output in the second modelling component (Savić, Selakov and Milošević, 2014). + +\bigskip + +One important method used in hybrid modelling is residual error forecasting. Andronikos, Tzelepi and Tefas (2023) proposed a residual error learning methodology for electricity demand forecasting which involved training a model on actual load values, then calculating the residual errors which would subsequently be used as targets to train a second model. The final prediction of forecast load would then be the sum of the first model's prediction and the second model's prediction. The authors found that if the errors have an underlying structure, the residual error forecasting method will improve forecasting accuracy. + +\bigskip + +A common method used in many hybrid studies which also aligns closely with modelling residuals is to decompose time series data into trend and residual components and model these components separately with appropriate methods. The forecasts from each component are then summed together for the final forecast. Amara et al.~(2019) used decomposition to extract the temperature-related component that makes up electricity demand and then analysed and forecasted the residual component. The two forecasts were then summed to produce the final forecast. This allowed for understanding of periodicity in the residuals and to improve the overall forecast accuracy. Zhang et al (2022) considered the Australian electricity market in their study using a decomposition-hybrid approach. They first extracted a trend component from the original electricity load, then obtained the nonlinear component by subtracting the trend component from the original electricity load. The two components were forecast separately and then added together to make up the final forecast. Their proposed model improved the forecasting accuracy against all comparison models. Another approach to hybrid modelling considered by Pao (2009) was a two step approach where a linear model was built and the results of this inputted into a neural network model to capture both linear and non-linear relationships in the data. This showed to produce superior predictions to a linear model alone. + +\bigskip + +The approach proposed in this report is based on a hybrid approach where the AEMO forecast will act as the initial model and a new model will be built considering the errors from that model in an attempt to improve the overall forecast accuracy. + +\section{AEMO forecasting methodology}\label{aemo-forecasting-methodology} + +The AEMO load Forecasting Methodology (AEMO, 2023) details the organisation's approach to forecasting electricity demand. With particular relevance to this report, AEMO pre-dispatch forecasts are short-term electricity demand forecasts that include intervals up to 40 hours. One of the important uses of pre-dispatch forecasting is to support operational planning that ensures electricity reliability and security of the network. The key inputs into the forecast include: + +\begin{itemize} +\item + Historical demand (such as recent load patterns) +\item + Weather forecast variables (particularly those that describe the temperature profile) +\item + Calendar variables (e.g.~weekday or weekend, public or school holiday, daylight savings) +\item + Solar and wind generation forecasts. +\end{itemize} + +\bigskip + +There is very little manual intervention for these forecasts, with AEMO's Demand Forecasting System (DFS) generating forecasts automatically through a combination of statistical and machine learning models, every half hour. + +\bigskip + +The pre-dispatch load forecasting error threshold for NSW is 150 MW based on historical peak demand for NSW and previous forecasting performance. The load forecast is reviewed whenever the forecast error is greater than the threshold for two consecutive 30-minute periods, therefore at an overall level the forecast is already quite accurate. + +\chapter{Material and Methods}\label{material-and-methods} + +Figure \ref{fig:modelDiagram} shows the overall structure of this project designed to address the research question. The process began with data collection and pre-processing, including calculating the forecast error. Exploratory data analysis was conducted to understand relationships between different variables and the forecast error. The data were then split into training and testing samples for models to be built, fine-tuned and compared. The remaining sections of this report detail the steps undertaken in the modelling as well as analysis and presentation of the results. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MaterialMethods} \caption{Project structure followed to address the research question}\label{fig:modelDiagram} +\end{figure} + +\section{Software}\label{software} + +Python was the primary software used for data analysis and modelling based on its flexibility in data visualisations and ability to execute machine learning models. + +\bigskip + +To ensure reproducibility, RMarkdown was used to prepare the final report. Power BI was also used in initial data exploration to understand high-level trends in the data. A Github repository was used to store data, code and working documents. The repository can be found here: \url{https://github.com/unswnick/project}. All relevant code for this project can be found in the Appendices. Appendix A contains data processing code, Appendix B contains Modelling code, and Appendix C contains code for plots. + +\bigskip + +A summary of software used as part of the project is summarised in Table \ref{tab:tab1}. +\bigskip + +\begin{table}[H] +\caption{Summary of software used} +\begin{center} +\begin{tabular}{|l|l|p{19em}|} +\hline +\textbf{Software} & \textbf{Library(s)} & \textbf{Purpose} \\ +\hline +\multirow{4}{4em}{Python} & Pandas & Reading, manipulating, cleaning and analysing datasets. \\ +\cline{2-3} +& Numpy & Manipulating data and mathematical \newline calculations. \\ +\cline{2-3} +& Matplotlib, Seaborn & Visualising data to understand trends and \newline patterns. \\ +\cline{2-3} +& Scikit-learn & Implementing and evaluating machine learning algorithms. \\ +\hline +PowerBI & - & Summarising and visualising data. \\ +\hline +RMarkdown & - & Writing final report. \\ +\hline +Github & - & Repository for project documents \\ +\hline +\end{tabular} +\end{center} +\label{tab:tab1} +\end{table} + +\section{Description of the Data}\label{description-of-the-data} + +Table \ref{tab:tab2} describes the data that was used in analysis. In addition to the data files provided by the client, historical weather forecasts including temperature, humidity and wind speed were sourced from OpenWeather (OpenWeather, 2025), a global company specialising in environmental data products. Forecast weather data rather than actual weather data was used as an input to ensure the forecast models were realistic. + +\begin{table}[H] +\caption{Datasets used in this project and their properties} +\centering +\begin{tabular}{|p{13em}|p{20em}|} +\hline +\textbf{Data} & \textbf{Description} \\ +\hline +\textbf{Electricity demand}\newline Use for both training and testing models. & Electricity demand from 2010 to 2021. Well-structured and low complexity with no duplicates and no null values.\newline Variables: Date-time, totalDemand, regionID\newline +Format: CSV, Storage: Github, Size: 6 Mb, Rows: 196,513 \\ +\hline +\textbf{Forecast demand} \newline Used as a baseline forecast model and improve on. & Provides forecasted demand data from 2010 to 2021. Well-structured with no null values. It is high complexity due to uneven time increments and duplicate rows. \newline Variables: Date-time, forecastDemand, totalDemand, regionID, preDispatchSeqNo, periodID, lastChange\newline Format: CSV, Storage: Github, Size: 722 Mb, Rows: 10,906,019 \\ +\hline +\textbf{Forecast weather indicators}\newline Exogenous variables included in modelling. & Provides previous forecast weather data for Bankstown from October 7 2017. Well-structured with no null values. \newline Variables: Date-time, temperature, humidity, wind speed, rain\newline Format: CSV, Storage: GitHub, Sharepoint/Teams, Size: 1327.1 MB, Rows: 10854100 \\ +\hline +\end{tabular} +\label{tab:tab2} +\end{table} + +\section{Data Cleaning}\label{data-cleaning} + +Data was found to be complete for Electricity Demand data Some forecast data were missing for forecast intervals \textgreater12 hours. To ensure complete data was used, and to reduce computational complexity, the forecast models were trained and tested on 12 hour forecast intervals only. There were no missing values in the Forecast Weather Indicators data, however the available data begins on October 7 2017. Consequently, the relevant data used to train and test the forecast model was between October 7 2017 and 17 March 2021 with a 12 hour forecast interval. No further missing values were present in the data. + +\bigskip + +Outliers were not removed from the data to ensure data completeness and to avoid introducing bias through exclusions. Further advice from industry experts would be required to determine which outliers, if any, should be removed based on appropriate criteria. + +\bigskip + +Additional data cleaning steps performed on all datasets are detailed below: + +\begin{enumerate} +\def\labelenumi{\arabic{enumi}.} +\item + Date/time variables were formatted consistently (i.e.~d/m/y H:M) +\item + Date/time variables were rounded to the nearest 30 minute increment to provide consistent 30-minute intervals +\item + Duplicate date/time rows were removed to ensure each date/time row was unique +\end{enumerate} + +\bigskip + +After each dataset was cleaned and checked, they were merged into one clean dataset, joined on the unique date/time variable. + +\section{Data pre-processing}\label{data-pre-processing} + +Outlined below are the steps undertaken to pre-process the data: + +\begin{enumerate} +\def\labelenumi{\arabic{enumi}.} +\item + \textbf{Feature extraction} -- The following features were extracted from date/time variables: + + \begin{itemize} + \tightlist + \item + Hour\_of\_day + \item + Month\_of\_year + \item + Day\_of\_week + \end{itemize} +\item + \textbf{Label enconding} -- Hour\_of\_day, Day\_of\_week and Month\_of\_year variables were one-hot-encoded into binary variables +\item + \textbf{Feature engineering} -- The following new features were created: + + \begin{itemize} + \tightlist + \item + Forecast interval (date/time future -- date/time current) + \item + Forecast error (total demand -- forecast demand) + \item + 24-hour Forecast Error (Forecast error from 24 hours ago) + \item + 48-hour Forecast Error (Forecast error from 48 hours ago) + \item + 72-hour Forecast Error (Forecast error from 72 hours ago) + \item + 7-day Forecast Error (Forecast error from 7 days ago) + \item + 14-day Forecast Error (Forecast error from 14 days ago) + \item + Relative error (Forecast error / total demand) + \item + Hour × Temperature (Hour * Temperature) + \item + Hour (Sine) (hour\_sin) --- sin(2π × Hour / 24) + \item + Hour (Cosine) (hour\_cos) --- cos(2π × Hour / 24) + \item + Month × Temperature (MonthNumb * Temperature) + \item + Hour × Forecast Demand (Hour * forecast\_demand) + \item + Temperature × Forecast Demand (Temperature * forecast\_demand) + \item + Temperature × Hour (Sine) (Temperature * hour\_sin) + \item + Temperature × Hour (Cosine) (Temperature * hour\_cos) + \item + Forecast Demand × Hour (Sine) (forecast\_demand * hour\_sin) + \item + Forecast Demand × Hour (Cosine) (forecast\_demand * hour\_cos) + \item + 24-hour Forecast Error × Hour (Cosine) (24hrpreverrors * hour\_cos) + \item + 24-hour Forecast Error × Hour (Sine) (24hrpreverrors * hour\_sin) + \end{itemize} +\item + \textbf{Splitting the data} - As a final step in pre-processing, the data were split into 70\% training 7 October 2017 -- 5 March 2020) and 30\% testing (6 March 2020 -- 17 March 2021). This split allowed for a large number of data to be trained on, and a full year to test which captured all seasonal effects. The same split was used across the models. +\end{enumerate} + +Note that modelling methods chosen did not require normalisation of the data. + +\section{Assumptions}\label{assumptions} + +\begin{itemize} +\item + AEMO's forecast data is released every 5 minutes, therefore forecast data for the 12 hour interval is available to use in the model +\item + Temperature/weather forecasts are available for 12 hours into the future. +\item + Bankstown weather variables are reasonable representations of weather conditions across New South Wales. +\end{itemize} + +\section{Modelling Methods}\label{modelling-methods} + +The following methods were in this study: + +\begin{itemize} +\item + Linear Regression: Baseline model for improving forecasts due to its simple implementation and interpretability. +\item + SARIMA: EDA identified autocorrelation between forecast errors. Due to the seasonal nature of electricity demand, SARIMA modelling was conducted. +\item + Decision Trees: EDA identified non-linearity between electricity demand and its explanatory variables. As such, decisions trees were implemented to explore simpler non-linear behaviors. +\item + XGBoost: Implemented to explore non-linear behaviors using advanced techniques. +\end{itemize} + +These modelling methodologies are described below. + +\bigskip + +\noindent \textbf{ARIMA} + +\bigskip + +\noindent Auto Regressive Integrated Moving Average (ARIMA) is a time series forecasting model. Besides being well-researched and more readily explainable compared to machine learning models, its algorithm specifications make it suitable for energy demand forecasting. The model consists of three main components: + +\bigskip + +Auto Regression: The model utilises lagged observations or previous time points. Due to the weather conditions of previous days having a direct influence on future weather, previous time points are relevant for forecasting. In addition, energy demand also exhibits seasonality that can be captured by previous inputs. + +\bigskip + +Differencing (Integration): Energy and weather demands over different time horizons exhibit slight trend. Raw observations are differenced to make statistical properties (such as mean or variance) stabilised over time. + +\bigskip + +Moving average: Smooths variance by modelling a moving average of lagged variables against point residuals. This reduces noise in highly variable factors susceptible to measurement error like weather. + +\bigskip + +Seasonal Auto Regressive Intergrated Moving Average (SARIMA) is an extension of the ARIMA model. SARIMA is designed to support seasonality in time series data. It can be modified to incorporate seasonality in different time horizons such as weekly, monthly, or quarterly time frames. The model parameters are the same as ARIMA with the inclusion of seasonal variants to control for seasonal effects: seasonal autoregressive order, seasonal differencing order, and seasonal moving average order. + +\bigskip + +\noindent \textbf{Decision Trees} + +\bigskip + +\noindent Decision Trees are a type of explainable machine learning model. They are trained by recursively dividing the dataset into subsets using entropy (a measure of impurity or randomness in the dataset) and optimise for information gain. The impurity is in context to the target variable. When a subset of data is comprised of an entire class, it is considered pure. It is interpretable because the model construction can be read as a series of conditional IF statements to achieve certain outputs. + +\bigskip + +A Random Forest is a collection of generated Decision Trees. The generation formula is consistent across each decision tree, the difference being each tree is generated from a different bootstrap sample. The prediction outputs for regression tasks, such as energy demand forecasting, is an average of all decision tree outputs. Random Forests lose the ability of decision trees to be interpretable, the benefit however, is improved accuracy and robustness. + +\bigskip + +\noindent \textbf{XGBoost} + +\bigskip + +\noindent XGBoost, short for extreme gradient boosting, is a gradient descent machine learning method. Its formulation is by use of a loss function to measure the difference between predicted and actual values and a regularization term to penalize complex models. + +\bigskip + +It functions by building decision trees sequentially. Each tree is trained to predict the residuals from previous trees. Each tree split mechanism follows the process of regular decision tree training. Each tree's contribution to the final prediction is weighted by a learning rate. It generally outperforms regular decision tree models due to its internal corrections of error and feature selection. Its construction makes it suitable for regression tasks such as energy demand forecasting. + +\chapter{Exploratory Data Analysis}\label{exploratory-data-analysis} + +This section presents an exploratory analysis of the temperature, forecasted demand, and actual electricity demand data. Exploratory data analysis (EDA) explored how demand responds to temperature variations and where forecast discrepancies are most pronounced. The data is manipulated and visualised with Python. + +\bigskip + +We begin the analysis by focusing on the individual distributions and characteristics of each dataset. This stage provides context on the seasonal variability of the data. + +\section{Electricity Demand}\label{electricity-demand} + +Electricity demand shows a cyclical pattern with a downward trend when observing it throughout the years (Figure \ref{fig:yeardemand}). This may be due to more households investing in embedded generation to supplement their electricity supply. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/demandvtime} \caption{Electricity Demand vs Time}\label{fig:yeardemand} +\end{figure} + +Electricity demand is higher during winter and summer months (Figure \ref{fig:monthdemand}). This is likely due to higher consumption of electricity to power heating and cooling appliances. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/demandMonth} \caption{Total Demand by Month}\label{fig:monthdemand} +\end{figure} + +Demand was observed to be greater in weekdays than weekends (Figure \ref{fig:weekdemand}). This may be due to many businesses closing during weekends. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/WeekDemand} \caption{Total Demand by Day of the Week}\label{fig:weekdemand} +\end{figure} + +Electricity demand is relatively high between 8am and 11pm (Figure \ref{fig:hourdemand}), likely due to the human sleeping cycle. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/demandHour} \caption{Total Demand by Hour of Day}\label{fig:hourdemand} +\end{figure} + +\section{Forecast Electricity Demand}\label{forecast-electricity-demand} + +This dataset contains electricity demand forecasts made every 30 minutes. Each time a forecast is made, it includes 48 predictions---one for each half-hour period from 30 minutes ahead up to 24 hours ahead. + +\bigskip + +The scatter plot of electricity demand forecasts vs actual electricity demand across different prediction time periods (Figure \ref{fig:forecastdem}), reveals lower correlation as both the prediction time period and actual electricity demand increase. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/forecastdemscatter} \caption{Scatter plots of Forecast Demand vs Total Demand by Lag interval}\label{fig:forecastdem} +\end{figure} + +\section{Weather Variables vs Electricity Demand}\label{weather-variables-vs-electricity-demand} + +In this next section, we will examine if and how weather affects both the electricity demand and its forecast. + +\bigskip + +The correlation of relevant weather variables with electricity demand shows weak correlation across all variables, with humidity having the highest correlation and rain having the lowest (Figure \ref{fig:weatherdemand}). + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/WeatherVDemand} \caption{Correlation of Weather variables with Electricity Demand}\label{fig:weatherdemand} +\end{figure} + +The plot of temperature against electricity demand reveals a distinct U-shaped correlation. This pattern reflects energy usage behaviour in response to extreme temperatures (Figure \ref{fig:tempcorr}). The lowest demand levels generally occur in temperate conditions where neither heating nor cooling is heavily used. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.35\textheight]{images/tempvsdemand} \caption{Scatter plot of Electricity demand vs Temperature}\label{fig:tempcorr} +\end{figure} + +\section{Forecast Error of Electricity Demand}\label{forecast-error-of-electricity-demand} + +In this section, we will explore whether forecast inaccuracies are correlated with known variables which contribute to electricity demand. Forecast error was defined as actual demand less forecast demand. + +\bigskip + +Figure \ref{fig:errorvtemp} shows that forecast error increases with temperature for temperatures greater than \textasciitilde29°C. This suggests the current forecasting model may lack information regarding forecasted temperatures. The trend also occurs for normalised demand (Figure \ref{fig:relerrorvtemp}). + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/forecastErrorTemp} \caption{Forecast Error vs Temperature Forecast}\label{fig:errorvtemp} +\end{figure} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.4\textheight]{images/PortionErrorTemp} \caption{Normalised forecast error vs temperature forecast}\label{fig:relerrorvtemp} +\end{figure} + +\subsection{Time Series Analysis}\label{time-series-analysis} + +Time series analysis of the forecast error was conducted to understand whether errors persisted with time. It was conducted for 6, 12, 18 and 24-hour forecasts. + +\bigskip + +Augmented Dickey-Fuller test (ADF Test) was conducted. It showed significant evidence for stationary forecast errors (Table \ref{tab:tab3}). + +\begin{table}[H] +\caption{ADF tests conducted for 6, 12, 18 and 24-hour forecasts} +\centering +\begin{tabular}{||c||c||} +\hline +Hour of day = 6 & Hour of Day = 18 \\ +\hline +ADF Statistic: -33.863838 & ADF Statistic: -31.926828 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.430 & 1\%: -3.430 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\hline +Hour of Day = 12 & Hour of Day = 24 \\ +\hline +ADF Statistic: -33.407029 & ADF Statistic: -29.030458 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.430 & 1\%: -3.430 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\end{tabular} +\label{tab:tab3} +\end{table} + +Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots were generated to understand the relationship between forecast errors and lagged versions of itself over successive time lags (Figure \ref{fig:acfErrors}, Figure \ref{fig:pacfErrors}). PACF plots showed that forecast errors were significantly partially correlated with the most recent forecasts and ones made 24 and 48 hours prior. The partial correlation of the 24-hour lagged forecast error was of note due its greater significance than the 48-hour lag and its availability when forecasting. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.65\textheight]{images/ACFForecastErrors} \caption{ACF of forecast errors}\label{fig:acfErrors} +\end{figure} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.6\textheight]{images/PACFForecastErrors} \caption{PACF of forecast errors}\label{fig:pacfErrors} +\end{figure} + +Scatterplots and correlations for forecast errors and its 24-hour lagged error can be seen in Figure \ref{fig:laggedError}. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.6\textheight]{images/ForecastErrorCorrelations} \caption{Scatterplots and correlations for forecast errors and its 24-hour lagged errors}\label{fig:laggedError} +\end{figure} + +\section{Summary of Key Findings}\label{summary-of-key-findings} + +\begin{itemize} +\item + \textbf{U-shaped relationship between temperature and electricity demand:} Electricity demand increases during both extreme cold and extreme heat conditions, with the lowest demand observed during temperate conditions. This pattern is consistent with expected heating and cooling behavior and is evident in both actual and forecasted demand data. +\item + \textbf{Forecasting models capture seasonal trends:} Forecasted electricity demand shows a similar U-shaped relationship with temperature, indicating that the models are aligned with seasonal usage patterns. +\item + \textbf{Forecast error increases non-linearly with temperature, especially during extreme heat:} Forecast accuracy deteriorates significantly at higher temperatures, suggesting that current models underperform during periods of extreme heat. In comparison, performance during extreme cold is better, though still less accurate than under mild conditions. +\item + \textbf{Forecast errors are autocorrelated with past errors:} Forecast errors may be modelled by understanding historical forecast errors. Of note, the 24-hour lagged forecast error may be used for improving forecasts. +\item + \textbf{The forecast has the potential to be improved:} These findings highlight some correlations between forecast errors, temperature and time variables such as season which may indicate the model could be improved by modelling forecast errors. +\end{itemize} + +\chapter{Analysis and Results}\label{analysis-and-results} + +\section{Performance measures}\label{performance-measures} + +Two common performance measures were chosen to calculate prediction accuracy and compare models. Mean square error (MSE) and mean absolute percentage error (MAPE) were chosen to be the most appropriate measures. MSE penalises large errors which is useful to assess when the aim is to reduce large errors. Furthermore, MAPE provides an easily interpretable and comparable result. The measures are described below: + +\bigskip + +MSE is the average of the squared difference between actual demand and forecasted demand. + +\begin{equation*} +\text{MSE} = \frac{1}{n}\sum^{n}_{i=1}{(Y_i-{\hat{Y}_{i}})^2} +\end{equation*} + +MAPE takes the absolute value of the difference between the actual demand and forecast demand expresses it as a percentage of actual demand, and takes the average of this. + +\begin{equation*} +\text{MAPE} = \frac{1}{n}\sum^{n}_{i=1}{\frac{\lvert A_{i}-F_{i}\rvert}{A_{i}} * 100} +\end{equation*} + +For both measures, a smaller value represents higher accuracy, and a better performing model. + +\bigskip + +MSE and MAPE values for the original forecast can be seen below. + +\begin{align*} +\text{Existing model MSE} &= 55159 \\ +\text{Existing model MAPE} &= 2.19\% \\ +\end{align*} + +\section{Linear Regression}\label{linear-regression} + +Forecast error was defined as actual demand less forecast demand (Equation \eqref{eq:oneA}). + +\begin{equation} +\varepsilon_t = y_t - \hat{y}_{t} \label{eq:oneA} +\end{equation} + +Two linear regression models were trained for predicting the forecast error (Equation \eqref{eq:oneB}). The predicted forecast error was then used to update the forecast (Equation \eqref{eq:oneC}). The aim of the models was to reduce the updated forecast error (i.e.~\(\varepsilon^{*}_t < \varepsilon\)). + +\begin{equation} +\varepsilon_t = \hat{\varepsilon}_t(...) + \varepsilon^{*}_t \label{eq:oneB} +\end{equation} + +\begin{equation} +\hat{y}^{*}_{t} = \hat{y}_t + \hat{\varepsilon}_{t}(...) + \varepsilon^{*}_t \label{eq:oneC} +\end{equation} + +\subsection{Model Construction}\label{model-construction} + +\bigskip + +\textbf{Linear Regression - Model 1} +\bigskip + +\noindent The first model used only the 24-hour lag forecast error for predicting the forecast error (Equation \eqref{eq:oneD}). Hence, it was a simple autoregressive model. + +\begin{equation} +\varepsilon^{\text{(LR1)}}_t = \theta_{0} + \theta_{1}\varepsilon_{t-24h} \label{eq:oneD} +\end{equation} + +The model summary can be seen in Table \ref{tab:tab4}. It showed that all variables are significant at the 0.05 significance level. +\bigskip + +\begin{table}[H] +\caption{Linear Regression Model 1, OLS Regression Results} +\centering +\begin{tabular}{lr|lr} +\hline +\hline +\multicolumn{4}{c}{OLS Regression Results} \\ +\hline +\hline +Dep. Variable: & y & R-squared: & 0.113 \\ +Model: & OLS & Adj. R-squared: & 0.113 \\ +Method: & Least Squares & F-statistic: & 2696. \\ +Date: & Sun, 20 Apr 2025 & Prob (F-statistic): & 0.00 \\ +Time: & 11:19:06 & Log-Likelihood: & -1.4233e+05 \\ +No. Observations: & 21064 & AIC: & 2.847e+05 \\ +Df Residuals: & 21062 & BIC: & 2.847e+05 \\ +Df Model: & 1 & & \\ +Covariance Type: & nonrobust & & \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrrrrrr} + & coef & std err & t & P>|t| & [0.025 & 0.975] \\ +\hline +const & 10.5613 & 1.438 & 7.346 & 0.000 & 7.743 & 13.379 \\ +forecast\_error & 0.3369 & 0.006 & 51.927 & 0.000 & 0.324 & 0.350 \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrlr} +Omnibus: & 2737.590 & Durbin-Watson: & 0.201 \\ +Prob(Omnibus): & 0.000 & Jarque-Bera (JB): & 23155.543 \\ +Skew: & -0.344 & Prob(JB): & 0.00 \\ +Kurtosis: & 8.090 & Cond. No. & 222.\\ +\hline +\hline +\end{tabular} +\label{tab:tab4} +\end{table} + +\noindent \textbf{Linear Regression - Model 2} + +\bigskip + +\noindent The second model used the 24-hour lag forecast error and all possible explanatory variables for the forecast error identified in EDA (Equation \eqref{eq:oneE}). + +\begin{equation} +\begin{split} +\varepsilon^{(LR2)}_{t} = &\theta_0 + \theta_1\varepsilon_{t-24h} +\theta_{2}forecastTemperature_t + \\ +& \theta_{3}forecastHumidity_t + \theta_{4}forecastWind_t + \theta_{5}forecastRain_t + \\ +& \theta_{6}isSaturday_t + \theta_{7}isSunday_t + \theta_{8}isJanuary_t + \\ +& \theta_{9}isNovember_t + \theta_{10}isDecember_t +\end{split} +\label{eq:oneE} +\end{equation} + +The model summary can be seen in Table \ref{tab:tab5}. It showed that all variables, except \textit{forecastRain}, are significant at the 0.05 significance level. + +\begin{table}[H] +\centering +\caption{Linear Regression Model 2, OLS Regression Results} +\begin{tabular}{lr|lr} +\hline +\hline +\multicolumn{4}{c}{OLS Regression Results} \\ +\hline +\hline +Dep. Variable: & y & R-squared: & 0.124 \\ +Model: & OLS & Adj. R-squared: & 0.124 \\ +Method: & Least Squares & F-statistic: & 298.6 \\ +Date: & Sun, 20 Apr 2025 & Prob (F-statistic): & 0.00 \\ +Time: & 11:19:08 & Log-Likelihood: & -1.4220e+05 \\ +No. Observations: & 21064 & AIC: & 2.844e+05 \\ +Df Residuals: & 21053 & BIC: & 2.845e+05 \\ +Df Model: & 10 & & \\ +Covariance Type: & nonrobust & & \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrrrrrr} + & coef & std err & t & P>|t| & [0.025 & 0.975] \\ +\hline +const & 10.5613 & 1.438 & 7.346 & 0.000 & 7.743 & 13.379 \\ +forecast\_error & 0.3369 & 0.006 & 51.927 & 0.000 & 0.324 & 0.350 \\ +Temperature & -1.1963 & 0.296 & -4.040 & 0.000 & -1.777 & -0.616 \\ +Humidity & 0.9220 & 0.094 & 9.811 & 0.000 & 0.738 & 1.106 \\ +Wind\_speed & 6.7613 & 0.927 & 7.296 & 0.000 & 4.945 & 8.578 \\ +Rain & -3.8983 & 2.807 & -1.389 & 0.165 & -9.399 & 1.603 \\ +isSaturday & 29.1366 & 4.143 & 7.033 & 0.000 & 21.016 & 37.257 \\ +isSunday & 8.2777 & 4.149 & 1.995 & 0.046 & 0.145 & 16.410 \\ +isDecember & 20.6354 & 4.986 & 4.139 & 0.000 & 10.863 & 30.408 \\ +isJanuary & 13.2394 & 5.233 & 2.530 & 0.011 & 2.982 & 23.497 \\ +isNovember & 10.3352 & 4.832 & 2.139 & 0.032 & 0.865 & 19.805 \\ +\hline +\hline +\end{tabular} + +\begin{tabular}{lrlr} +Omnibus: & 2701.773 & Durbin-Watson: & 0.203 \\ +Prob(Omnibus): & 0.000 & Jarque-Bera (JB): & 23691.264 \\ +Skew: & -0.315 & Prob(JB): & 0.00 \\ +Kurtosis: & 8.157 & Cond. No. & 1.67e+03\\ +\hline +\hline +\end{tabular} +\label{tab:tab5} +\end{table} + +\subsection{Model Performance}\label{model-performance} + +The predicted forecast error was then used to update the forecast error (Equation \eqref{eq:oneC}. Model evaluation (MSE and MAPE) can be seen below. + +\begin{multicols}{2} +\noindent\textbf{LRegression Model 1 Performance} \\ +- MSE: 49348 \\ +- MAPE: 2.06\% \\ + + +\columnbreak + + +\noindent\textbf{LRegression Model 2 Performance} \\ +- MSE: 49718\\ +- MAPE: 2.078\%\\ +\end{multicols} + +Model 1 performed better as it minimised both MAPE and MSE values. + +\section{S-ARIMA}\label{s-arima} + +Two SARIMA model collections were trained for predicting the forecast error (Equation \eqref{eq:oneB}. The predicted forecast error was then used to update the forecast (Equation \eqref{eq:oneC}). The aim of the models was to reduce the updated forecast error (i.e.~\(\varepsilon^{*}_{t} < \varepsilon_t\)). + +\bigskip + +A model collection contained a SARIMA model for each hour of the day. This reduced overall computation time, while allowing hour of day to be an explanatory variable (note, training the model on all data was not feasible due to limited computing power). The large data size should allow for data segmentation to have minimal impact on model training. + +\bigskip + +EDA, conducted earlier, showed that forecast errors are partially correlated with lagged values of itself in 24-hour intervals. As such, SARIMA modelling only considered lags of 24-hours. + +\subsection{Parameter Selection}\label{parameter-selection} + +ADF tests conducted showed significant evidence for stationary forecast errors, after segmentation by hour of day (Table \ref{tab:tab6}). As such no differencing (d, D) was considered for SARIMA modelling. + +\begin{table}[H] +\caption{ADF tests for 4, 10, 16, 22-hour forecasts} +\centering +\begin{tabular}{||c||c||} +\hline +Hour of day = 4 & Hour of Day = 10 \\ +\hline +ADF Statistic: -5.875132 & ADF Statistic: -7.680207 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.432 & 1\%: -3.432 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\hline +Hour of Day = 16 & Hour of Day = 22 \\ +\hline +ADF Statistic: -9.601104 & ADF Statistic: -7.968233 \\ +p-value: 0.000000 & p-value: 0.000000 \\ +Critical Values: & Critical Values: \\ + 1\%: -3.432 & 1\%: -3.432 \\ + 5\%: -2.862 & 5\%: -2.862 \\ + 10\%: -2.567 & 10\%: -2.567 \\ +Stationary & Stationary \\ +\hline +\end{tabular} +\label{tab:tab6} +\end{table} + +\bigskip + +ACF and PACF plots were generated to assist in SARIMA parameters selection (Figure \ref{fig:ACF}, Figure \ref{fig:PACF}). The PACF plot showed that, generally, forecast errors are partially correlated with the first lagged term, followed by the next six lagged term, then 1-week and 2-week lags. As such, auto-regressed parameters (p) considered were 1, 2, 6 and 7, and the auto-regressed seasonal parameters (P) considered were 1 and 2. The seasonality parameter (s) was set at 7 for a weekly seasonality. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.35\textheight]{images/Stationarity1} \caption{ACF of forecast errors with 24-hour lags}\label{fig:ACF} +\end{figure} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.35\textheight]{images/Stationarity2} \caption{PACF of forecast errors with 24-hour lags}\label{fig:PACF} +\end{figure} + +\bigskip + +Moving average parameters considered (q, Q) were 0, 1 and 2. A summary of model parameters can be seen in Table \ref{tab:tab7}. + +\bigskip + +\begin{table}[H] +\centering +\caption{SARIMA Model parameters} +\begin{tabular}{|c|ccc|cccc|} +\hline +\multirow{2}{*}{\textbf{ID}} & \multicolumn{3}{c|}{\textbf{ARIMA Order}} & \multicolumn{4}{c|}{\textbf{Seasonal Order}} \\ \cline{2-8} + & \multicolumn{1}{c|}{\textbf{p}} & \multicolumn{1}{c|}{\textbf{d}} & \textbf{q} & \multicolumn{1}{c|}{\textbf{p}} & \multicolumn{1}{c|}{\textbf{D}} & \multicolumn{1}{c|}{\textbf{Q}} & \textbf{s} \\ \hline +0 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 0 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +1 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +2 & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +3 & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{0} & 7 & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{0} & 0 \\ \hline +4 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & 0 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +5 & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{0} & 2 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +6 & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{0} & 2 & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{2} & 7 \\ \hline +7 & \multicolumn{1}{c|}{8} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +8 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & 1 & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{0} & \multicolumn{1}{c|}{1} & 7 \\ \hline +\end{tabular} +\label{tab:tab7} +\end{table} + +\bigskip + +MSE and MAPE values were generated for each model in Table \ref{tab:tab7} (Figure \ref{fig:MSEsarima}, Figure \ref{fig:MAPEsarima}). Models 5, 6 and 7 equally improved MSE and minimised MAPE values. Model 5 was selected as it was the least complex of the three. + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MSEarima} \caption{MSE improvement for each SARIMA model}\label{fig:MSEsarima} +\end{figure} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MAPEtuning} \caption{MAPE for each SARIMA model}\label{fig:MAPEsarima} +\end{figure} + +\subsection{Model Construction}\label{model-construction-1} + +\textbf{SARIMA - Model 1} + +\bigskip + +The first model used only lagged versions of the forecast error for predicting the forecast error (Equation \eqref{eq:sarima1}). + +\begin{equation} +\varepsilon^{\text{(SARIMA1)}}_{t} = \text{SARIMA}(6,0,2)(1,0,1,7) +\label{eq:sarima1} +\end{equation}\\ +\noindent \textbf{SARIMA - Model 2} + +\bigskip + +The second model used lagged versions of the forecast error and exogenous data (forecast temperature, humidity, wind and rainfall) for predicting the forecast error (Equation \eqref{eq:sarima2}). + +\begin{equation} +\begin{split} +\varepsilon^{\text(SARIMA2)}_t =& \text(SARIMA)(6,0,2)(1,0,1,7) + \\ +& \theta_{1}forecastTemperature_t + \\ +& \theta_{2}forecastHumidity_t + \theta_{3}forecastWind_t + \\ +& \theta_{4}forecastRain_t +\end{split} +\label{eq:sarima2} +\end{equation} + +\subsection{Model Performance}\label{model-performance-1} + +The predicted forecast error was then used to update the forecast error (Equation \eqref{eq:oneC}). Model evaluation (MSE and MAPE) can be seen below. + +\begin{multicols}{3} +\noindent\textbf{Old Model}\\ +\textbf{Performance}\\ +- MSE: 55078.33198 \\ +- MAPE: 2.178\% \\ + + +\columnbreak + + +\noindent\textbf{SARIMA Model} \\ +\textbf{(no Exog) Performance} \\ +- MSE: 49519.31974\\ +- MAPE: 2.049\%\\ + +\columnbreak + + +\noindent\textbf{SARIMA Model (with} \\ +\textbf{Exog) Performance} \\ +- MSE: 49165.32932 \\ +- MAPE: 2.082\% \\ +\end{multicols} + +Model 1 performed better as it minimised MAPE, which was given greater importance. + +\section{Random Forest}\label{random-forest} + +Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the average prediction of the individual trees. + +The Random Forest model discussed aims to reduce the demand forecast error by predicting demand directly rather than predicting the error and then updating the original forecast. + +\subsection{Model Construction}\label{model-construction-2} + +\textbf{RFMF1 - Model 1}\\ +The first model used the lag of the forecast error. The accuracy of predictions improved slightly in this model. +\bigskip + +\noindent\textbf{RFMF1 - Model 2}\\ +The second model used the lag of the forecast error and included weather variables (forecast temperature, wind speed, humidity and rain). The model predictions improved in this model (Figure \ref{fig:ForestModel2}). + +\subsection{Parameter Selection (Fine Tuning)}\label{parameter-selection-fine-tuning} + +Fine tuning was done by utilising Grid Search on the Random Forest Model. + +\bigskip + +By trialing many parameters combinations, the following combination was found to be the best performing. + +\begin{align*} +n\_estimators&=200\\ +max\_depth&=10\\ +min\_samples\_split&=5\\ +min\_samples\_leaf&=2\\ +\end{align*} + +\noindent Where \(n\_estimator\) is the number of trees, \(max\_depth\) is the maximum depth of each individual tree, \(min\_samples\_split\) is the minimum number of samples required to split an internal node and, \(min\_samples\_leaf\) is the minimum number of samples required to be at a leaf node. + +\subsection{Model Performance}\label{model-performance-2} + +Setting up models with the values above had the following results (Figure \ref{fig:ForestModel1}, Figure \ref{fig:ForestModel2}): + +\begin{multicols}{2} +\noindent\textbf{RFMF1 Performance} \\ +- MSE: 51971.086 \\ +- MAPE: 2.111% \\ + + +\columnbreak + + +\noindent\textbf{RFMF2 Performance} \\ +- MSE: 51395.334\\ +- MAPE: 2.095%\\ +\end{multicols} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/ForestModel1} \caption{Line Plot of RFMF1's Performance vs Original Forecast Model Performance}\label{fig:ForestModel1} +\end{figure} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/ForestModel2} \caption{Line Plot of RFMF2's Performance vs Original Forecast Model Performance}\label{fig:ForestModel2} +\end{figure} + +\section{XGBoost}\label{xgboost} + +Extreme Gradient Boosting (XGBoost) is a machine learning algorithm that utilises gradient boosting decision trees that generates fast and effective models used for forecasting, classification and regression problems. + +\bigskip + +As discussed above, forecasting has been seen to improve when incorporating the new weather forecast values combined with previous errors. The overall aim being to reduce the demand forecast error. + +\bigskip + +The XGBoost model discussed aims to reduce the demand forecast error by predicting demand directly rather than predicting the error and then updating the original forecast. + +\subsection{Model Construction}\label{model-construction-3} + +The base model involved using forecasted temperature, humidity, wind speed and rain, combining that with the hour, month and the day of week. Taking the model to the next level involved including the previous forecasted demand and the previous forecast error from 24 hours, 48 hours, 7 days and 14 days ago. + +\bigskip + +Assessing where a base model performs worse based on hour of day yields the following. + +\bigskip + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/MAPEXGBoost} \caption{MAPE by Hour of the Day}\label{fig:MAPEXGBoost} +\end{figure} + +\bigskip + +Taking this error as a wave format, the model improved when variables were combined with a sin or cos wave. Specifically, combining hour with sin/cos wave and then multiplying by forecasted temperature improved the model. + +\subsection{Parameter Selection (Fine Tuning)}\label{parameter-selection-fine-tuning-1} + +Fine tuning is especially important for XGBoost and a grid search was utilised to find the highest performing combination from a wide distribution of parameters. The following was found to be the most effective combination of parameters. + +\begin{center} +\begin{align*} +learning\_rate &= 0.1,\\ +n\_estimators &= 150,\\ +max\_depth &= 3,\\ +subsample &= 0.8 +\end{align*} +\end{center} + +\subsection{Model Performance}\label{model-performance-3} + +Utilising the above parameters gave accuracy scores of + +\begin{align*} +\text{MSE} &= 46526.86 \\ +\text{MAPE} &= 2.042\% +\end{align*} + +\subsection{Combining variables}\label{combining-variables} + +Introducing new variables as functions of other variables boosted the performance of XGBoost. Whilst in theory introducing variables such as Temperature * Humidity could improve the model, introducing them created unnecessary complexity that reduced the accuracy of the model. It could also been seen that XGBoost took these into account inside the algorithm. + +\section{Model Comparison}\label{model-comparison} + +Comparison of all models tested against the baseline AEMO model (Table \ref{tab:tab8}). The XG Boost model produced the highest level of accuracy of the models considered. This is further evident when observing prediction error distribution (Figure \ref{fig:Cpmparison}). + +\begin{table}[H] +\caption{Models compared by MSE and MAPE} +\centering +\begin{tabular}{|ll|cc|} +\hline +\multicolumn{2}{|l|}{\multirow{2}{*}{}} & \multicolumn{2}{c|}{\textbf{Measure}} \\ \cline{3-4} +\multicolumn{2}{|l|}{} & \multicolumn{1}{c|}{MSE} & MAPE \\ \hline +\multicolumn{1}{|l|}{\multirow{5}{*}{\textbf{Model}}} & {\textbf{AEMO}} & \multicolumn{1}{c|}{{55,159}} & {2.190\%} \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{Linear Regression} & \multicolumn{1}{c|}{49,718} & 2.080\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{SARIMA} & \multicolumn{1}{c|}{49,519} & 2.049\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{XGBoost} & \multicolumn{1}{c|}{46,526} & 2.042\% \\ \cline{2-4} +\multicolumn{1}{|l|}{} & \textbf{Random Forest} & \multicolumn{1}{c|}{51,395} & 2.095\% \\ \hline +\end{tabular} +\label{tab:tab8} +\end{table} + +\begin{figure} +\includegraphics[width=1\linewidth,height=0.3\textheight]{images/Comparison} \caption{MSE Distribution}\label{fig:Cpmparison} +\end{figure} + +\section{Summary of Key Findings}\label{summary-of-key-findings-1} + +\begin{itemize} +\item + \textbf{All models outperformed AEMO's model in terms of MSE and MAPE.} This indicates that there are likely underlying patterns in the AEMO residuals that are not currently captured in their model, therefore their model could be improved. This also validates forecasting methodologies which aims to use forecasting errors in modelling to further improve forecasting accuracy. +\item + \textbf{XGBoost performed best compared with linear regression, SARIMA and Decision trees.} This may indicate that some underlying patterns in AEMO's forecast errors are likely non-linear, and therefore best forecasted by a black box method that can handle non-linear relationships. +\item + \textbf{SARIMA also performed strongly}, indicating seasonality in the forecast error. This reinforces EDA findings where correlations existed between lagged forecast errors and current forecast errors. The SARIMA model also performed quite strongly based on MSE and MAPE. Furthermore, the SARIMA model which only included the lagged error performed the strongest, which provides further evidence of this relationship. +\item + \textbf{Both machine learning models performed better when including weather related variables.} This may indicate a non-linear relationship between the forecast error (at least partly) and weather indicators (note, SARIMA would be limited in its ability to capture non-linearity). +\end{itemize} + +\newpage + +\chapter{Discussion}\label{discussion} + +\section{Interpretation of results}\label{interpretation-of-results} + +The results of this project demonstrate the validity of using forecast errors in modelling to further improve electricity demand predictions. This is particularly important for short-term energy demand forecasting which relies on the precision of forecasts to balance electricity demand and supply rather than requiring an interpretable model. + +\bigskip + +While XGBoost was the best performing model for the datasets considered, this may not be the case for all forecasts, depending on the underlying patterns in the forecast errors. If patterns in forecast errors are linear, traditional models such as ARIMA may perform better to improve the forecast. On the other hand, this project demonstrated that non-linear trends were present in the forecast errors which allowed the XGBoost model to be the better performing model. + +\bigskip + +Both machine learning methods performed better when incorporating weather indicator variables which may point to different models capturing different types of relationships (e.g.~SARIMA capturing the effect of the lagged error and XGBoost capturing effects of weather-related variables). This could indicate that modelling the forecast errors of the best model (XGBoost) with a different model (e.g.~SARIMA) has the potential to produce even more accurate results. + +\section{Implications for energy planning}\label{implications-for-energy-planning} + +Future short-term electricity forecasts should consider how residuals could be used to further improve forecasting accuracy. This could involve decomposing the data in the first instance as others have done and discussed in the literature review, or incorporating residuals from an initial forecasted model in a subsequent model. Improving forecast accuracy will likely have the benefit of greater efficiency in managing electricity generation. + +\bigskip + +It should be noted that the method described in this report has proven useful to increase accuracy of forecast predictions which is essential for short-term forecasting, however may not be appropriate for longer-term forecasting where interpretability of the model is more important. + +\section{Limitations, challenges, and further research}\label{limitations-challenges-and-further-research} + +While this project was able to improve on the AEMO forecast by modelling the forecast error, there is the potential that this could have been achieved more efficiently if a detailed AEMO forecasting methodology was available. This could have provided a better idea of what information or trends could be missing from the original forecast, and therefore which method would have been most useful to model the residuals. + +\bigskip + +This study only considered 12-hour interval due to the complexity of including several intervals and computational burden. Future research could consider longer or shorter forecast intervals to test the impact of modelling forecasting errors to improve accuracy for different intervals. Further research could also consider forecasting model errors using multiple models to capture the different patterns of errors. + +\chapter{Conclusion and Further Issues}\label{conclusion-and-further-issues} + +Hybrid models are known to improve the accuracy of electricity demand forecasts. This report took the concepts of a hybrid model to improve AEMO's short-term electricity demand forecast by incorporating AEMO forecast errors in a subsequent model to produce a more accurate prediction. The report found that for all models tested, the accuracy of the short-term forecast improved. This is valuable to industry as balancing the supply and demand of energy requires highly accurate forecasting. While this report only considered a 12-hour forecast interval, future studies could investigate different forecast intervals or multiple-iteration error-corrections to improve the accuracy of energy demand forecasts. + +\newpage + +\chapter*{References}\label{references} +\addcontentsline{toc}{chapter}{References} + +\begin{hangparas}{.25in}{1} +Abbasi, R.A., Javaid, N., Ghuman, M.N.J., Khan, Z.A., Ur Rehman, S. \& Amanullah (2019). ‘Short Term Load Forecasting Using XGBoost’, \textit{Advances in Intelligent Systems and Computing}, pp.1120–1131. doi:\url{https://doi.org/10.1007/978-3-030-15035-8_108}. + +AEMO. (2023). Load forecasting. Available at: \url{https://aemo.com.au/-/media/files/electricity/nem/security_and_reliability/power_system_ops/procedures/so_op_3710-load-forecasting.pdf?la=en} [Accessed 24 Mar. 2025]. + +Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H. \& Saidur, R. (2014). ‘A review on applications of ANN and SVM for building electrical energy consumption forecasting’, \textit{Renewable and Sustainable Energy Reviews}, [online] 33, pp.102–109. doi:\url{https://doi.org/10.1016/j.rser.2014.01.069}. + +Ahmad, T. \& Chen, H. (2018). ‘Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment’, \textit{Energy}, 160, pp.1008–1020. doi:\url{https://doi.org/10.1016/j.energy.2018.07.084}. + +Ahmad, W., Ayub, N., Ali, T., Irfan, M., Awais, M., Shiraz, M. \& Glowacz, A. (2020). ‘Towards short term electricity load forecasting using improved support vector machine and extreme learning machine’, \textit{Energies}, 13(11), p.2907. doi:\url{https://doi.org/10.3390/en13112907}. + +Amara, F., Agbossou, K., Dubé, Y., Kelouwani, S., Cardenas, A. \& Hosseini, S.S. (2019). ‘A residual load modeling approach for household short-term load forecasting application’, \textit{Energy and Buildings}, 187, pp.132–143. doi:\url{https://doi.org/10.1016/j.enbuild.2019.01.009}. + +Andronikos, A., Tzelepi, M. \& Tefas, A. (2023). ‘Residual Error Learning for Electricity Demand Forecasting’, In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C. \& Pimenidis, E. (eds) \textit{Engineering Applications of Neural Networks}. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. doi:\url{https://doi.org/10.1007/978-3-031-34204-2_33}. + +Añel, J.A., Pérez-Souto, C., Bayo-Besteiro, S., Prieto-Godino, L., Bloomfield, H., Troccoli, A. \& Laura (2024). ‘Extreme weather events and the energy sector in 2021’, \textit{Weather Climate and Society}. doi:\url{https://doi.org/10.1175/wcas-d-23-0115.1}. + +Ardakani, F.J. \& Ardehali, M.M. (2014). ‘Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types’, \textit{Energy}, 65, pp.452–461. doi:\url{https://doi.org/10.1016/j.energy.2013.12.031}. + +Boroojeni, K.G., Amini, M.H., Bahrami, S., Iyengar, S.S., Sarwat, A.I. \& Karabasoglu, O. (2017). ‘A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon’, \textit{Electric Power Systems Research}, 142, pp.58–73. doi:\url{https://doi.org/10.1016/j.epsr.2016.08.031}. + +Deng, X., Ye, A., Zhong, J., Xu, D., Yang, W., Song, Z., Zhang, Z., Guo, J., Wang, T., Tian, Y., Pan, H., Zhang, Z., Wang, H., Wu, C., Shao, J. \& Chen, X. (2022). ‘Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model’, \textit{Energy Reports}, 8, pp.8661–8674. doi:\url{https://doi.org/10.1016/j.egyr.2022.06.072}. + +Divina, F., García Torres, M., Goméz Vela, F.A. \& Vázquez Noguera, J.L. (2019). ‘A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings’, \textit{Energies}, 12(10), p.1934. doi:\url{https://doi.org/10.3390/en12101934}. + +Ediger, V.Ş. \& Akar, S. (2007). ‘ARIMA forecasting of primary energy demand by fuel in Turkey’, \textit{Energy Policy}, 35(3), pp.1701–1708. doi: \url{https://doi.org/10.1016/j.enpol.2006.05.009}. + +Ertuğrul, Ö.F., Tekin, H. \& Tekin, R. (2020). ‘A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid’, \textit{Electrical Engineering}, 103, pp: 717-728. doi:\url{https://doi.org/10.1007/s00202-020-01114-3}. + +Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R. \& Young, W.A. (2016). ‘An overview of energy demand forecasting methods published in 2005–2015’, \textit{Energy Systems}, 8(2), pp.411–447. doi:\url{https://doi.org/10.1007/s12667-016-0203-y}. + +Klyuev, R.V., Morgoev, I.D., Morgoeva, A.D., Gavrina, O.A., Martyushev, N.V., Efremenkov, E.A. \& Mengxu, Q. (2022). ‘Methods of Forecasting Electric Energy Consumption: A Literature Review’, \textit{Energies}, 15(23), p.8919. doi:\url{https://doi.org/10.3390/en15238919}. + +Kopyt, M., Piotrowski, P. \& Baczyński, D. (2024). ‘Short-Term Energy Generation Forecasts at a Wind Farm—A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models’, \textit{Energies}, 17(23), p.6194. doi:\url{https://doi.org/10.3390/en17236194}. + +Koukaras, P., Mustapha, A., Mystakidis, A. \& Tjortjis, C. (2024). ‘Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models’, \textit{Energies}, 17(6), p.1450. doi:\url{https://doi.org/10.3390/en17061450}. + +Kuo, P.-H. \& Huang, C.-J. (2018). ‘A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting’, \textit{Energies}, 11(1), p.213. doi:\url{https://doi.org/10.3390/en11010213}. + +Liu, X.-Q., Zhang, C., Zhou, Y. \& Liao, H. (2021). ‘Temperature change and electricity consumption of the group living: A case study of college students’, \textit{Science of The Total Environment}, 781, p.146574. doi:\url{https://doi.org/10.1016/j.scitotenv.2021.146574}. + +Maia-Silva, D., Kumar, R. \& Nateghi, R. (2020). ‘The critical role of humidity in modeling summer electricity demand across the United States’, \textit{Nature Communications}, 11, p.1686. doi:\url{https://doi.org/10.1038/s41467-020-15393-8}. + +Manno, A., Martelli, E. and Amaldi, E. (2022). ‘A Shallow Neural Network Approach for the Short-Term Forecast of Hourly Energy Consumption’, \textit{Energies}, [online] 15(3), pp.958. doi:\url{https://doi.org/10.3390/en15030958}. + +Marco G. Pinheiro, Sara C. Madeira, Alexandre P. Francisco, (2023). ‘Short-term electricity load forecasting—A systematic approach from system level to secondary substations’, \textit{Applied Energy}, 332, pp.120493, ISSN 0306-2619, doi:\url{https://doi.org/10.1016/j.apenergy.2022.120493}. + +Mystakidis, A., Koukaras, P., Tsalikidis, N., Ioannidis, D. and Tjortjis, C. (2024). ‘Energy Forecasting: A Comprehensive Review of Techniques and Technologies’, \textit{Energies}, [online] 17(7), pp.1662. doi:\url{https://doi.org/10.3390/en17071662}. + +OpenWeather. (2025). \textit{Custom Weather Products.} [Online] Available at: \url{https://home.openweathermap.org/marketplace} [Accessed 29 Mar. 2025] + +Pao, H.T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear models. \textit{Energy}, 34(10), pp.1438–1446. doi:\url{https://doi.org/10.1016/j.energy.2009.04.026}. + +Papalexopoulos, A.D. and Hesterberg, T.C. (1990). ‘A regression-based approach to short-term system load forecasting’, \textit{IEEE Transactions on Power Systems}, 5(4), pp.1535–1547. doi:\url{https://doi.org/10.1109/59.99410}. + +Phyo, P.P. and Byun, Y.-C. (2021). ‘Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction’, \textit{Symmetry}, 13(10), pp.1942. doi:\url{https://doi.org/10.3390/sym13101942}. + +Rakpho, P. and Yamaka, W. (2021). ‘The forecasting power of economic policy uncertainty for energy demand and supply’, \textit{Energy Reports}, 7, pp.338–343. doi:\url{https://doi.org/10.1016/j.egyr.2021.06.059}. + +Sanhudo, L., Rodrigues, J. and Filho, Ê.V. (2021). ‘Multivariate time series clustering and forecasting for building energy analysis: Application to weather data quality control’, \textit{Journal of Building Engineering}, 35, pp.101996. doi:\url{https://doi.org/10.1016/j.jobe.2020.101996}. + +Savić, S., Selakov, A. and Milošević, D. (2014). ‘Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas’, \textit{Natural Hazards}, 73(2), pp.373–387. doi:\url{https://doi.org/10.1007/s11069-014-1074-y}. + +Singh, S. and Yassine, A. (2018). ‘Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting’, \textit{Energies}, 11(2), pp.452. doi:\url{https://doi.org/10.3390/en11020452}. + +Suganthi, L. and Samuel, A.A. (2012). ‘Energy models for demand forecasting—A review’, \textit{Renewable and Sustainable Energy Reviews}, 16(2), pp.1223–1240. doi:\url{https://doi.org/10.1016/j.rser.2011.08.014}. + +Tarmanini, C., Sarma, N., Gezegin, C. and Ozgonenel, O. (2023). ‘Short term load forecasting based on ARIMA and ANN approaches’, \textit{Energy Reports}, 9, pp.550–557. doi:\url{https://doi.org/10.1016/j.egyr.2023.01.060}. + +Wang, J., Li, P., Ran, R., Che, Y. and Zhou, Y. (2018). ‘A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree’, \textit{Applied Sciences}, 8(5), pp.689. doi:\url{https://doi.org/10.3390/app8050689}. + +Zhang, S., Guo, Q., Smyth, R. and Yao, Y. (2022). ‘Extreme temperatures and residential electricity consumption: Evidence from Chinese households’, \textit{Energy Economics}, pp.105890. doi:\url{https://doi.org/10.1016/j.eneco.2022.105890}. +\end{hangparas} + +\newpage + +\chapter*{Appendix}\label{appendix} +\addcontentsline{toc}{chapter}{Appendix} + +\appendix + +\section*{Appendix A: Data Processing}\label{appendix-a-data-processing} +\addcontentsline{toc}{section}{Appendix A: Data Processing} + +Packages used for data cleaning + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} + +\NormalTok{pd.options.mode.chained\_assignment }\OperatorTok{=} \VariableTok{None} +\end{Highlighting} +\end{Shaded} + +Importing the data + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\# Forecast Data} +\CommentTok{\#\# Reading data and formating data{-}time columns} +\NormalTok{df\_forecast }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{\textquotesingle{}data/forecastdemand\_nsw.csv\textquotesingle{}}\NormalTok{, names }\OperatorTok{=} +\NormalTok{ [}\StringTok{\textquotesingle{}id\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}region\_id\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}period\_id\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}date\_time\_current\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}date\_time\_future\textquotesingle{}}\NormalTok{], skiprows }\OperatorTok{=} \DecValTok{1}\NormalTok{)} +\NormalTok{df\_forecast.date\_time\_current }\OperatorTok{=}\NormalTok{ pd.to\_datetime(} +\NormalTok{ df\_forecast.date\_time\_current, }\BuiltInTok{format} \OperatorTok{=} \StringTok{"\%Y{-}\%m{-}}\SpecialCharTok{\%d}\StringTok{ \%H:\%M:\%S"}\NormalTok{)} +\NormalTok{df\_forecast.date\_time\_future }\OperatorTok{=}\NormalTok{ pd.to\_datetime(} +\NormalTok{ df\_forecast.date\_time\_future, }\BuiltInTok{format} \OperatorTok{=} \StringTok{"\%Y{-}\%m{-}}\SpecialCharTok{\%d}\StringTok{ \%H:\%M:\%S"}\NormalTok{)} + +\CommentTok{\#\# Using \textquotesingle{}period\_id\textquotesingle{} to round \textquotesingle{}current time\textquotesingle{}} +\NormalTok{df\_forecast[}\StringTok{"date\_time\_current\_rounded"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_forecast.period\_id.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: pd.Timedelta(hours }\OperatorTok{=}\NormalTok{ x}\OperatorTok{/}\DecValTok{2}\NormalTok{))} +\NormalTok{df\_forecast.date\_time\_current\_rounded }\OperatorTok{=}\NormalTok{ df\_forecast.date\_time\_future }\OperatorTok{{-}} +\NormalTok{ df\_forecast.date\_time\_current\_rounded} + +\CommentTok{\# Demand Data} +\CommentTok{\#\# Reading data and formating data{-}time columns} +\NormalTok{df\_demand }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{\textquotesingle{}data/totaldemand\_nsw.csv\textquotesingle{}}\NormalTok{, names }\OperatorTok{=} +\NormalTok{ [}\StringTok{\textquotesingle{}date\_time\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}region\_id\textquotesingle{}}\NormalTok{], skiprows }\OperatorTok{=} \DecValTok{1}\NormalTok{)} +\NormalTok{df\_demand.date\_time }\OperatorTok{=}\NormalTok{ pd.to\_datetime(df\_demand.date\_time, } + \BuiltInTok{format} \OperatorTok{=} \StringTok{"}\SpecialCharTok{\%d}\StringTok{/\%m/\%Y \%H:\%M"}\NormalTok{)} + + +\CommentTok{\# Forecast temperature data} +\NormalTok{df\_weather\_forecast }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{\textquotesingle{}data/forecast\_temperatre.csv\textquotesingle{}}\NormalTok{)} + +\NormalTok{df\_weather\_forecast }\OperatorTok{=}\NormalTok{ df\_weather\_forecast.rename(} +\NormalTok{ \{}\StringTok{\textquotesingle{}forecast dt iso\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}date\_time\_current\_utc\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}slice dt iso\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}date\_time\_future\_utc\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}temperature\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}temperature\_future\_forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}humidity\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}humidity\_future\_forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}rain\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}rain\_future\_forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}wind\_speed\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}wind\_speed\_future\_forecast\textquotesingle{}}\NormalTok{\}, axis }\OperatorTok{=} \DecValTok{1}\NormalTok{)} + +\NormalTok{df\_weather\_forecast[}\StringTok{"date\_time\_current\_rounded"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ pd.to\_datetime(df\_weather\_forecast.date\_time\_current\_utc, } + \BuiltInTok{format} \OperatorTok{=} \StringTok{"\%Y{-}\%m{-}}\SpecialCharTok{\%d}\StringTok{ \%H:\%M:\%S +0000 UTC"}\NormalTok{) }\OperatorTok{+}\NormalTok{ pd.Timedelta(hours }\OperatorTok{=} \DecValTok{10}\NormalTok{)} +\NormalTok{df\_weather\_forecast[}\StringTok{"date\_time\_future"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ pd.to\_datetime(df\_weather\_forecast.date\_time\_future\_utc, } + \BuiltInTok{format} \OperatorTok{=} \StringTok{"\%Y{-}\%m{-}}\SpecialCharTok{\%d}\StringTok{ \%H:\%M:\%S +0000 UTC"}\NormalTok{) }\OperatorTok{+}\NormalTok{ pd.Timedelta(hours }\OperatorTok{=} \DecValTok{10}\NormalTok{)} + +\NormalTok{df\_weather\_forecast }\OperatorTok{=} +\NormalTok{ df\_weather\_forecast[[}\StringTok{\textquotesingle{}date\_time\_current\_rounded\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}date\_time\_future\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}temperature\_future\_forecast\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}humidity\_future\_forecast\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}rain\_future\_forecast\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}wind\_speed\_future\_forecast\textquotesingle{}}\NormalTok{]]} +\end{Highlighting} +\end{Shaded} + +Merging the data + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#Merging Datasets} +\NormalTok{df\_all }\OperatorTok{=}\NormalTok{ pd.merge(df\_forecast, df\_demand[[}\StringTok{"date\_time"}\NormalTok{, }\StringTok{"total\_demand"}\NormalTok{]], } +\NormalTok{ left\_on }\OperatorTok{=} \StringTok{"date\_time\_future"}\NormalTok{, right\_on }\OperatorTok{=} \StringTok{"date\_time"}\NormalTok{).drop(} +\NormalTok{ columns }\OperatorTok{=} \StringTok{"date\_time"}\NormalTok{)} + +\NormalTok{df\_all }\OperatorTok{=}\NormalTok{ pd.merge(df\_all, } +\NormalTok{ df\_temperature[[}\StringTok{"date\_time\_30m"}\NormalTok{, }\StringTok{"temperature"}\NormalTok{]], } +\NormalTok{ left\_on }\OperatorTok{=} \StringTok{"date\_time\_future"}\NormalTok{, right\_on }\OperatorTok{=} \StringTok{"date\_time\_30m"}\NormalTok{)} +\NormalTok{df\_all }\OperatorTok{=}\NormalTok{ df\_all.drop(} +\NormalTok{ columns }\OperatorTok{=}\NormalTok{ [}\StringTok{"date\_time\_30m"}\NormalTok{, }\StringTok{"region\_id"}\NormalTok{]).rename(} +\NormalTok{ \{}\StringTok{"temperature"}\NormalTok{: }\StringTok{"temperature\_future"}\NormalTok{\}, axis }\OperatorTok{=} \DecValTok{1}\NormalTok{)} + +\NormalTok{df\_all }\OperatorTok{=}\NormalTok{ pd.merge(df\_all, } +\NormalTok{ df\_temperature[[}\StringTok{"date\_time\_30m"}\NormalTok{, }\StringTok{"temperature"}\NormalTok{]], } +\NormalTok{ left\_on }\OperatorTok{=} \StringTok{"date\_time\_current\_rounded"}\NormalTok{, right\_on }\OperatorTok{=} \StringTok{"date\_time\_30m"}\NormalTok{)} +\NormalTok{df\_all }\OperatorTok{=}\NormalTok{ df\_all.drop(columns }\OperatorTok{=} + \StringTok{"date\_time\_30m"}\NormalTok{).rename(\{}\StringTok{"temperature"}\NormalTok{: }\StringTok{"temperature\_current"}\NormalTok{\}, } +\NormalTok{ axis }\OperatorTok{=} \DecValTok{1}\NormalTok{)} + +\NormalTok{df\_all }\OperatorTok{=}\NormalTok{ pd.merge(df\_all, df\_weather\_forecast, on }\OperatorTok{=} +\NormalTok{ [}\StringTok{"date\_time\_current\_rounded"}\NormalTok{, }\StringTok{"date\_time\_future"}\NormalTok{], how }\OperatorTok{=} \StringTok{\textquotesingle{}left\textquotesingle{}}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +Format data. + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_all[}\StringTok{"forecast\_interval"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future }\OperatorTok{{-}} +\NormalTok{ df\_all.date\_time\_current\_rounded} +\NormalTok{df\_all[}\StringTok{"forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.total\_demand }\OperatorTok{{-}} +\NormalTok{ df\_all.forecast\_demand} +\NormalTok{df\_all[}\StringTok{"forecast\_error\_relative"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_all.forecast\_error}\OperatorTok{/}\NormalTok{df\_all.total\_demand} + +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_month"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.month} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_year"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.year} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_weekday"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.dayofweek} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_hour"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.hour} + +\NormalTok{df\_all[}\StringTok{"week\_day\_name"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.day\_name()} + +\NormalTok{df\_all[}\StringTok{"isSaturday"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.week\_day\_name.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: }\DecValTok{1} \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \StringTok{\textquotesingle{}Saturday\textquotesingle{}} \ControlFlowTok{else} \DecValTok{0}\NormalTok{)} +\NormalTok{df\_all[}\StringTok{"isSunday"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.week\_day\_name.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: }\DecValTok{1} \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \StringTok{\textquotesingle{}Sunday\textquotesingle{}} \ControlFlowTok{else} \DecValTok{0}\NormalTok{)} + +\NormalTok{df\_all[}\StringTok{"isDecember"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future\_month.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: }\DecValTok{1} \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \DecValTok{12} \ControlFlowTok{else} \DecValTok{0}\NormalTok{)} +\NormalTok{df\_all[}\StringTok{"isJanuary"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future\_month.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: }\DecValTok{1} \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \DecValTok{1} \ControlFlowTok{else} \DecValTok{0}\NormalTok{)} +\NormalTok{df\_all[}\StringTok{"isFebruary"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future\_month.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: }\DecValTok{1} \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \DecValTok{2} \ControlFlowTok{else} \DecValTok{0}\NormalTok{)} +\NormalTok{df\_all[}\StringTok{"isNovember"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future\_month.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: }\DecValTok{1} \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \DecValTok{11} \ControlFlowTok{else} \DecValTok{0}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\newpage + +\section*{Appendix B: Models}\label{appendix-b-models} +\addcontentsline{toc}{section}{Appendix B: Models} + +\subsection*{B1. AEMO Model}\label{b1.-aemo-model} + +Import packages + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} +\ImportTok{import}\NormalTok{ seaborn }\ImportTok{as}\NormalTok{ sns} +\ImportTok{import}\NormalTok{ statsmodels.api }\ImportTok{as}\NormalTok{ sm} + +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_squared\_error} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_absolute\_percentage\_error} +\ImportTok{from}\NormalTok{ matplotlib.pyplot }\ImportTok{import}\NormalTok{ figure} +\end{Highlighting} +\end{Shaded} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{delta }\OperatorTok{=} \DecValTok{24} + +\NormalTok{df\_lag }\OperatorTok{=}\NormalTok{ df\_all.loc[df\_all.period\_id }\OperatorTok{==}\NormalTok{ delta].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} +\NormalTok{df\_lag\_temp }\OperatorTok{=} +\NormalTok{df\_lag.copy()[} +\NormalTok{ [}\StringTok{"forecast\_error"}\NormalTok{, }\StringTok{"forecast\_error\_relative"}\NormalTok{, }\StringTok{"date\_time\_future"}\NormalTok{]} +\NormalTok{ ].rename(\{}\StringTok{"forecast\_error"}\NormalTok{ : }\StringTok{"forecast\_error\_24h\_ago"}\NormalTok{, } + \StringTok{"forecast\_error\_relative"}\NormalTok{: }\StringTok{"forecast\_error\_relative\_24h\_ago"}\NormalTok{,} + \StringTok{"date\_time\_future"}\NormalTok{: }\StringTok{"date\_time\_future\_24h\_ago"}\NormalTok{\}, axis }\OperatorTok{=} \DecValTok{1}\NormalTok{)} +\NormalTok{df\_lag[}\StringTok{"date\_time\_current\_24h\_ago"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_lag.date\_time\_current }\OperatorTok{{-}}\NormalTok{ pd.DateOffset(hours }\OperatorTok{=} \DecValTok{24}\NormalTok{)} +\NormalTok{df\_lag[}\StringTok{"date\_time\_future\_24h\_ago"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_lag.date\_time\_future }\OperatorTok{{-}}\NormalTok{ pd.DateOffset(hours }\OperatorTok{=} \DecValTok{24}\NormalTok{)} + +\NormalTok{df\_lag }\OperatorTok{=}\NormalTok{ df\_lag.loc[} +\NormalTok{ df\_lag.date\_time\_future\_24h\_ago }\OperatorTok{\textgreater{}=} \BuiltInTok{min}\NormalTok{(df\_lag.date\_time\_future)]} +\NormalTok{df\_lag }\OperatorTok{=}\NormalTok{ pd.merge(df\_lag, df\_lag\_temp, } +\NormalTok{ on }\OperatorTok{=} \StringTok{"date\_time\_future\_24h\_ago"}\NormalTok{, how }\OperatorTok{=} \StringTok{\textquotesingle{}left\textquotesingle{}}\NormalTok{)} +\NormalTok{df\_lag }\OperatorTok{=}\NormalTok{ df\_lag.loc[df\_lag.forecast\_error\_relative\_24h\_ago.notna()]} + +\NormalTok{train\_test\_split }\OperatorTok{=} \FloatTok{0.7} +\NormalTok{split\_int }\OperatorTok{=} \BuiltInTok{int}\NormalTok{(train\_test\_split }\OperatorTok{*} \BuiltInTok{len}\NormalTok{(df\_lag))} +\NormalTok{df\_lag\_train, df\_lag\_test }\OperatorTok{=}\NormalTok{ df\_lag[:split\_int], df\_lag[split\_int:]} +\end{Highlighting} +\end{Shaded} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{mse }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_lag\_test.forecast\_demand, } +\NormalTok{ df\_lag\_test.total\_demand)} +\NormalTok{mape }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(df\_lag\_test.forecast\_demand, } +\NormalTok{ df\_lag\_test.total\_demand)} + +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Existing model MSE = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(mse)}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Existing model MAPE = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(}\DecValTok{100}\OperatorTok{*}\NormalTok{mape,}\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{\%"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{B2. Linear Regression}\label{b2.-linear-regression} + +Import packages + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} +\ImportTok{import}\NormalTok{ seaborn }\ImportTok{as}\NormalTok{ sns} +\ImportTok{import}\NormalTok{ statsmodels.api }\ImportTok{as}\NormalTok{ sm} +\ImportTok{import}\NormalTok{ warnings} + +\ImportTok{from}\NormalTok{ statsmodels.graphics.tsaplots }\ImportTok{import}\NormalTok{ plot\_acf, plot\_pacf} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_squared\_error} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_absolute\_percentage\_error} +\ImportTok{from}\NormalTok{ statsmodels.tsa.stattools }\ImportTok{import}\NormalTok{ adfuller} +\ImportTok{from}\NormalTok{ matplotlib.pyplot }\ImportTok{import}\NormalTok{ figure} +\ImportTok{from}\NormalTok{ statsmodels.graphics.api }\ImportTok{import}\NormalTok{ qqplot} +\end{Highlighting} +\end{Shaded} + +\textbf{Linear Regression Model 1} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{x\_columns }\OperatorTok{=}\NormalTok{ [}\StringTok{"forecast\_error\_24h\_ago"}\NormalTok{]} +\NormalTok{x }\OperatorTok{=}\NormalTok{ sm.add\_constant(df\_lag\_train[x\_columns])} +\NormalTok{x }\OperatorTok{=}\NormalTok{ sm.add\_constant(x)} +\NormalTok{y }\OperatorTok{=}\NormalTok{ np.array(df\_lag\_train.forecast\_error)} + +\NormalTok{model }\OperatorTok{=}\NormalTok{ sm.OLS(y, x)} +\NormalTok{results }\OperatorTok{=}\NormalTok{ model.fit()} +\BuiltInTok{print}\NormalTok{(results.summary())} + +\NormalTok{df\_lag\_test[}\StringTok{"lm\_forecast\_error\_pred"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ results.predict(sm.add\_constant(df\_lag\_test[x\_columns]))} +\NormalTok{df\_lag\_test[}\StringTok{"lm\_forecast\_demand\_new"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_lag\_test.forecast\_demand }\OperatorTok{+}\NormalTok{ df\_lag\_test.lm\_forecast\_error\_pred} + +\NormalTok{mse\_lm1 }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_lag\_test.lm\_forecast\_demand\_new, } +\NormalTok{ df\_lag\_test.total\_demand)} +\NormalTok{mape\_lm1 }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(} +\NormalTok{ df\_lag\_test.lm\_forecast\_demand\_new,df\_lag\_test.total\_demand)} + +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"}\CharTok{\textbackslash{}n}\SpecialStringTok{New model MSE = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(mse\_lm1)}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"New model MAPE = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(}\DecValTok{100}\OperatorTok{*}\NormalTok{mape\_lm1,}\DecValTok{3}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{\%"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\textbf{Linear Regression Model 2} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{x\_columns }\OperatorTok{=}\NormalTok{ [}\StringTok{"forecast\_error\_24h\_ago"}\NormalTok{, }\StringTok{"Temperature"}\NormalTok{, }\StringTok{"Humidity"}\NormalTok{, } + \StringTok{"Wind\_speed"}\NormalTok{, }\StringTok{"Rain"}\NormalTok{, }\StringTok{"isSaturday"}\NormalTok{, }\StringTok{"isSunday"}\NormalTok{, }\StringTok{"isDecember"}\NormalTok{, } + \StringTok{"isJanuary"}\NormalTok{, }\StringTok{"isNovember"}\NormalTok{]} +\NormalTok{x }\OperatorTok{=}\NormalTok{ sm.add\_constant(df\_lag\_train[x\_columns])} +\NormalTok{x }\OperatorTok{=}\NormalTok{ sm.add\_constant(x)} +\NormalTok{y }\OperatorTok{=}\NormalTok{ np.array(df\_lag\_train.forecast\_error)} + +\NormalTok{model }\OperatorTok{=}\NormalTok{ sm.OLS(y, x)} +\NormalTok{results }\OperatorTok{=}\NormalTok{ model.fit()} +\BuiltInTok{print}\NormalTok{(results.summary())} + +\NormalTok{df\_lag\_test[}\StringTok{"lm2\_forecast\_error\_pred"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ results.predict(sm.add\_constant(df\_lag\_test[x\_columns]))} +\NormalTok{df\_lag\_test[}\StringTok{"lm2\_forecast\_demand\_new"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_lag\_test.forecast\_demand }\OperatorTok{+} +\NormalTok{ df\_lag\_test.lm2\_forecast\_error\_pred} + +\NormalTok{mse\_lm2 }\OperatorTok{=}\NormalTok{ mean\_squared\_error(} +\NormalTok{ df\_lag\_test.lm2\_forecast\_demand\_new, df\_lag\_test.total\_demand)} +\NormalTok{mape\_lm2 }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(} +\NormalTok{ df\_lag\_test.lm2\_forecast\_demand\_new, df\_lag\_test.total\_demand)} + +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"}\CharTok{\textbackslash{}n}\SpecialStringTok{New model MSE = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(mse\_lm2)}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"New model MAPE = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(}\DecValTok{100}\OperatorTok{*}\NormalTok{mape\_lm2,}\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{\%"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{B2. SARIMA}\label{b2.-sarima} + +Import packages + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} +\ImportTok{import}\NormalTok{ seaborn }\ImportTok{as}\NormalTok{ sns} +\ImportTok{import}\NormalTok{ statsmodels.api }\ImportTok{as}\NormalTok{ sm} +\ImportTok{import}\NormalTok{ warnings} + +\ImportTok{from}\NormalTok{ statsmodels.graphics.tsaplots }\ImportTok{import}\NormalTok{ plot\_acf, plot\_pacf} +\ImportTok{from}\NormalTok{ statsmodels.tsa.stattools }\ImportTok{import}\NormalTok{ adfuller} +\ImportTok{from}\NormalTok{ matplotlib.pyplot }\ImportTok{import}\NormalTok{ figure} +\ImportTok{from}\NormalTok{ sklearn.linear\_model }\ImportTok{import}\NormalTok{ LinearRegression} +\ImportTok{from}\NormalTok{ statsmodels.tsa.arima.model }\ImportTok{import}\NormalTok{ ARIMA} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_squared\_error} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_absolute\_percentage\_error} +\end{Highlighting} +\end{Shaded} + +\noindent SARIMA models considered: + +\begin{itemize} +\tightlist +\item + order=(1,0,0), seasonal\_order=(0, 0, 0, 0) +\item + order=(1,0,1), seasonal\_order=(0, 0, 0, 0) +\item + order=(7,0,1), seasonal\_order=(0, 0, 0, 0) +\item + order=(7,0,7), seasonal\_order=(0, 0, 0, 0) +\item + order=(1,0,0), seasonal\_order=(1, 0, 1, 7) +\item + order=(6,0,2), seasonal\_order=(1, 0, 1, 7) +\item + order=(6,0,2), seasonal\_order=(1, 0, 2, 7) +\item + order=(6,0,1), seasonal\_order=(2, 0, 1, 7) +\item + order=(2,0,1), seasonal\_order=(2, 0, 1, 7) +\end{itemize} + +\noindent Model evaluation and tuning + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{sarimas }\OperatorTok{=}\NormalTok{ pd.DataFrame(\{}\StringTok{"order"}\NormalTok{:[(}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{0}\NormalTok{), (}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{), (}\DecValTok{7}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{), (}\DecValTok{7}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{7}\NormalTok{), } +\NormalTok{ (}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{0}\NormalTok{), (}\DecValTok{6}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{2}\NormalTok{), (}\DecValTok{6}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{2}\NormalTok{), (}\DecValTok{6}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{), } +\NormalTok{ (}\DecValTok{2}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{)],} + + \StringTok{"seasonal\_order"}\NormalTok{: [(}\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{), (}\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{), } +\NormalTok{ (}\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{), (}\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{0}\NormalTok{), } +\NormalTok{ (}\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{7}\NormalTok{), (}\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{7}\NormalTok{), } +\NormalTok{ (}\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{7}\NormalTok{), (}\DecValTok{2}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{7}\NormalTok{), } +\NormalTok{ (}\DecValTok{2}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{7}\NormalTok{)]\})} +\NormalTok{sarimas }\OperatorTok{=}\NormalTok{ sarimas.reset\_index().rename(\{}\StringTok{"index"}\NormalTok{: }\StringTok{"id"}\NormalTok{\}, axis }\OperatorTok{=} \DecValTok{1}\NormalTok{)} + +\NormalTok{columns }\OperatorTok{=}\NormalTok{ [}\StringTok{"sarima\_id"}\NormalTok{, }\StringTok{"hour\_of\_day"}\NormalTok{, }\StringTok{"order"}\NormalTok{, }\StringTok{"seasonal\_order"}\NormalTok{, } + \StringTok{"ljung\_val"}\NormalTok{, }\StringTok{"ljung\_p"}\NormalTok{, }\StringTok{"jb\_val"}\NormalTok{, }\StringTok{"jb\_p"}\NormalTok{, }\StringTok{"hetro\_val"}\NormalTok{, }\StringTok{"hetro\_p"}\NormalTok{, } + \StringTok{"skew"}\NormalTok{, }\StringTok{"kurtosis"}\NormalTok{, }\StringTok{"aic"}\NormalTok{, }\StringTok{"bic"}\NormalTok{, }\StringTok{"n\_observations"}\NormalTok{, }\StringTok{"mse\_pre"}\NormalTok{, } + \StringTok{"mse\_post"}\NormalTok{, }\StringTok{"mape"}\NormalTok{]} +\NormalTok{sarima\_tune }\OperatorTok{=}\NormalTok{ pd.DataFrame(columns }\OperatorTok{=}\NormalTok{ columns)} + +\NormalTok{period\_id }\OperatorTok{=} \DecValTok{24} + +\NormalTok{hours\_all }\OperatorTok{=}\NormalTok{ [}\DecValTok{0}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{8}\NormalTok{, }\DecValTok{12}\NormalTok{, }\DecValTok{16}\NormalTok{, }\DecValTok{20}\NormalTok{]} + +\ControlFlowTok{for}\NormalTok{ hour\_of\_day }\KeywordTok{in}\NormalTok{ hours\_all:} + \ControlFlowTok{for}\NormalTok{ index, row }\KeywordTok{in}\NormalTok{ sarimas.iterrows():} +\NormalTok{ order }\OperatorTok{=}\NormalTok{ row[}\StringTok{"order"}\NormalTok{]} +\NormalTok{ seasonal\_order }\OperatorTok{=}\NormalTok{ row[}\StringTok{"seasonal\_order"}\NormalTok{]} +\NormalTok{ sarima\_id }\OperatorTok{=}\NormalTok{ row[}\StringTok{"id"}\NormalTok{]} + +\NormalTok{ df\_all\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[(df\_all.period\_id }\OperatorTok{==}\NormalTok{ period\_id) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future\_hour }\OperatorTok{==}\NormalTok{ hour\_of\_day) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future.dt.minute }\OperatorTok{==} \DecValTok{0}\NormalTok{)].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} + + \CommentTok{\# Model fit} +\NormalTok{ model }\OperatorTok{=}\NormalTok{ ARIMA(df\_all\_delta.forecast\_error, order }\OperatorTok{=}\NormalTok{ order, } +\NormalTok{ seasonal\_order }\OperatorTok{=}\NormalTok{ seasonal\_order)} +\NormalTok{ model\_fit }\OperatorTok{=}\NormalTok{ model.fit()} +\NormalTok{ df\_all\_delta[}\StringTok{"predicted\_forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ model\_fit.fittedvalues} +\NormalTok{ df\_all\_delta[}\StringTok{"new\_forecast"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_demand }\OperatorTok{+} +\NormalTok{ df\_all\_delta.predicted\_forecast\_error} + + \CommentTok{\# Model Evaluation (MSE)} +\NormalTok{ mse\_pre }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_all\_delta.total\_demand,} +\NormalTok{ df\_all\_delta.forecast\_demand)} +\NormalTok{ mse\_post }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_all\_delta.total\_demand,} +\NormalTok{ df\_all\_delta.new\_forecast)} +\NormalTok{ mape }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(df\_all\_delta.total\_demand,} +\NormalTok{ df\_all\_delta.new\_forecast)} + + \CommentTok{\# Model Evaluation (Crit values)} +\NormalTok{ stat\_tests }\OperatorTok{=}\NormalTok{ pd.read\_html(model\_fit.summary().tables[}\DecValTok{2}\NormalTok{].as\_html(),} +\NormalTok{ header}\OperatorTok{=}\VariableTok{None}\NormalTok{,index\_col}\OperatorTok{=}\DecValTok{0}\NormalTok{)[}\DecValTok{0}\NormalTok{]} +\NormalTok{ ljung\_val, ljung\_p }\OperatorTok{=}\NormalTok{ stat\_tests[}\DecValTok{1}\NormalTok{].iloc[}\DecValTok{0}\NormalTok{], stat\_tests[}\DecValTok{1}\NormalTok{].iloc[}\DecValTok{1}\NormalTok{], } +\NormalTok{ jb\_val, jb\_p }\OperatorTok{=}\NormalTok{ stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{0}\NormalTok{], stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{1}\NormalTok{], } +\NormalTok{ hetro\_val, hetro\_p }\OperatorTok{=}\NormalTok{ stat\_tests[}\DecValTok{1}\NormalTok{].iloc[}\DecValTok{2}\NormalTok{], stat\_tests[}\DecValTok{1}\NormalTok{].iloc[}\DecValTok{3}\NormalTok{], } +\NormalTok{ skew, kurtosis }\OperatorTok{=}\NormalTok{ stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{2}\NormalTok{], stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{3}\NormalTok{]} + + \CommentTok{\# Model Evaluation (AIC, BIC)} +\NormalTok{ stat\_tests }\OperatorTok{=}\NormalTok{ pd.read\_html(model\_fit.summary().tables[}\DecValTok{0}\NormalTok{].as\_html(),} +\NormalTok{ header}\OperatorTok{=}\VariableTok{None}\NormalTok{,index\_col}\OperatorTok{=}\DecValTok{0}\NormalTok{)[}\DecValTok{0}\NormalTok{]} +\NormalTok{ aic, bic }\OperatorTok{=}\NormalTok{ stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{2}\NormalTok{], stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{3}\NormalTok{]} +\NormalTok{ n\_observations }\OperatorTok{=}\NormalTok{ stat\_tests[}\DecValTok{3}\NormalTok{].iloc[}\DecValTok{0}\NormalTok{]} +\NormalTok{ sarima\_tune }\OperatorTok{=}\NormalTok{ sarima\_tune.append(pd.DataFrame([[sarima\_id, } +\NormalTok{ hour\_of\_day, order, seasonal\_order, ljung\_val, ljung\_p, } +\NormalTok{ jb\_val, jb\_p, hetro\_val, hetro\_p, skew, kurtosis, aic, } +\NormalTok{ bic, n\_observations, mse\_pre, mse\_post, mape]], } +\NormalTok{ columns}\OperatorTok{=}\NormalTok{columns), ignore\_index}\OperatorTok{=}\VariableTok{True}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{sarima\_tune[}\StringTok{"mse\_improvement"}\NormalTok{] }\OperatorTok{=} \BuiltInTok{round}\NormalTok{(}\DecValTok{100}\OperatorTok{*}\NormalTok{(sarima\_tune.mse\_pre }\OperatorTok{{-}} +\NormalTok{ sarima\_tune.mse\_post)}\OperatorTok{/}\NormalTok{sarima\_tune.mse\_pre)} +\NormalTok{sarima\_tune }\OperatorTok{=}\NormalTok{ pd.merge(sarima\_tune, sarimas, on }\OperatorTok{=} +\NormalTok{ [}\StringTok{"order"}\NormalTok{, }\StringTok{"seasonal\_order"}\NormalTok{], how }\OperatorTok{=} \StringTok{"left"}\NormalTok{).sort\_values(}\StringTok{"id"}\NormalTok{)} + +\NormalTok{plot }\OperatorTok{=}\NormalTok{ sarima\_tune.groupby([}\StringTok{"order"}\NormalTok{, }\StringTok{"seasonal\_order"}\NormalTok{, }\StringTok{"hour\_of\_day"}\NormalTok{], } +\NormalTok{ as\_index }\OperatorTok{=} \VariableTok{False}\NormalTok{).mean()} +\NormalTok{sns.lineplot(data }\OperatorTok{=}\NormalTok{ plot, x }\OperatorTok{=} \StringTok{\textquotesingle{}hour\_of\_day\textquotesingle{}}\NormalTok{, y }\OperatorTok{=} \StringTok{\textquotesingle{}mape\textquotesingle{}}\NormalTok{, hue }\OperatorTok{=} \StringTok{\textquotesingle{}id\textquotesingle{}}\NormalTok{, } +\NormalTok{ palette }\OperatorTok{=} \StringTok{\textquotesingle{}pastel\textquotesingle{}}\NormalTok{, alpha }\OperatorTok{=} \DecValTok{1}\NormalTok{, linestyle }\OperatorTok{=} \StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{sarima\_tune[}\StringTok{"mse\_improvement"}\NormalTok{] }\OperatorTok{=} \BuiltInTok{round}\NormalTok{(}\DecValTok{100}\OperatorTok{*}\NormalTok{(sarima\_tune.mse\_pre }\OperatorTok{{-}} +\NormalTok{ sarima\_tune.mse\_post)}\OperatorTok{/}\NormalTok{sarima\_tune.mse\_pre)} + +\NormalTok{plot }\OperatorTok{=}\NormalTok{ sarima\_tune.groupby([}\StringTok{"order"}\NormalTok{, }\StringTok{"seasonal\_order"}\NormalTok{, }\StringTok{"hour\_of\_day"}\NormalTok{], } +\NormalTok{ as\_index }\OperatorTok{=} \VariableTok{False}\NormalTok{).mean()} +\NormalTok{sns.lineplot(data }\OperatorTok{=}\NormalTok{ plot, x }\OperatorTok{=} \StringTok{\textquotesingle{}hour\_of\_day\textquotesingle{}}\NormalTok{, y }\OperatorTok{=} \StringTok{\textquotesingle{}mse\_improvement\textquotesingle{}}\NormalTok{, } +\NormalTok{ hue }\OperatorTok{=} \StringTok{\textquotesingle{}id\textquotesingle{}}\NormalTok{, palette }\OperatorTok{=} \StringTok{\textquotesingle{}pastel\textquotesingle{}}\NormalTok{, alpha }\OperatorTok{=} \DecValTok{1}\NormalTok{, linestyle }\OperatorTok{=} \StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\noindent Parameters chosen + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{period\_id }\OperatorTok{=} \DecValTok{24} +\NormalTok{arima\_order }\OperatorTok{=}\NormalTok{ (}\DecValTok{6}\NormalTok{,}\DecValTok{0}\NormalTok{,}\DecValTok{2}\NormalTok{)} +\NormalTok{arima\_season\_order }\OperatorTok{=}\NormalTok{ (}\DecValTok{1}\NormalTok{, }\DecValTok{0}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{7}\NormalTok{)} + +\NormalTok{train\_test\_split }\OperatorTok{=} \FloatTok{0.7} +\end{Highlighting} +\end{Shaded} + +\noindent textbf\{SARIMA Model 1 - Without Exogenous Variables\} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_predict }\OperatorTok{=}\NormalTok{ pd.DataFrame(columns }\OperatorTok{=}\NormalTok{ [}\StringTok{"period\_id"}\NormalTok{, }\StringTok{"date\_time\_future"}\NormalTok{, } + \StringTok{"new\_forecast"}\NormalTok{, }\StringTok{"forecast\_demand"}\NormalTok{, }\StringTok{"total\_demand"}\NormalTok{])} + +\ControlFlowTok{for}\NormalTok{ hour\_of\_day }\KeywordTok{in} \BuiltInTok{set}\NormalTok{(df\_all.date\_time\_future\_hour):} +\NormalTok{ df\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[(df\_all.period\_id }\OperatorTok{==}\NormalTok{ period\_id) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future\_hour }\OperatorTok{==}\NormalTok{ hour\_of\_day) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future.dt.minute }\OperatorTok{==} \DecValTok{0}\NormalTok{)].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} + + \CommentTok{\# Test/Train split} +\NormalTok{ split\_int }\OperatorTok{=} \BuiltInTok{int}\NormalTok{(train\_test\_split }\OperatorTok{*} \BuiltInTok{len}\NormalTok{(df\_delta))} +\NormalTok{ df\_delta\_train, df\_delta\_test }\OperatorTok{=} +\NormalTok{ df\_delta[:split\_int], df\_delta[split\_int:]} +\NormalTok{ x\_all, x\_train, x\_test }\OperatorTok{=}\NormalTok{ df\_delta.forecast\_error, } +\NormalTok{ df\_delta\_train.forecast\_error, df\_delta\_test.forecast\_error} + + \CommentTok{\# Model {-} Train Data} +\NormalTok{ arima\_model\_train }\OperatorTok{=}\NormalTok{ ARIMA(x\_train, order }\OperatorTok{=}\NormalTok{ arima\_order, } +\NormalTok{ seasonal\_order }\OperatorTok{=}\NormalTok{ arima\_season\_order)} +\NormalTok{ arima\_mode\_train\_fit }\OperatorTok{=}\NormalTok{ arima\_model\_train.fit()} + + \CommentTok{\# Model {-} Test Data} +\NormalTok{ arima\_model\_test }\OperatorTok{=}\NormalTok{ ARIMA(x\_all, order }\OperatorTok{=}\NormalTok{ arima\_order, } +\NormalTok{ seasonal\_order }\OperatorTok{=}\NormalTok{ arima\_season\_order)} +\NormalTok{ arima\_model\_test\_fit }\OperatorTok{=}\NormalTok{ arima\_model\_test.}\BuiltInTok{filter}\NormalTok{(} +\NormalTok{ arima\_mode\_train\_fit.params)} + + \CommentTok{\# Predicted Values} +\NormalTok{ arima\_model\_test\_predict }\OperatorTok{=} +\NormalTok{ arima\_model\_test\_fit.predict().loc[split\_int:]} + + \CommentTok{\# Calculate new forecast} +\NormalTok{ df\_delta\_test[}\StringTok{"predicted\_forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ arima\_model\_test\_predict} +\NormalTok{ df\_delta\_test[}\StringTok{"new\_forecast"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_delta\_test.forecast\_demand }\OperatorTok{+} +\NormalTok{ df\_delta\_test.predicted\_forecast\_error} + + \CommentTok{\# Model evaluation} +\NormalTok{ mse\_pre }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.forecast\_demand)} +\NormalTok{ mse\_post }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.new\_forecast)} +\NormalTok{ mape\_pre }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.forecast\_demand)} +\NormalTok{ mape\_post }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.new\_forecast)} + +\NormalTok{ df\_predict }\OperatorTok{=}\NormalTok{ pd.concat([df\_predict, df\_delta\_test[[}\StringTok{"period\_id"}\NormalTok{, } + \StringTok{"date\_time\_future"}\NormalTok{, }\StringTok{"new\_forecast"}\NormalTok{, }\StringTok{"forecast\_demand"}\NormalTok{, } + \StringTok{"total\_demand"}\NormalTok{]]])} +\end{Highlighting} +\end{Shaded} + +\noindent textbf\{SARIMA Model 2 - With Exogenous Variables\} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_predict\_with\_exog }\OperatorTok{=}\NormalTok{ pd.DataFrame(columns }\OperatorTok{=}\NormalTok{ [}\StringTok{"period\_id"}\NormalTok{,} + \StringTok{"date\_time\_future"}\NormalTok{, }\StringTok{"new\_forecast"}\NormalTok{, }\StringTok{"forecast\_demand"}\NormalTok{, } + \StringTok{"total\_demand"}\NormalTok{])} +\NormalTok{exog\_vars }\OperatorTok{=}\NormalTok{ [}\StringTok{"Temperature"}\NormalTok{, }\StringTok{"Humidity"}\NormalTok{, }\StringTok{"Wind\_speed"}\NormalTok{, }\StringTok{"Rain"}\NormalTok{]} + +\ControlFlowTok{for}\NormalTok{ hour\_of\_day }\KeywordTok{in} \BuiltInTok{set}\NormalTok{(df\_all.date\_time\_future\_hour):} +\NormalTok{ df\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[(df\_all.period\_id }\OperatorTok{==}\NormalTok{ period\_id) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future\_hour }\OperatorTok{==}\NormalTok{ hour\_of\_day) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future.dt.minute }\OperatorTok{==} \DecValTok{0}\NormalTok{)].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} + + \CommentTok{\# Test/Train split} +\NormalTok{ split\_int }\OperatorTok{=} \BuiltInTok{int}\NormalTok{(train\_test\_split }\OperatorTok{*} \BuiltInTok{len}\NormalTok{(df\_delta))} +\NormalTok{ df\_delta\_train, df\_delta\_test }\OperatorTok{=} +\NormalTok{ df\_delta[:split\_int], df\_delta[split\_int:]} +\NormalTok{ x\_all, x\_train, x\_test }\OperatorTok{=} +\NormalTok{ df\_delta.forecast\_error, df\_delta\_train.forecast\_error, } +\NormalTok{ df\_delta\_test.forecast\_error} +\NormalTok{ exog\_all, exog\_train, exog\_test }\OperatorTok{=} +\NormalTok{ df\_delta[exog\_vars], df\_delta\_train[exog\_vars], } +\NormalTok{ df\_delta\_test[exog\_vars]} + + \CommentTok{\# Model {-} Train Data} +\NormalTok{ arima\_model\_train }\OperatorTok{=}\NormalTok{ ARIMA(x\_train, exog }\OperatorTok{=}\NormalTok{ exog\_train, } +\NormalTok{ order }\OperatorTok{=}\NormalTok{ arima\_order, seasonal\_order }\OperatorTok{=}\NormalTok{ arima\_season\_order)} +\NormalTok{ arima\_mode\_train\_fit }\OperatorTok{=}\NormalTok{ arima\_model\_train.fit()} + + \CommentTok{\# Model {-} Test Data} +\NormalTok{ arima\_model\_test }\OperatorTok{=}\NormalTok{ ARIMA(x\_all, exog }\OperatorTok{=}\NormalTok{ exog\_all, } +\NormalTok{ order }\OperatorTok{=}\NormalTok{ arima\_order, } +\NormalTok{ seasonal\_order }\OperatorTok{=}\NormalTok{ arima\_season\_order)} +\NormalTok{ arima\_model\_test\_fit }\OperatorTok{=} +\NormalTok{ arima\_model\_test.}\BuiltInTok{filter}\NormalTok{(arima\_mode\_train\_fit.params) } + + \CommentTok{\# Predicted Values} +\NormalTok{ arima\_model\_test\_predict }\OperatorTok{=} +\NormalTok{ arima\_model\_test\_fit.predict().loc[split\_int:]} + + \CommentTok{\# Calculate new forecast} +\NormalTok{ df\_delta\_test[}\StringTok{"predicted\_forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ arima\_model\_test\_predict} +\NormalTok{ df\_delta\_test[}\StringTok{"new\_forecast"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_delta\_test.forecast\_demand } + \OperatorTok{+}\NormalTok{ df\_delta\_test.predicted\_forecast\_error} + + \CommentTok{\# Model evaluation} +\NormalTok{ mse\_pre }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.forecast\_demand)} +\NormalTok{ mse\_post }\OperatorTok{=}\NormalTok{ mean\_squared\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.new\_forecast)} +\NormalTok{ mape\_pre }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.forecast\_demand)} +\NormalTok{ mape\_post }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(df\_delta\_test.total\_demand,} +\NormalTok{ df\_delta\_test.new\_forecast)} + +\NormalTok{ df\_predict\_with\_exog }\OperatorTok{=}\NormalTok{ pd.concat([df\_predict\_with\_exog, } +\NormalTok{ df\_delta\_test[[}\StringTok{"period\_id"}\NormalTok{, }\StringTok{"date\_time\_future"}\NormalTok{, } + \StringTok{"new\_forecast"}\NormalTok{, }\StringTok{"forecast\_demand"}\NormalTok{, }\StringTok{"total\_demand"}\NormalTok{]]])} +\end{Highlighting} +\end{Shaded} + +Model evaluation + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_predict[}\StringTok{"forecast\_error\_old"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_predict.total\_demand }\OperatorTok{{-}}\NormalTok{ df\_predict.forecast\_demand} +\NormalTok{df\_predict[}\StringTok{"forecast\_error\_new"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_predict.total\_demand }\OperatorTok{{-}}\NormalTok{ df\_predict.new\_forecast} +\NormalTok{df\_predict\_with\_exog[}\StringTok{"forecast\_error\_new"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_predict\_with\_exog.total\_demand }\OperatorTok{{-}}\NormalTok{ df\_predict\_with\_exog.new\_forecast} + +\NormalTok{mse\_pre }\OperatorTok{=}\NormalTok{ mean\_squared\_error(} +\NormalTok{ df\_predict.total\_demand, df\_predict.forecast\_demand)} +\NormalTok{mse\_sarima }\OperatorTok{=}\NormalTok{ mean\_squared\_error(} +\NormalTok{ df\_predict.total\_demand, df\_predict.new\_forecast)} +\NormalTok{mse\_sarima\_with\_exog }\OperatorTok{=}\NormalTok{ mean\_squared\_error(} +\NormalTok{ df\_predict\_with\_exog.total\_demand, } +\NormalTok{ df\_predict\_with\_exog.new\_forecast)} + +\NormalTok{mape\_pre }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(} +\NormalTok{ df\_predict.total\_demand, df\_predict.forecast\_demand)} +\NormalTok{mape\_sarima }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(} +\NormalTok{ df\_predict.total\_demand, df\_predict.new\_forecast)} +\NormalTok{mape\_sarima\_with\_exog }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(} +\NormalTok{ df\_predict\_with\_exog.total\_demand, } +\NormalTok{ df\_predict\_with\_exog.new\_forecast)} +\end{Highlighting} +\end{Shaded} + +\subsection*{B3. Random Forest}\label{b3.-random-forest} + +Import packages + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{from}\NormalTok{ sklearn.ensemble }\ImportTok{import}\NormalTok{ RandomForestRegressor} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_absolute\_error, mean\_squared\_error} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} +\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt} +\ImportTok{import}\NormalTok{ seaborn }\ImportTok{as}\NormalTok{ sns} +\end{Highlighting} +\end{Shaded} + +\noindent Data Processing + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\# Filter for PERIODID 24 and sort} +\NormalTok{df\_sliced }\OperatorTok{=}\NormalTok{ df[df[}\StringTok{"period\_id"}\NormalTok{] }\OperatorTok{==} \DecValTok{24}\NormalTok{].copy()} +\NormalTok{df\_sliced }\OperatorTok{=}\NormalTok{ df\_sliced.sort\_values(}\StringTok{"date\_time\_future"}\NormalTok{)} +\NormalTok{df\_sliced }\OperatorTok{=}\NormalTok{ df\_sliced.dropna()} + +\CommentTok{\# Get forecast error} +\NormalTok{df\_sliced[}\StringTok{\textquotesingle{}forecast\_error\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_sliced[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{] }\OperatorTok{{-}} +\NormalTok{ df\_sliced[}\StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{]} +\NormalTok{df\_sliced[}\StringTok{\textquotesingle{}forecast\_error\_lag24h\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_sliced.sort\_values(} + \StringTok{\textquotesingle{}date\_time\_current\_rounded\textquotesingle{}}\NormalTok{)[}\StringTok{\textquotesingle{}forecast\_error\textquotesingle{}}\NormalTok{].shift(}\DecValTok{24}\NormalTok{)} + +\CommentTok{\# Check for NaN counts in key columns} +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{NaN counts in key columns:"}\NormalTok{)} +\ControlFlowTok{for}\NormalTok{ col }\KeywordTok{in}\NormalTok{ [}\StringTok{\textquotesingle{}demand\_lag\_24h\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}demand\_lag\_48h\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}demand\_lag\_7d\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}forecast\_error\_lag12h\textquotesingle{}}\NormalTok{]:} + \ControlFlowTok{if}\NormalTok{ col }\KeywordTok{in}\NormalTok{ df\_sliced.columns:} + \BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"}\SpecialCharTok{\{}\NormalTok{col}\SpecialCharTok{\}}\SpecialStringTok{: }\SpecialCharTok{\{}\NormalTok{df\_sliced[col]}\SpecialCharTok{.}\NormalTok{isna()}\SpecialCharTok{.}\BuiltInTok{sum}\NormalTok{()}\SpecialCharTok{\}}\SpecialStringTok{ NaNs"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\noindent \textbf{RFMF1 - Random Forest Model 1} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\# Define features that would be available at prediction } +\CommentTok{\# time (12 hours ahead)} +\NormalTok{features }\OperatorTok{=}\NormalTok{ [} + \CommentTok{\# Basic time features} + \CommentTok{\# Original forecast} + \StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}forecast\_error\_lag24h\textquotesingle{}} +\NormalTok{]} +\CommentTok{\# Define target} +\NormalTok{target }\OperatorTok{=} \StringTok{\textquotesingle{}total\_demand\textquotesingle{}} + +\CommentTok{\# Create the modeling dataframe} +\NormalTok{model\_df }\OperatorTok{=}\NormalTok{ df\_sliced[features }\OperatorTok{+}\NormalTok{ [target] }\OperatorTok{+} +\NormalTok{ [}\StringTok{\textquotesingle{}date\_time\_current\_rounded\textquotesingle{}}\NormalTok{]].copy()} + +\CommentTok{\# Print the shape before dropping missing values} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"}\CharTok{\textbackslash{}n}\SpecialStringTok{Shape before dropping missing values: }\SpecialCharTok{\{}\NormalTok{model\_df}\SpecialCharTok{.}\NormalTok{shape}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} + +\CommentTok{\# Drop rows with NaN values} +\NormalTok{model\_df }\OperatorTok{=}\NormalTok{ model\_df.dropna()} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Shape after dropping NaN values: }\SpecialCharTok{\{}\NormalTok{model\_df}\SpecialCharTok{.}\NormalTok{shape}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} + +\CommentTok{\# If we still have no data, show a clear error and exit} +\ControlFlowTok{if} \BuiltInTok{len}\NormalTok{(model\_df) }\OperatorTok{==} \DecValTok{0}\NormalTok{:} + \BuiltInTok{print}\NormalTok{(}\StringTok{"ERROR: No data left after dropping NaN values!"}\NormalTok{)} + \ImportTok{import}\NormalTok{ sys} +\NormalTok{ sys.exit(}\DecValTok{1}\NormalTok{)} + +\CommentTok{\# Sort data to ensure temporal order} +\NormalTok{model\_df }\OperatorTok{=}\NormalTok{ model\_df.sort\_values(} + \StringTok{\textquotesingle{}date\_time\_current\_rounded\textquotesingle{}}\NormalTok{).reset\_index(drop}\OperatorTok{=}\VariableTok{True}\NormalTok{)} + +\CommentTok{\# Split data temporally {-} use 70{-}30 split} +\NormalTok{X }\OperatorTok{=}\NormalTok{ model\_df[features]} +\NormalTok{y }\OperatorTok{=}\NormalTok{ model\_df[target]} +\NormalTok{train\_size }\OperatorTok{=} \FloatTok{0.7} +\NormalTok{split\_idx }\OperatorTok{=} \BuiltInTok{int}\NormalTok{(}\BuiltInTok{len}\NormalTok{(model\_df) }\OperatorTok{*}\NormalTok{ train\_size)} + +\CommentTok{\# Split into train/test} +\NormalTok{X\_train }\OperatorTok{=}\NormalTok{ X.iloc[:split\_idx]} +\NormalTok{y\_train }\OperatorTok{=}\NormalTok{ y.iloc[:split\_idx]} +\NormalTok{X\_test }\OperatorTok{=}\NormalTok{ X.iloc[split\_idx:]} +\NormalTok{y\_test }\OperatorTok{=}\NormalTok{ y.iloc[split\_idx:]} + +\CommentTok{\# Train model} +\NormalTok{model }\OperatorTok{=}\NormalTok{ RandomForestRegressor(} +\NormalTok{ n\_estimators}\OperatorTok{=}\DecValTok{200}\NormalTok{,} +\NormalTok{ max\_depth}\OperatorTok{=}\DecValTok{10}\NormalTok{,} +\NormalTok{ min\_samples\_split}\OperatorTok{=}\DecValTok{5}\NormalTok{,} +\NormalTok{ min\_samples\_leaf}\OperatorTok{=}\DecValTok{2}\NormalTok{,} +\NormalTok{ random\_state}\OperatorTok{=}\DecValTok{42}\NormalTok{,} +\NormalTok{ n\_jobs}\OperatorTok{={-}}\DecValTok{1} +\NormalTok{)} +\NormalTok{model.fit(X\_train, y\_train)} + +\CommentTok{\# Make predictions} +\NormalTok{y\_pred }\OperatorTok{=}\NormalTok{ model.predict(X\_test)} +\NormalTok{y\_pred\_original }\OperatorTok{=}\NormalTok{ X\_test[}\StringTok{"forecast\_demand"}\NormalTok{]} +\end{Highlighting} +\end{Shaded} + +\noindent Evaluate performance + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\# Evaluate performance} +\KeywordTok{def}\NormalTok{ calculate\_metrics(y\_true, y\_pred):} +\NormalTok{ mse }\OperatorTok{=}\NormalTok{ mean\_squared\_error(y\_true, y\_pred)} +\NormalTok{ mape }\OperatorTok{=}\NormalTok{ np.mean(} +\NormalTok{ np.}\BuiltInTok{abs}\NormalTok{((y\_true }\OperatorTok{{-}}\NormalTok{ y\_pred) }\OperatorTok{/}\NormalTok{ np.maximum(}\FloatTok{0.001}\NormalTok{, y\_true))) }\OperatorTok{*} \DecValTok{100} + \ControlFlowTok{return}\NormalTok{ mse, mape} + +\CommentTok{\# Model metrics} +\NormalTok{model\_mse, model\_mape }\OperatorTok{=}\NormalTok{ calculate\_metrics(y\_test, y\_pred)} + +\CommentTok{\# Original forecast metrics} +\NormalTok{original\_mse, original\_mape }\OperatorTok{=}\NormalTok{ calculate\_metrics(y\_test, y\_pred\_original)} + +\CommentTok{\# Print formatted results} +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Model Performance:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MSE: }\SpecialCharTok{\{}\NormalTok{model\_mse}\SpecialCharTok{:.3f\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MAPE: }\SpecialCharTok{\{}\NormalTok{model\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Original Forecast Performance:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MSE: }\SpecialCharTok{\{}\NormalTok{original\_mse}\SpecialCharTok{:.3f\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MAPE: }\SpecialCharTok{\{}\NormalTok{original\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + +\CommentTok{\# Calculate improvement percentages} +\NormalTok{improvement\_mse }\OperatorTok{=}\NormalTok{ (}\DecValTok{1} \OperatorTok{{-}}\NormalTok{ model\_mse}\OperatorTok{/}\NormalTok{original\_mse) }\OperatorTok{*} \DecValTok{100} +\NormalTok{improvement\_mape }\OperatorTok{=}\NormalTok{ (}\DecValTok{1} \OperatorTok{{-}}\NormalTok{ model\_mape}\OperatorTok{/}\NormalTok{original\_mape) }\OperatorTok{*} \DecValTok{100} + +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Improvement Over Original Forecast:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MSE: }\SpecialCharTok{\{}\NormalTok{improvement\_mse}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MAPE: }\SpecialCharTok{\{}\NormalTok{improvement\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + +\CommentTok{\# Feature importance} +\NormalTok{feature\_importance }\OperatorTok{=}\NormalTok{ pd.DataFrame(} +\NormalTok{ \{}\StringTok{\textquotesingle{}Feature\textquotesingle{}}\NormalTok{: features,} + \StringTok{\textquotesingle{}Importance\textquotesingle{}}\NormalTok{: model.feature\_importances\_\}} +\NormalTok{).sort\_values(}\StringTok{\textquotesingle{}Importance\textquotesingle{}}\NormalTok{, ascending}\OperatorTok{=}\VariableTok{False}\NormalTok{)} + +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Feature Importance:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(feature\_importance)} +\end{Highlighting} +\end{Shaded} + +\noindent \textbf{RFMF2 - Random Forest Model 2} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\# Define features that would be available at prediction } +\CommentTok{\# time (12 hours ahead)} +\NormalTok{features }\OperatorTok{=}\NormalTok{ [} + \StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}Temperature\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}Humidity\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}Wind\_speed\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}Rain\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}forecast\_error\_lag24h\textquotesingle{}} +\NormalTok{]} + + +\CommentTok{\# Define target} +\NormalTok{target }\OperatorTok{=} \StringTok{\textquotesingle{}total\_demand\textquotesingle{}} + + +\CommentTok{\# Print the number of NaN values for each feature} +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{NaN counts in features:"}\NormalTok{)} +\ControlFlowTok{for}\NormalTok{ feature }\KeywordTok{in}\NormalTok{ features:} + \BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"}\SpecialCharTok{\{}\NormalTok{feature}\SpecialCharTok{\}}\SpecialStringTok{: }\SpecialCharTok{\{}\NormalTok{df\_sliced[feature]}\SpecialCharTok{.}\NormalTok{isna()}\SpecialCharTok{.}\BuiltInTok{sum}\NormalTok{()}\SpecialCharTok{\}}\SpecialStringTok{ NaNs"}\NormalTok{)} + +\CommentTok{\# Create the modeling dataframe} +\NormalTok{model\_df }\OperatorTok{=}\NormalTok{ df\_sliced[features }\OperatorTok{+}\NormalTok{ [target] ].copy()} +\CommentTok{\# Drop rows with NaN values} +\NormalTok{model\_df }\OperatorTok{=}\NormalTok{ model\_df.dropna()} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Shape after dropping NaN values: }\SpecialCharTok{\{}\NormalTok{model\_df}\SpecialCharTok{.}\NormalTok{shape}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} + + +\CommentTok{\# Split data temporally {-} using 70{-}30 split} +\NormalTok{X }\OperatorTok{=}\NormalTok{ model\_df[features]} +\NormalTok{y }\OperatorTok{=}\NormalTok{ model\_df[target]} +\NormalTok{train\_size }\OperatorTok{=} \FloatTok{0.7} +\NormalTok{split\_idx }\OperatorTok{=} \BuiltInTok{int}\NormalTok{(}\BuiltInTok{len}\NormalTok{(model\_df) }\OperatorTok{*}\NormalTok{ train\_size)} + +\CommentTok{\# Split into train/test} +\NormalTok{X\_train }\OperatorTok{=}\NormalTok{ X.iloc[:split\_idx]} +\NormalTok{y\_train }\OperatorTok{=}\NormalTok{ y.iloc[:split\_idx]} +\NormalTok{X\_test }\OperatorTok{=}\NormalTok{ X.iloc[split\_idx:]} +\NormalTok{y\_test }\OperatorTok{=}\NormalTok{ y.iloc[split\_idx:]} + + +\CommentTok{\# Train model} +\NormalTok{model }\OperatorTok{=}\NormalTok{ RandomForestRegressor(} +\NormalTok{ n\_estimators}\OperatorTok{=}\DecValTok{200}\NormalTok{,} +\NormalTok{ max\_depth}\OperatorTok{=}\DecValTok{10}\NormalTok{,} +\NormalTok{ min\_samples\_split}\OperatorTok{=}\DecValTok{5}\NormalTok{,} +\NormalTok{ min\_samples\_leaf}\OperatorTok{=}\DecValTok{2}\NormalTok{,} +\NormalTok{ random\_state}\OperatorTok{=}\DecValTok{42}\NormalTok{,} +\NormalTok{ n\_jobs}\OperatorTok{={-}}\DecValTok{1} +\NormalTok{)} +\NormalTok{model.fit(X\_train, y\_train)} + +\CommentTok{\# Make predictions} +\NormalTok{y\_pred }\OperatorTok{=}\NormalTok{ model.predict(X\_test)} +\NormalTok{y\_pred\_original }\OperatorTok{=}\NormalTok{ X\_test[}\StringTok{"forecast\_demand"}\NormalTok{]} +\end{Highlighting} +\end{Shaded} + +\noindent Evaluate performance + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{def}\NormalTok{ calculate\_metrics(y\_true, y\_pred):} +\NormalTok{ mse }\OperatorTok{=}\NormalTok{ mean\_squared\_error(y\_true, y\_pred)} +\NormalTok{ mape }\OperatorTok{=}\NormalTok{ np.mean(} +\NormalTok{ np.}\BuiltInTok{abs}\NormalTok{((y\_true }\OperatorTok{{-}}\NormalTok{ y\_pred) }\OperatorTok{/}\NormalTok{ np.maximum(}\FloatTok{0.001}\NormalTok{, y\_true))) }\OperatorTok{*} \DecValTok{100} + \ControlFlowTok{return}\NormalTok{ mse, mape} + +\CommentTok{\# Model metrics} +\NormalTok{model\_mse, model\_mape }\OperatorTok{=}\NormalTok{ calculate\_metrics(y\_test, y\_pred)} + +\CommentTok{\# Original forecast metrics} +\NormalTok{original\_mse, original\_mape }\OperatorTok{=}\NormalTok{ calculate\_metrics(y\_test, y\_pred\_original)} + +\CommentTok{\# Print formatted results} +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Model Performance:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MSE: }\SpecialCharTok{\{}\NormalTok{model\_mse}\SpecialCharTok{:.3f\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MAPE: }\SpecialCharTok{\{}\NormalTok{model\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Original Forecast Performance:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MSE: }\SpecialCharTok{\{}\NormalTok{original\_mse}\SpecialCharTok{:.3f\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MAPE: }\SpecialCharTok{\{}\NormalTok{original\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + +\CommentTok{\# Calculate improvement percentages} +\NormalTok{improvement\_mse }\OperatorTok{=}\NormalTok{ (}\DecValTok{1} \OperatorTok{{-}}\NormalTok{ model\_mse}\OperatorTok{/}\NormalTok{original\_mse) }\OperatorTok{*} \DecValTok{100} +\NormalTok{improvement\_mape }\OperatorTok{=}\NormalTok{ (}\DecValTok{1} \OperatorTok{{-}}\NormalTok{ model\_mape}\OperatorTok{/}\NormalTok{original\_mape) }\OperatorTok{*} \DecValTok{100} + +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Improvement Over Original Forecast:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MSE: }\SpecialCharTok{\{}\NormalTok{improvement\_mse}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"{-} MAPE: }\SpecialCharTok{\{}\NormalTok{improvement\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + +\CommentTok{\# Feature importance} +\NormalTok{feature\_importance }\OperatorTok{=}\NormalTok{ pd.DataFrame(} +\NormalTok{ \{}\StringTok{\textquotesingle{}Feature\textquotesingle{}}\NormalTok{: features,} + \StringTok{\textquotesingle{}Importance\textquotesingle{}}\NormalTok{: model.feature\_importances\_\}} +\NormalTok{).sort\_values(}\StringTok{\textquotesingle{}Importance\textquotesingle{}}\NormalTok{, ascending}\OperatorTok{=}\VariableTok{False}\NormalTok{)} + +\BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}n}\StringTok{Feature Importance:"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(feature\_importance)} +\end{Highlighting} +\end{Shaded} + +\subsection*{B4. XGBoost}\label{b4.-xgboost} + +Import packages + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} +\ImportTok{from}\NormalTok{ sklearn.model\_selection }\ImportTok{import}\NormalTok{ RandomizedSearchCV, TimeSeriesSplit} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_squared\_error} +\ImportTok{from}\NormalTok{ sklearn.metrics }\ImportTok{import}\NormalTok{ mean\_absolute\_percentage\_error} +\ImportTok{import}\NormalTok{ xgboost }\ImportTok{as}\NormalTok{ xgb} +\ImportTok{import}\NormalTok{ shap} +\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt} +\end{Highlighting} +\end{Shaded} + +\noindent Data Processing + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df[}\StringTok{\textquotesingle{}24hrpreverrors\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{\textquotesingle{}forecast\_error\textquotesingle{}}\NormalTok{].shift(}\DecValTok{24}\NormalTok{)} +\NormalTok{df[}\StringTok{\textquotesingle{}48hrpreverrors\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{\textquotesingle{}forecast\_error\textquotesingle{}}\NormalTok{].shift(}\DecValTok{48}\NormalTok{)} +\NormalTok{df[}\StringTok{\textquotesingle{}7daypreverrors\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{\textquotesingle{}forecast\_error\textquotesingle{}}\NormalTok{].shift(}\DecValTok{24} \OperatorTok{*} \DecValTok{7}\NormalTok{)} +\NormalTok{df[}\StringTok{\textquotesingle{}14daypreverrors\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{\textquotesingle{}forecast\_error\textquotesingle{}}\NormalTok{].shift(}\DecValTok{24} \OperatorTok{*} \DecValTok{14}\NormalTok{)} +\CommentTok{\# Time{-}based features} +\NormalTok{df[}\StringTok{"Hour"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df.date\_time\_future.dt.hour} +\NormalTok{df[}\StringTok{"MonthNumb"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df.date\_time\_future.dt.month} +\NormalTok{df[}\StringTok{"Day of week"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df.date\_time\_future.dt.dayofweek} + +\NormalTok{df }\OperatorTok{=}\NormalTok{ df.dropna()} + + + +\CommentTok{\# Encode Hour as cyclic features} +\NormalTok{df[}\StringTok{"hour\_sin"}\NormalTok{] }\OperatorTok{=}\NormalTok{ np.sin(}\DecValTok{2} \OperatorTok{*}\NormalTok{ np.pi }\OperatorTok{*}\NormalTok{ df[}\StringTok{"Hour"}\NormalTok{] }\OperatorTok{/} \DecValTok{24}\NormalTok{)} +\NormalTok{df[}\StringTok{"hour\_cos"}\NormalTok{] }\OperatorTok{=}\NormalTok{ np.cos(}\DecValTok{2} \OperatorTok{*}\NormalTok{ np.pi }\OperatorTok{*}\NormalTok{ df[}\StringTok{"Hour"}\NormalTok{] }\OperatorTok{/} \DecValTok{24}\NormalTok{)} + +\CommentTok{\# Interaction features} +\NormalTok{df[}\StringTok{"hour\_x\_temp"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"Hour"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"Temperature"}\NormalTok{]} +\NormalTok{df[}\StringTok{"month\_x\_temp"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"MonthNumb"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"Temperature"}\NormalTok{]} +\NormalTok{df[}\StringTok{"hour\_x\_forecast"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"Hour"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"forecast\_demand"}\NormalTok{]} +\NormalTok{df[}\StringTok{"temp\_x\_forecast"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"Temperature"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"forecast\_demand"}\NormalTok{]} +\NormalTok{df[}\StringTok{"temp\_x\_hour\_sin"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"Temperature"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"hour\_sin"}\NormalTok{]} +\NormalTok{df[}\StringTok{"temp\_x\_hour\_cos"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"Temperature"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"hour\_cos"}\NormalTok{]} +\NormalTok{df[}\StringTok{"forecast\_x\_hour\_sin"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"forecast\_demand"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"hour\_sin"}\NormalTok{]} +\NormalTok{df[}\StringTok{"forecast\_x\_hour\_cos"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"forecast\_demand"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"hour\_cos"}\NormalTok{]} +\NormalTok{df[}\StringTok{"forecast\_24\_hour\_cos"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"24hrpreverrors"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"hour\_cos"}\NormalTok{]} +\NormalTok{df[}\StringTok{"forecast\_24\_hour\_sin"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df[}\StringTok{"24hrpreverrors"}\NormalTok{] }\OperatorTok{*}\NormalTok{ df[}\StringTok{"hour\_sin"}\NormalTok{]} +\end{Highlighting} +\end{Shaded} + +\noindent Parameter Selection + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{features }\OperatorTok{=}\NormalTok{ [} + \StringTok{\textquotesingle{}Temperature\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}Humidity\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}Wind\_speed\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}Rain\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}hour\_sin\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}hour\_cos\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}MonthNumb\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}Day of week\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}24hrpreverrors\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}48hrpreverrors\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}7daypreverrors\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}14daypreverrors\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}hour\_x\_temp\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}month\_x\_temp\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}hour\_x\_forecast\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}temp\_x\_forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}temp\_x\_hour\_sin\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}temp\_x\_hour\_cos\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}forecast\_x\_hour\_sin\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}forecast\_x\_hour\_cos\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}forecast\_24\_hour\_cos\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}forecast\_24\_hour\_sin\textquotesingle{}} + +\NormalTok{]} + +\NormalTok{train\_df }\OperatorTok{=}\NormalTok{ df[(df[}\StringTok{\textquotesingle{}date\_time\_future\textquotesingle{}}\NormalTok{] }\OperatorTok{\textgreater{}=} \StringTok{"2017{-}10{-}07 23:00:00"}\NormalTok{) }\OperatorTok{\&} +\NormalTok{ (df[}\StringTok{\textquotesingle{}date\_time\_future\textquotesingle{}}\NormalTok{] }\OperatorTok{\textless{}=} \StringTok{"2020{-}03{-}05 23:00:00"}\NormalTok{)]} +\NormalTok{test\_df }\OperatorTok{=}\NormalTok{ df[(df[}\StringTok{\textquotesingle{}date\_time\_future\textquotesingle{}}\NormalTok{] }\OperatorTok{\textgreater{}} \StringTok{"2020{-}03{-}06 23:00:00"}\NormalTok{) }\OperatorTok{\&} +\NormalTok{ (df[}\StringTok{\textquotesingle{}date\_time\_future\textquotesingle{}}\NormalTok{] }\OperatorTok{\textless{}=} \StringTok{"2021{-}03{-}17 23:00:00"}\NormalTok{)]} + +\CommentTok{\# Prepare train/test split sets} +\NormalTok{X\_train }\OperatorTok{=}\NormalTok{ train\_df[features]} +\NormalTok{y\_train }\OperatorTok{=}\NormalTok{ train\_df[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]} +\NormalTok{X\_test }\OperatorTok{=}\NormalTok{ test\_df[features]} +\NormalTok{y\_test }\OperatorTok{=}\NormalTok{ test\_df[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]} + +\CommentTok{\# Baseline metrics from forecast and total demand} +\NormalTok{original\_mse }\OperatorTok{=}\NormalTok{ mean\_squared\_error(y\_test, } +\NormalTok{ test\_df[}\StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{])} +\NormalTok{original\_mape }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(y\_test, } +\NormalTok{ test\_df[}\StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{]) }\OperatorTok{*} \DecValTok{100} + +\CommentTok{\# TimeSeriesSplit to respect time order} +\NormalTok{tscv }\OperatorTok{=}\NormalTok{ TimeSeriesSplit(n\_splits}\OperatorTok{=}\DecValTok{3}\NormalTok{)} + +\CommentTok{\# Parameter grid for randomized search} +\NormalTok{param\_dist }\OperatorTok{=}\NormalTok{ \{} + \StringTok{\textquotesingle{}n\_estimators\textquotesingle{}}\NormalTok{: [}\DecValTok{100}\NormalTok{, }\DecValTok{150}\NormalTok{, }\DecValTok{200}\NormalTok{, }\DecValTok{250}\NormalTok{],} + \StringTok{\textquotesingle{}max\_depth\textquotesingle{}}\NormalTok{: [}\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{5}\NormalTok{],} + \StringTok{\textquotesingle{}learning\_rate\textquotesingle{}}\NormalTok{: [}\FloatTok{0.01}\NormalTok{, }\FloatTok{0.03}\NormalTok{, }\FloatTok{0.05}\NormalTok{, }\FloatTok{0.1}\NormalTok{],} + \StringTok{\textquotesingle{}subsample\textquotesingle{}}\NormalTok{: [}\FloatTok{0.7}\NormalTok{, }\FloatTok{0.8}\NormalTok{, }\FloatTok{1.0}\NormalTok{],} + \StringTok{\textquotesingle{}colsample\_bytree\textquotesingle{}}\NormalTok{: [}\FloatTok{0.7}\NormalTok{, }\FloatTok{0.8}\NormalTok{, }\FloatTok{1.0}\NormalTok{]} +\NormalTok{\}} + +\CommentTok{\# Create base model} +\NormalTok{xgb\_model }\OperatorTok{=}\NormalTok{ xgb.XGBRegressor(} +\NormalTok{ objective}\OperatorTok{=}\StringTok{\textquotesingle{}reg:squarederror\textquotesingle{}}\NormalTok{,} +\NormalTok{ tree\_method}\OperatorTok{=}\StringTok{\textquotesingle{}hist\textquotesingle{}}\NormalTok{,} +\NormalTok{ random\_state}\OperatorTok{=}\DecValTok{42} +\NormalTok{)} + +\CommentTok{\# Randomized search} +\NormalTok{random\_search }\OperatorTok{=}\NormalTok{ RandomizedSearchCV(} +\NormalTok{ estimator}\OperatorTok{=}\NormalTok{xgb\_model,} +\NormalTok{ param\_distributions}\OperatorTok{=}\NormalTok{param\_dist,} +\NormalTok{ n\_iter}\OperatorTok{=}\DecValTok{2000}\NormalTok{, } +\NormalTok{ scoring}\OperatorTok{=}\StringTok{\textquotesingle{}neg\_mean\_absolute\_percentage\_error\textquotesingle{}}\NormalTok{,} +\NormalTok{ cv}\OperatorTok{=}\NormalTok{tscv,} +\NormalTok{ verbose}\OperatorTok{=}\DecValTok{1}\NormalTok{,} +\NormalTok{ n\_jobs}\OperatorTok{={-}}\DecValTok{1}\NormalTok{,} +\NormalTok{ random\_state}\OperatorTok{=}\DecValTok{42} +\NormalTok{)} + +\CommentTok{\# Run the search} +\NormalTok{random\_search.fit(X\_train, y\_train)} + +\CommentTok{\# Use the best model} +\NormalTok{model }\OperatorTok{=}\NormalTok{ random\_search.best\_estimator\_} + +\CommentTok{\# Optional: Print best parameters} +\BuiltInTok{print}\NormalTok{(}\StringTok{"Best Parameters:"}\NormalTok{, random\_search.best\_params\_)} + +\CommentTok{\#\#\#Output learning\_rate=0.1, n\_estimators=150, max\_depth=3,} +\CommentTok{\#\#\# subsample=0.8} +\end{Highlighting} +\end{Shaded} + +\noindent **Tuned Model** + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{X\_train }\OperatorTok{=}\NormalTok{ train\_df[features]} +\NormalTok{y\_train }\OperatorTok{=}\NormalTok{ train\_df[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]} +\NormalTok{X\_test }\OperatorTok{=}\NormalTok{ test\_df[features]} +\NormalTok{y\_test }\OperatorTok{=}\NormalTok{ test\_df[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]} + +\CommentTok{\# Baseline metrics from forecast and total demand} +\NormalTok{original\_mse }\OperatorTok{=}\NormalTok{ mean\_squared\_error(y\_test, test\_df[}\StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{])} +\NormalTok{original\_mape }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(y\_test, } +\NormalTok{ test\_df[}\StringTok{\textquotesingle{}forecast\_demand\textquotesingle{}}\NormalTok{]) }\OperatorTok{*} \DecValTok{100} + +\CommentTok{\# Model creation, taken from fine tuning} +\NormalTok{xgb\_model }\OperatorTok{=}\NormalTok{ xgb.XGBRegressor(objective}\OperatorTok{=}\StringTok{\textquotesingle{}reg:squarederror\textquotesingle{}}\NormalTok{, } +\NormalTok{ tree\_method}\OperatorTok{=}\StringTok{\textquotesingle{}hist\textquotesingle{}}\NormalTok{, random\_state}\OperatorTok{=}\DecValTok{42}\NormalTok{)} + +\NormalTok{model }\OperatorTok{=}\NormalTok{ xgb.XGBRegressor(} +\NormalTok{ objective}\OperatorTok{=}\StringTok{\textquotesingle{}reg:squarederror\textquotesingle{}}\NormalTok{,} +\NormalTok{ learning\_rate}\OperatorTok{=}\FloatTok{0.1}\NormalTok{,} +\NormalTok{ n\_estimators}\OperatorTok{=}\DecValTok{150}\NormalTok{,} +\NormalTok{ max\_depth}\OperatorTok{=}\DecValTok{3}\NormalTok{,} +\NormalTok{ subsample}\OperatorTok{=}\FloatTok{0.8}\NormalTok{,} +\NormalTok{ random\_state}\OperatorTok{=}\DecValTok{42} +\NormalTok{)} + +\NormalTok{model.fit(X\_train, y\_train)} + + +\CommentTok{\# Predict and evaluate} +\NormalTok{y\_pred }\OperatorTok{=}\NormalTok{ model.predict(X\_test)} +\NormalTok{model\_mse }\OperatorTok{=}\NormalTok{ mean\_squared\_error(y\_test, y\_pred)} +\NormalTok{model\_mape }\OperatorTok{=}\NormalTok{ mean\_absolute\_percentage\_error(y\_test, y\_pred) }\OperatorTok{*} \DecValTok{100} +\end{Highlighting} +\end{Shaded} + +Evaluate Performance + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\# Results} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Original Forecast MSE: }\SpecialCharTok{\{}\NormalTok{original\_mse}\SpecialCharTok{:.2f\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Original Forecast MAPE: }\SpecialCharTok{\{}\NormalTok{original\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"XGBoost Tuned Model MSE: }\SpecialCharTok{\{}\NormalTok{model\_mse}\SpecialCharTok{:.2f\}}\SpecialStringTok{"}\NormalTok{)} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"XGBoost Tuned Model MAPE: }\SpecialCharTok{\{}\NormalTok{model\_mape}\SpecialCharTok{:.3f\}}\SpecialStringTok{\%"}\NormalTok{)} + + +\CommentTok{\# Explain model predictions using SHAP} +\NormalTok{explainer }\OperatorTok{=}\NormalTok{ shap.Explainer(model, X\_test)} +\NormalTok{shap\_values }\OperatorTok{=}\NormalTok{ explainer(X\_test)} +\NormalTok{shap\_df }\OperatorTok{=}\NormalTok{ pd.DataFrame(shap\_values.values, columns}\OperatorTok{=}\NormalTok{X\_test.columns)} + +\CommentTok{\# Forecast demand skews the plot so hide it} +\NormalTok{filtered\_shap\_values }\OperatorTok{=}\NormalTok{ shap\_df.drop(columns}\OperatorTok{=}\NormalTok{[}\StringTok{"forecast\_demand"}\NormalTok{])} +\NormalTok{filtered\_X\_test }\OperatorTok{=}\NormalTok{ X\_test.drop(columns}\OperatorTok{=}\NormalTok{[}\StringTok{"forecast\_demand"}\NormalTok{])} + +\NormalTok{shap.summary\_plot(} +\NormalTok{ filtered\_shap\_values.values,} +\NormalTok{ features}\OperatorTok{=}\NormalTok{filtered\_X\_test,} +\NormalTok{ feature\_names}\OperatorTok{=}\NormalTok{filtered\_X\_test.columns} +\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\section*{Appendix C: Plots}\label{appendix-c-plots} +\addcontentsline{toc}{section}{Appendix C: Plots} + +Packages used for plotting data. + +\begin{Shaded} +\begin{Highlighting}[] +\ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd} +\ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np} +\ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt} +\ImportTok{import}\NormalTok{ seaborn }\ImportTok{as}\NormalTok{ sns} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:yeardemand}}\label{figure-reffigyeardemand} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_demand\_14d }\OperatorTok{=}\NormalTok{ df\_demand[[}\StringTok{\textquotesingle{}date\_time\textquotesingle{}}\NormalTok{,}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]].copy()} + +\NormalTok{df\_demand\_14d[}\StringTok{\textquotesingle{}dem\_14d\textquotesingle{}}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_demand.total\_demand.rolling(window}\OperatorTok{=}\DecValTok{672}\NormalTok{).mean()} + +\NormalTok{plt.figure(figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{6}\NormalTok{, }\DecValTok{4}\NormalTok{))} +\NormalTok{plt.plot(df\_demand\_14d[}\StringTok{\textquotesingle{}date\_time\textquotesingle{}}\NormalTok{], df\_demand\_14d[}\StringTok{\textquotesingle{}dem\_14d\textquotesingle{}}\NormalTok{],} +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}14{-}Day Rolling Avg\textquotesingle{}}\NormalTok{, color}\OperatorTok{=}\StringTok{\textquotesingle{}blue\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}Year\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{\textquotesingle{}Total Demand (MW)\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.legend()} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:monthdemand}}\label{figure-reffigmonthdemand} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_demand\_month }\OperatorTok{=}\NormalTok{ df\_demand[[}\StringTok{\textquotesingle{}date\_time\textquotesingle{}}\NormalTok{,}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]].copy()} + +\NormalTok{df\_demand\_month[}\StringTok{\textquotesingle{}month\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_demand\_month.date\_time.dt.month} +\NormalTok{df\_demand\_month[}\StringTok{\textquotesingle{}month\_name\textquotesingle{}}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_demand\_month.date\_time.dt.month\_name().}\BuiltInTok{str}\NormalTok{[:}\DecValTok{3}\NormalTok{]} + +\NormalTok{plt.figure(figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{6}\NormalTok{,}\DecValTok{4}\NormalTok{))} +\NormalTok{sns.boxplot(} +\NormalTok{ data }\OperatorTok{=}\NormalTok{ df\_demand\_month.groupby(} + \StringTok{"date\_time"}\NormalTok{, as\_index }\OperatorTok{=} \VariableTok{False}\NormalTok{).first().sort\_values(}\StringTok{"month"}\NormalTok{), } +\NormalTok{ x }\OperatorTok{=} \StringTok{\textquotesingle{}month\_name\textquotesingle{}}\NormalTok{, y }\OperatorTok{=} \StringTok{"total\_demand"}\NormalTok{, hue }\OperatorTok{=} \StringTok{\textquotesingle{}month\textquotesingle{}}\NormalTok{, } +\NormalTok{ palette }\OperatorTok{=} \StringTok{\textquotesingle{}Blues\textquotesingle{}}\NormalTok{, showfliers }\OperatorTok{=} \VariableTok{False}\NormalTok{, legend }\OperatorTok{=} \VariableTok{False}\NormalTok{)} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}Month\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{\textquotesingle{}Total Demand (MW)\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.grid(alpha }\OperatorTok{=} \FloatTok{0.5}\NormalTok{)} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:weekdemand}}\label{figure-reffigweekdemand} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_demand\_weekday }\OperatorTok{=}\NormalTok{ df\_demand[[}\StringTok{\textquotesingle{}date\_time\textquotesingle{}}\NormalTok{,}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{]].copy()} + +\NormalTok{df\_demand\_weekday[}\StringTok{\textquotesingle{}weekday\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_demand\_weekday.date\_time.dt.day\_of\_week} +\NormalTok{df\_demand\_weekday[}\StringTok{\textquotesingle{}weekday\_name\textquotesingle{}}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_demand\_weekday.date\_time.dt.day\_name().}\BuiltInTok{str}\NormalTok{[:}\DecValTok{3}\NormalTok{]} + +\NormalTok{plt.figure(figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{6}\NormalTok{,}\DecValTok{4}\NormalTok{))} +\NormalTok{sns.boxplot(data }\OperatorTok{=}\NormalTok{ df\_demand\_weekday.groupby(}\StringTok{"date\_time"}\NormalTok{, } +\NormalTok{ as\_index }\OperatorTok{=} \VariableTok{False}\NormalTok{).first().sort\_values(}\StringTok{"weekday"}\NormalTok{), } +\NormalTok{ x }\OperatorTok{=} \StringTok{\textquotesingle{}weekday\_name\textquotesingle{}}\NormalTok{, y }\OperatorTok{=} \StringTok{"total\_demand"}\NormalTok{, } +\NormalTok{ hue }\OperatorTok{=} \StringTok{\textquotesingle{}weekday\textquotesingle{}}\NormalTok{, palette }\OperatorTok{=} \StringTok{\textquotesingle{}Blues\textquotesingle{}}\NormalTok{, showfliers }\OperatorTok{=} \VariableTok{False}\NormalTok{, } +\NormalTok{ legend }\OperatorTok{=} \VariableTok{False}\NormalTok{)} +\NormalTok{plt.grid(alpha }\OperatorTok{=} \FloatTok{0.5}\NormalTok{)} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}Day of the Week\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{\textquotesingle{}Total Demand (MW)\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:hourdemand}}\label{figure-reffighourdemand} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_hour }\OperatorTok{=}\NormalTok{ df\_all.copy()} +\NormalTok{df\_hour[}\StringTok{"date\_time\_future\_hour"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_hour.date\_time\_future.dt.hour} +\NormalTok{df\_hour }\OperatorTok{=}\NormalTok{ df\_hour.sort\_values(}\StringTok{"date\_time\_future\_hour"}\NormalTok{)} + + + +\NormalTok{plt.figure(figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{12}\NormalTok{,}\DecValTok{6}\NormalTok{))} +\NormalTok{sns.boxplot(data }\OperatorTok{=}\NormalTok{ df\_hour.groupby(} + \StringTok{"date\_time\_future"}\NormalTok{, as\_index }\OperatorTok{=} \VariableTok{False}\NormalTok{).first().sort\_values(} + \StringTok{"date\_time\_future\_hour"}\NormalTok{), } +\NormalTok{ x}\OperatorTok{=}\StringTok{"date\_time\_future\_hour"}\NormalTok{, y }\OperatorTok{=} \StringTok{"total\_demand"}\NormalTok{, } +\NormalTok{ palette }\OperatorTok{=} \StringTok{\textquotesingle{}Blues\textquotesingle{}}\NormalTok{, showfliers }\OperatorTok{=} \VariableTok{False}\NormalTok{)} +\NormalTok{plt.grid(alpha }\OperatorTok{=} \FloatTok{0.5}\NormalTok{)} +\NormalTok{plt.title(}\StringTok{"Hour vs Total Demand"}\NormalTok{)}\OperatorTok{;} + +\NormalTok{time\_decomposition\_error\_plots(df }\OperatorTok{=}\NormalTok{ df\_hour, } +\NormalTok{ x }\OperatorTok{=} \StringTok{"date\_time\_future\_hour"}\NormalTok{, time\_interval }\OperatorTok{=} \StringTok{"Hour"}\NormalTok{,} +\NormalTok{ show\_outliers }\OperatorTok{=} \VariableTok{False}\NormalTok{, forecast\_interval }\OperatorTok{=} \DecValTok{12}\NormalTok{, } +\NormalTok{ show\_relative\_error\_all }\OperatorTok{=} \VariableTok{True}\NormalTok{, } +\NormalTok{ show\_relative\_error\_interval }\OperatorTok{=} \VariableTok{True}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:forecastdem}}\label{figure-reffigforecastdem} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{forecast\_df[}\StringTok{\textquotesingle{}forecast\_hours\textquotesingle{}}\NormalTok{] }\OperatorTok{=}\NormalTok{ (forecast\_df[}\StringTok{\textquotesingle{}DATETIME\textquotesingle{}}\NormalTok{] }\OperatorTok{{-}} +\NormalTok{ forecast\_df[}\StringTok{\textquotesingle{}LASTCHANGED\textquotesingle{}}\NormalTok{]).dt.total\_seconds() }\OperatorTok{/} \DecValTok{3600} + + +\NormalTok{merged\_df }\OperatorTok{=}\NormalTok{ forecast\_df.merge(} +\NormalTok{ actual\_df,} +\NormalTok{ on}\OperatorTok{=}\NormalTok{[}\StringTok{\textquotesingle{}DATETIME\textquotesingle{}}\NormalTok{],} +\NormalTok{ how}\OperatorTok{=}\StringTok{\textquotesingle{}inner\textquotesingle{}} +\NormalTok{)} + + +\NormalTok{hours }\OperatorTok{=}\NormalTok{ [}\DecValTok{6}\NormalTok{, }\DecValTok{12}\NormalTok{, }\DecValTok{18}\NormalTok{, }\DecValTok{24}\NormalTok{]} +\NormalTok{dfs }\OperatorTok{=}\NormalTok{ \{} +\NormalTok{ h: merged\_df[} +\NormalTok{ merged\_df[}\StringTok{\textquotesingle{}forecast\_hours\textquotesingle{}}\NormalTok{].}\BuiltInTok{round}\NormalTok{() }\OperatorTok{==}\NormalTok{ h].sample(n}\OperatorTok{=}\DecValTok{3000}\NormalTok{,} +\NormalTok{ random\_state}\OperatorTok{=}\DecValTok{42}\NormalTok{)} + \ControlFlowTok{for}\NormalTok{ h }\KeywordTok{in}\NormalTok{ hours} +\NormalTok{\}} + + +\NormalTok{fig, axes }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{2}\NormalTok{, }\DecValTok{2}\NormalTok{, figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{8}\NormalTok{, }\DecValTok{6}\NormalTok{))} +\ControlFlowTok{for}\NormalTok{ ax, h }\KeywordTok{in} \BuiltInTok{zip}\NormalTok{(axes.flat, hours):} +\NormalTok{ sns.regplot(} +\NormalTok{ data}\OperatorTok{=}\NormalTok{dfs[h],} +\NormalTok{ x}\OperatorTok{=}\StringTok{\textquotesingle{}TOTALDEMAND\textquotesingle{}}\NormalTok{,} +\NormalTok{ y}\OperatorTok{=}\StringTok{\textquotesingle{}FORECASTDEMAND\textquotesingle{}}\NormalTok{,} +\NormalTok{ line\_kws}\OperatorTok{=}\NormalTok{\{}\StringTok{\textquotesingle{}color\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}red\textquotesingle{}}\NormalTok{\},} +\NormalTok{ ax}\OperatorTok{=}\NormalTok{ax} +\NormalTok{ )} +\NormalTok{ ax.set\_title(}\SpecialStringTok{f\textquotesingle{}}\SpecialCharTok{\{}\NormalTok{h}\SpecialCharTok{\}}\SpecialStringTok{{-}Hour Ahead Forecast\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax.set\_xlabel(}\StringTok{\textquotesingle{}Actual Demand\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax.set\_ylabel(}\StringTok{\textquotesingle{}Forecasted Demand\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax.axhline(y }\OperatorTok{=} \DecValTok{9000}\NormalTok{,} +\NormalTok{ color }\OperatorTok{=} \StringTok{\textquotesingle{}green\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.legend([}\StringTok{\textquotesingle{}Correlation points\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}Trendline\textquotesingle{}}\NormalTok{,}\StringTok{\textquotesingle{}\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}Forecasted = 9000\textquotesingle{}}\NormalTok{])} +\NormalTok{plt.tight\_layout()} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:weatherdemand}}\label{figure-reffigweatherdemand} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_corr\_demand }\OperatorTok{=}\NormalTok{ df\_all[[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}temperature\_future\_forecast\textquotesingle{}}\NormalTok{,}\StringTok{\textquotesingle{}humidity\_future\_forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}rain\_future\_forecast\textquotesingle{}}\NormalTok{,}\StringTok{\textquotesingle{}wind\_speed\_future\_forecast\textquotesingle{}}\NormalTok{]]} + +\NormalTok{df\_corr\_demand }\OperatorTok{=}\NormalTok{ df\_corr\_demand.rename(} +\NormalTok{ columns}\OperatorTok{=}\NormalTok{\{}\StringTok{\textquotesingle{}temperature\_future\_forecast\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}Temperature Forecast\textquotesingle{}}\NormalTok{, } + \StringTok{\textquotesingle{}humidity\_future\_forecast\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}Humidity Forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}rain\_future\_forecast\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}Rain Forecast\textquotesingle{}}\NormalTok{,} + \StringTok{\textquotesingle{}wind\_speed\_future\_forecast\textquotesingle{}}\NormalTok{: }\StringTok{\textquotesingle{}Wind Speed Forecast\textquotesingle{}}\NormalTok{\})} + +\NormalTok{correlation\_demand }\OperatorTok{=}\NormalTok{ df\_corr\_demand.corr()} +\NormalTok{correlationsD }\OperatorTok{=}\NormalTok{ correlation\_demand[}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{].drop(}\StringTok{\textquotesingle{}total\_demand\textquotesingle{}}\NormalTok{)} + +\NormalTok{plt.figure(figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{10}\NormalTok{, }\DecValTok{6}\NormalTok{))} +\NormalTok{correlationsD.sort\_values().plot(kind}\OperatorTok{=}\StringTok{\textquotesingle{}barh\textquotesingle{}}\NormalTok{, } +\NormalTok{ color}\OperatorTok{=}\NormalTok{plt.cm.coolwarm(np.}\BuiltInTok{abs}\NormalTok{(correlationsD)}\OperatorTok{/}\BuiltInTok{max}\NormalTok{(}\BuiltInTok{abs}\NormalTok{(correlationsD))))} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}Correlation Coefficient\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.axvline(x}\OperatorTok{=}\DecValTok{0}\NormalTok{, color}\OperatorTok{=}\StringTok{\textquotesingle{}k\textquotesingle{}}\NormalTok{, linestyle}\OperatorTok{=}\StringTok{\textquotesingle{}{-}\textquotesingle{}}\NormalTok{, alpha}\OperatorTok{=}\FloatTok{0.3}\NormalTok{)} +\NormalTok{plt.grid(axis}\OperatorTok{=}\StringTok{\textquotesingle{}x\textquotesingle{}}\NormalTok{, alpha}\OperatorTok{=}\FloatTok{0.3}\NormalTok{)} +\NormalTok{plt.tight\_layout()} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:tempcorr}}\label{figure-reffigtempcorr} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{hourly\_temp }\OperatorTok{=}\NormalTok{ temperature\_df.groupby(} +\NormalTok{ [}\StringTok{\textquotesingle{}HOUR\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}LOCATION\textquotesingle{}}\NormalTok{])[}\StringTok{\textquotesingle{}TEMPERATURE\textquotesingle{}}\NormalTok{].mean().reset\_index()} +\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Aggregated temperature data: }\SpecialCharTok{\{}\BuiltInTok{len}\NormalTok{(hourly\_temp)}\SpecialCharTok{\}}\SpecialStringTok{ rows"}\NormalTok{)} + +\NormalTok{merged\_df }\OperatorTok{=}\NormalTok{ pd.merge(} +\NormalTok{ demand\_df,} +\NormalTok{ hourly\_temp,} +\NormalTok{ left\_on}\OperatorTok{=}\StringTok{\textquotesingle{}HOUR\textquotesingle{}}\NormalTok{,} +\NormalTok{ right\_on}\OperatorTok{=}\StringTok{\textquotesingle{}HOUR\textquotesingle{}}\NormalTok{,} +\NormalTok{ how}\OperatorTok{=}\StringTok{\textquotesingle{}inner\textquotesingle{}} +\NormalTok{)} + + +\NormalTok{plt.figure(figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{10}\NormalTok{, }\DecValTok{6}\NormalTok{))} +\NormalTok{plt.scatter(merged\_df[}\StringTok{\textquotesingle{}TEMPERATURE\textquotesingle{}}\NormalTok{], merged\_df[}\StringTok{\textquotesingle{}TOTALDEMAND\textquotesingle{}}\NormalTok{], } +\NormalTok{ alpha}\OperatorTok{=}\FloatTok{0.5}\NormalTok{)} +\NormalTok{plt.title(}\StringTok{\textquotesingle{}Relationship between Temperature and Electricity Demand\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}Temperature (°C)\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{\textquotesingle{}Total Demand (MW)\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.grid(}\VariableTok{True}\NormalTok{, alpha}\OperatorTok{=}\FloatTok{0.3}\NormalTok{)} + +\CommentTok{\# Add trend line} +\NormalTok{z }\OperatorTok{=}\NormalTok{ np.polyfit(merged\_df[}\StringTok{\textquotesingle{}TEMPERATURE\textquotesingle{}}\NormalTok{], merged\_df[}\StringTok{\textquotesingle{}TOTALDEMAND\textquotesingle{}}\NormalTok{], }\DecValTok{2}\NormalTok{)} +\NormalTok{p }\OperatorTok{=}\NormalTok{ np.poly1d(z)} +\NormalTok{temp\_range }\OperatorTok{=}\NormalTok{ np.linspace(merged\_df[}\StringTok{\textquotesingle{}TEMPERATURE\textquotesingle{}}\NormalTok{].}\BuiltInTok{min}\NormalTok{(), } +\NormalTok{ merged\_df[}\StringTok{\textquotesingle{}TEMPERATURE\textquotesingle{}}\NormalTok{].}\BuiltInTok{max}\NormalTok{(), }\DecValTok{100}\NormalTok{)} +\NormalTok{plt.plot(temp\_range, p(temp\_range), }\StringTok{"r{-}{-}"}\NormalTok{, linewidth}\OperatorTok{=}\DecValTok{2}\NormalTok{)} + +\NormalTok{plt.savefig(}\StringTok{\textquotesingle{}temperature\_vs\_demand\_scatter.png\textquotesingle{}}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:errorvtemp}}\label{figure-reffigerrorvtemp} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{interval }\OperatorTok{=} \DecValTok{60}\OperatorTok{*}\DecValTok{60} \CommentTok{\#sets the interval in seconds} +\NormalTok{df\_forecast[}\StringTok{"forecast\_interval"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_forecast.date\_time\_prediction }\OperatorTok{{-}} +\NormalTok{ df\_forecast.date\_time\_forecast} +\NormalTok{df\_forecast.forecast\_interval }\OperatorTok{=}\NormalTok{ df\_forecast.forecast\_interval.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: x.total\_seconds()}\OperatorTok{/}\NormalTok{interval)} + + +\NormalTok{interval\_min, interval\_max }\OperatorTok{=} \DecValTok{23}\NormalTok{ , }\DecValTok{25} \CommentTok{\#sets a window for forecast periods} +\NormalTok{df\_forecast\_near24hour }\OperatorTok{=} +\NormalTok{ df\_forecast.loc[(df\_forecast.forecast\_interval }\OperatorTok{\textgreater{}}\NormalTok{ interval\_min) }\OperatorTok{\&} +\NormalTok{ (df\_forecast.forecast\_interval }\OperatorTok{\textless{}}\NormalTok{ interval\_max)]} +\NormalTok{df\_forecast\_near24hour[}\StringTok{"date\_time\_forecast\_rounded"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_forecast\_near24hour.date\_time\_forecast.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: x.}\BuiltInTok{round}\NormalTok{(freq}\OperatorTok{=}\StringTok{\textquotesingle{}30min\textquotesingle{}}\NormalTok{))} +\NormalTok{df\_forecast\_near24hour\_1instance }\OperatorTok{=} +\NormalTok{ df\_forecast\_near24hour.loc[} +\NormalTok{ df\_forecast\_near24hour.groupby(} + \StringTok{"date\_time\_forecast\_rounded"}\NormalTok{)[}\StringTok{"forecast\_interval"}\NormalTok{].idxmax()]} + + +\NormalTok{df\_forecast\_near24hour\_1instance\_with\_demand }\OperatorTok{=} +\NormalTok{ pd.merge(df\_forecast\_near24hour\_1instance, } +\NormalTok{ df\_demand, left\_on }\OperatorTok{=} \StringTok{"date\_time\_forecast\_rounded"}\NormalTok{, } +\NormalTok{ right\_on }\OperatorTok{=} \StringTok{"date\_time"}\NormalTok{)} +\NormalTok{df\_forecast\_near24hour\_1instance\_with\_demand[}\StringTok{"forecast\_error"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_forecast\_near24hour\_1instance\_with\_demand.total\_demand }\OperatorTok{{-}} +\NormalTok{ df\_forecast\_near24hour\_1instance\_with\_demand.forecast\_demand} + +\NormalTok{df\_forecast\_near24hour\_1instance\_with\_demand\_temperature }\OperatorTok{=} +\NormalTok{ pd.merge(df\_forecast\_near24hour\_1instance\_with\_demand, } +\NormalTok{ df\_temperature, left\_on }\OperatorTok{=} \StringTok{"date\_time\_forecast\_rounded"}\NormalTok{, } +\NormalTok{ right\_on }\OperatorTok{=} \StringTok{"date\_time"}\NormalTok{)} +\NormalTok{df\_forecast\_near24hour\_1instance\_with\_demand\_temperature[} + \StringTok{"forecast\_error\_relative"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_forecast\_near24hour\_1instance\_with\_demand\_temperature.forecast\_error}\OperatorTok{/} +\NormalTok{ df\_forecast\_near24hour\_1instance\_with\_demand\_temperature.total\_demand} + +\NormalTok{df\_plot }\OperatorTok{=}\NormalTok{ df\_forecast\_near24hour\_1instance\_with\_demand\_temperature[[} + \StringTok{"temperature"}\NormalTok{, }\StringTok{"forecast\_error"}\NormalTok{, }\StringTok{"forecast\_error\_relative"}\NormalTok{]].copy()} +\NormalTok{df\_plot.temperature }\OperatorTok{=}\NormalTok{ df\_plot.temperature.}\BuiltInTok{round}\NormalTok{()} + +\NormalTok{plt.figure(figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{12}\NormalTok{,}\DecValTok{7}\NormalTok{))} +\NormalTok{sns.boxplot(data}\OperatorTok{=}\NormalTok{df\_plot, x}\OperatorTok{=}\StringTok{"temperature"}\NormalTok{, y}\OperatorTok{=}\StringTok{"forecast\_error"}\NormalTok{, } +\NormalTok{ fliersize }\OperatorTok{=} \DecValTok{1}\NormalTok{)} +\NormalTok{plt.axhline(}\DecValTok{0}\NormalTok{, color}\OperatorTok{=}\StringTok{\textquotesingle{}r\textquotesingle{}}\NormalTok{, alpha }\OperatorTok{=} \FloatTok{0.2}\NormalTok{)} +\NormalTok{plt.xticks(rotation }\OperatorTok{=} \DecValTok{90}\NormalTok{)}\OperatorTok{;} +\NormalTok{plt.title(}\StringTok{"Accuracy of forecasting 24h into the future"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:relerrorvtemp}}\label{figure-reffigrelerrorvtemp} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{plt.figure(figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{12}\NormalTok{,}\DecValTok{7}\NormalTok{))} +\NormalTok{sns.boxplot(data}\OperatorTok{=}\NormalTok{df\_plot, x}\OperatorTok{=}\StringTok{"temperature"}\NormalTok{, } +\NormalTok{ y}\OperatorTok{=}\StringTok{"forecast\_error\_relative"}\NormalTok{, fliersize }\OperatorTok{=} \DecValTok{1}\NormalTok{)} +\NormalTok{plt.axhline(}\DecValTok{0}\NormalTok{, color}\OperatorTok{=}\StringTok{\textquotesingle{}r\textquotesingle{}}\NormalTok{, alpha }\OperatorTok{=} \FloatTok{0.2}\NormalTok{)} +\NormalTok{plt.xticks(rotation }\OperatorTok{=} \DecValTok{90}\NormalTok{)}\OperatorTok{;} +\NormalTok{plt.ylabel(}\StringTok{"Forecast Error as Portion of Actual Demand"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:acfErrors} \& Figure \ref{fig:pacfErrors}}\label{figure-reffigacferrors-figure-reffigpacferrors} + +\begin{Shaded} +\begin{Highlighting}[] +\ControlFlowTok{for}\NormalTok{ i, delta }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{([}\DecValTok{12}\NormalTok{, }\DecValTok{24}\NormalTok{, }\DecValTok{36}\NormalTok{, }\DecValTok{48}\NormalTok{]):} +\NormalTok{ df\_all\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[} +\NormalTok{ df\_all.period\_id }\OperatorTok{==}\NormalTok{ delta].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} +\NormalTok{ delta\_24h\_later }\OperatorTok{=} \DecValTok{48} \OperatorTok{{-}}\NormalTok{ delta} +\NormalTok{ previous\_lag }\OperatorTok{=} \DecValTok{48} + +\NormalTok{ x }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_error\_relative[} +\NormalTok{ previous\_lag:}\BuiltInTok{len}\NormalTok{(df\_all\_delta)]} +\NormalTok{ y }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_error\_relative[} + \DecValTok{0}\NormalTok{:}\BuiltInTok{len}\NormalTok{(df\_all\_delta)}\OperatorTok{{-}}\NormalTok{previous\_lag]} + +\KeywordTok{def}\NormalTok{ check\_stationarity(series):} + +\NormalTok{ result }\OperatorTok{=}\NormalTok{ adfuller(series.values)} + + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}ADF Statistic: }\SpecialCharTok{\%f}\StringTok{\textquotesingle{}} \OperatorTok{\%}\NormalTok{ result[}\DecValTok{0}\NormalTok{])} + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}p{-}value: }\SpecialCharTok{\%f}\StringTok{\textquotesingle{}} \OperatorTok{\%}\NormalTok{ result[}\DecValTok{1}\NormalTok{])} + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}Critical Values:\textquotesingle{}}\NormalTok{)} + \ControlFlowTok{for}\NormalTok{ key, value }\KeywordTok{in}\NormalTok{ result[}\DecValTok{4}\NormalTok{].items():} + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}}\CharTok{\textbackslash{}t}\SpecialCharTok{\%s}\StringTok{: }\SpecialCharTok{\%.3f}\StringTok{\textquotesingle{}} \OperatorTok{\%}\NormalTok{ (key, value))} + + \ControlFlowTok{if}\NormalTok{ (result[}\DecValTok{1}\NormalTok{] }\OperatorTok{\textless{}=} \FloatTok{0.05}\NormalTok{) }\OperatorTok{\&}\NormalTok{ (result[}\DecValTok{4}\NormalTok{][}\StringTok{\textquotesingle{}5\%\textquotesingle{}}\NormalTok{] }\OperatorTok{\textgreater{}}\NormalTok{ result[}\DecValTok{0}\NormalTok{]):} + \BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}u001b}\StringTok{[32mStationary}\CharTok{\textbackslash{}u001b}\StringTok{[0m"}\NormalTok{)} + \ControlFlowTok{else}\NormalTok{:} + \BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}x1b}\StringTok{[31mNon{-}stationary}\CharTok{\textbackslash{}x1b}\StringTok{[0m"}\NormalTok{)} + +\NormalTok{fig1, ax1 }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{, figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{18}\NormalTok{, }\DecValTok{18}\NormalTok{))} +\NormalTok{fig2, ax2 }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{, figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{18}\NormalTok{, }\DecValTok{18}\NormalTok{))} + +\NormalTok{i\_subplot }\OperatorTok{=}\NormalTok{ \{}\DecValTok{0}\NormalTok{: [}\DecValTok{0}\NormalTok{,}\DecValTok{0}\NormalTok{], }\DecValTok{1}\NormalTok{: [}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{], }\DecValTok{2}\NormalTok{: [}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{], }\DecValTok{3}\NormalTok{: [}\DecValTok{1}\NormalTok{,}\DecValTok{1}\NormalTok{]\}} + +\ControlFlowTok{for}\NormalTok{ i, period\_id }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{([}\DecValTok{12}\NormalTok{, }\DecValTok{24}\NormalTok{, }\DecValTok{36}\NormalTok{, }\DecValTok{48}\NormalTok{]):} + \BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Forecast Interval = }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(period\_id}\OperatorTok{/}\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} + +\NormalTok{ df\_all\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[df\_all.period\_id }\OperatorTok{==} +\NormalTok{ period\_id].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} + +\NormalTok{ check\_stationarity(df\_all\_delta.forecast\_error\_relative)} + +\NormalTok{ plot\_acf(df\_all\_delta.forecast\_error\_relative, lags }\OperatorTok{=} \DecValTok{100}\NormalTok{, } +\NormalTok{ ax }\OperatorTok{=}\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]])} +\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_xlabel(}\StringTok{\textquotesingle{}lag\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_title(}\SpecialStringTok{f\textquotesingle{}Forecast Interval = } +\ErrorTok{ \{round}\NormalTok{(period\_id}\OperatorTok{/}\DecValTok{2}\NormalTok{)\}h}\StringTok{\textquotesingle{}) } +\ErrorTok{ ax1}\NormalTok{[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_ylim(}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{)} + +\NormalTok{ plot\_pacf(df\_all\_delta.forecast\_error\_relative, lags }\OperatorTok{=} \DecValTok{100}\NormalTok{, } +\NormalTok{ ax }\OperatorTok{=}\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]])} +\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_xlabel(}\StringTok{\textquotesingle{}lag\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_title(}\SpecialStringTok{f\textquotesingle{}Forecast Interval = } +\ErrorTok{ \{round}\NormalTok{(period\_id}\OperatorTok{/}\DecValTok{2}\NormalTok{)\}h}\StringTok{\textquotesingle{})} +\ErrorTok{ ax2}\NormalTok{[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_ylim(}\OperatorTok{{-}}\FloatTok{0.5}\NormalTok{,}\DecValTok{1}\NormalTok{)} + +\CommentTok{\#fig1.suptitle(\textquotesingle{}Autocorrelation\textquotesingle{})} +\CommentTok{\#fig2.suptitle(\textquotesingle{}Partial Autocorrelation\textquotesingle{})} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:laggedError}}\label{figure-reffiglaggederror} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{delta }\OperatorTok{=} \DecValTok{24} + +\NormalTok{df\_all\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[df\_all.period\_id }\OperatorTok{==}\NormalTok{ delta].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} + +\NormalTok{x }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_error\_relative[delta:}\BuiltInTok{len}\NormalTok{(df\_all\_delta)]} +\NormalTok{y }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_error\_relative[}\DecValTok{0}\NormalTok{:}\BuiltInTok{len}\NormalTok{(df\_all\_delta)}\OperatorTok{{-}}\NormalTok{delta]} + +\NormalTok{plt.subplots(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{, figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{18}\NormalTok{, }\DecValTok{18}\NormalTok{))} + +\ControlFlowTok{for}\NormalTok{ i, delta }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{([}\DecValTok{12}\NormalTok{, }\DecValTok{24}\NormalTok{, }\DecValTok{36}\NormalTok{, }\DecValTok{48}\NormalTok{]):} +\NormalTok{ df\_all\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[df\_all.period\_id }\OperatorTok{==}\NormalTok{ delta].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} +\NormalTok{ delta\_24h\_later }\OperatorTok{=} \DecValTok{48} \OperatorTok{{-}}\NormalTok{ delta} +\NormalTok{ previous\_lag }\OperatorTok{=} \DecValTok{48} + +\NormalTok{ x }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_error\_relative[} +\NormalTok{ previous\_lag:}\BuiltInTok{len}\NormalTok{(df\_all\_delta)]} +\NormalTok{ y }\OperatorTok{=}\NormalTok{ df\_all\_delta.forecast\_error\_relative[} + \DecValTok{0}\NormalTok{:}\BuiltInTok{len}\NormalTok{(df\_all\_delta)}\OperatorTok{{-}}\NormalTok{previous\_lag]} + + \CommentTok{\#plt.figure(figsize = (12, 9))} +\NormalTok{ plt.subplot(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{,i}\OperatorTok{+}\DecValTok{1}\NormalTok{)} +\NormalTok{ plt.plot(np.array(x), np.array(y), }\StringTok{\textquotesingle{}.\textquotesingle{}}\NormalTok{, alpha }\OperatorTok{=} \FloatTok{0.3}\NormalTok{)} + \CommentTok{\#plt.plot(0,0, \textquotesingle{}r.\textquotesingle{})} +\NormalTok{ plt.xlim(}\OperatorTok{{-}}\FloatTok{0.15}\NormalTok{, }\FloatTok{0.15}\NormalTok{)} +\NormalTok{ plt.ylim(}\OperatorTok{{-}}\FloatTok{0.15}\NormalTok{, }\FloatTok{0.15}\NormalTok{)} +\NormalTok{ plt.grid(alpha }\OperatorTok{=} \FloatTok{0.5}\NormalTok{)} +\NormalTok{ plt.xlabel(}\StringTok{\textquotesingle{}Relative Forecast Error at Time = t\textquotesingle{}}\NormalTok{)} +\NormalTok{ plt.ylabel(}\StringTok{\textquotesingle{}Relative Forecast Error at Time = t {-} 24h\textquotesingle{}}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:ACF} \& Figure \ref{fig:PACF}}\label{figure-reffigacf-figure-reffigpacf} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{df\_all[}\StringTok{"forecast\_error\_relative"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_all.forecast\_error}\OperatorTok{/}\NormalTok{df\_all.total\_demand} + +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_month"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.month} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_year"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.year} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_weekday"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.dayofweek} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_yearTime"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ df\_all.date\_time\_future\_year.}\BuiltInTok{apply}\NormalTok{(} + \KeywordTok{lambda}\NormalTok{ x: pd.DateOffset(years}\OperatorTok{=}\NormalTok{x}\OperatorTok{{-}}\DecValTok{2000}\NormalTok{))} +\NormalTok{df\_all[}\StringTok{"date\_time\_future\_hour"}\NormalTok{] }\OperatorTok{=}\NormalTok{ df\_all.date\_time\_future.dt.hour} + +\KeywordTok{def}\NormalTok{ check\_stationarity(series):} + +\NormalTok{ result }\OperatorTok{=}\NormalTok{ adfuller(series.values)} + + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}ADF Statistic: }\SpecialCharTok{\%f}\StringTok{\textquotesingle{}} \OperatorTok{\%}\NormalTok{ result[}\DecValTok{0}\NormalTok{])} + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}p{-}value: }\SpecialCharTok{\%f}\StringTok{\textquotesingle{}} \OperatorTok{\%}\NormalTok{ result[}\DecValTok{1}\NormalTok{])} + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}Critical Values:\textquotesingle{}}\NormalTok{)} + \ControlFlowTok{for}\NormalTok{ key, value }\KeywordTok{in}\NormalTok{ result[}\DecValTok{4}\NormalTok{].items():} + \BuiltInTok{print}\NormalTok{(}\StringTok{\textquotesingle{}}\CharTok{\textbackslash{}t}\SpecialCharTok{\%s}\StringTok{: }\SpecialCharTok{\%.3f}\StringTok{\textquotesingle{}} \OperatorTok{\%}\NormalTok{ (key, value))} + + \ControlFlowTok{if}\NormalTok{ (result[}\DecValTok{1}\NormalTok{] }\OperatorTok{\textless{}=} \FloatTok{0.05}\NormalTok{) }\OperatorTok{\&}\NormalTok{ (result[}\DecValTok{4}\NormalTok{][}\StringTok{\textquotesingle{}5\%\textquotesingle{}}\NormalTok{] }\OperatorTok{\textgreater{}}\NormalTok{ result[}\DecValTok{0}\NormalTok{]):} + \BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}u001b}\StringTok{[32mStationary}\CharTok{\textbackslash{}u001b}\StringTok{[0m"}\NormalTok{)} + \ControlFlowTok{else}\NormalTok{:} + \BuiltInTok{print}\NormalTok{(}\StringTok{"}\CharTok{\textbackslash{}x1b}\StringTok{[31mNon{-}stationary}\CharTok{\textbackslash{}x1b}\StringTok{[0m"}\NormalTok{)} + + +\NormalTok{period\_id }\OperatorTok{=} \DecValTok{24} + +\NormalTok{fig1, ax1 }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{, figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{18}\NormalTok{, }\DecValTok{10}\NormalTok{))} +\NormalTok{fig2, ax2 }\OperatorTok{=}\NormalTok{ plt.subplots(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{, figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{18}\NormalTok{, }\DecValTok{10}\NormalTok{))} + +\NormalTok{i\_subplot }\OperatorTok{=}\NormalTok{ \{}\DecValTok{0}\NormalTok{: [}\DecValTok{0}\NormalTok{,}\DecValTok{0}\NormalTok{], }\DecValTok{1}\NormalTok{: [}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{], }\DecValTok{2}\NormalTok{: [}\DecValTok{1}\NormalTok{,}\DecValTok{0}\NormalTok{], }\DecValTok{3}\NormalTok{: [}\DecValTok{1}\NormalTok{,}\DecValTok{1}\NormalTok{]\}} + +\ControlFlowTok{for}\NormalTok{ i, hour\_of\_day }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{([}\DecValTok{4}\NormalTok{, }\DecValTok{10}\NormalTok{, }\DecValTok{16}\NormalTok{, }\DecValTok{22}\NormalTok{]):} + \BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Hour of Day = }\SpecialCharTok{\{}\NormalTok{hour\_of\_day}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)} +\NormalTok{ df\_all\_delta }\OperatorTok{=}\NormalTok{ df\_all.loc[(df\_all.period\_id }\OperatorTok{==}\NormalTok{ period\_id) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future\_hour }\OperatorTok{==}\NormalTok{ hour\_of\_day) }\OperatorTok{\&} +\NormalTok{ (df\_all.date\_time\_future.dt.minute }\OperatorTok{==} \DecValTok{0}\NormalTok{)].sort\_values(} + \StringTok{"date\_time\_future"}\NormalTok{).reset\_index(drop }\OperatorTok{=} \VariableTok{True}\NormalTok{)} + +\NormalTok{ check\_stationarity(df\_all\_delta.forecast\_error\_relative)} + +\NormalTok{ plot\_acf(df\_all\_delta.forecast\_error\_relative, lags }\OperatorTok{=} \DecValTok{28}\NormalTok{, } +\NormalTok{ ax }\OperatorTok{=}\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]])} +\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_xlabel(}\StringTok{\textquotesingle{}lag\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_title(} + \SpecialStringTok{f\textquotesingle{}Hour of Day = }\SpecialCharTok{\{}\NormalTok{hour\_of\_day}\SpecialCharTok{\}}\SpecialStringTok{\textquotesingle{}}\NormalTok{) } +\NormalTok{ ax1[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_ylim(}\DecValTok{0}\NormalTok{,}\DecValTok{1}\NormalTok{)} + +\NormalTok{ plot\_pacf(df\_all\_delta.forecast\_error\_relative, } +\NormalTok{ lags }\OperatorTok{=} \DecValTok{28}\NormalTok{, ax }\OperatorTok{=}\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]])} +\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_xlabel(}\StringTok{\textquotesingle{}lag\textquotesingle{}}\NormalTok{)} +\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_title(} + \SpecialStringTok{f\textquotesingle{}Hour of Day = }\SpecialCharTok{\{}\NormalTok{hour\_of\_day}\SpecialCharTok{\}}\SpecialStringTok{\textquotesingle{}}\NormalTok{) } +\NormalTok{ ax2[i\_subplot[i][}\DecValTok{0}\NormalTok{]][i\_subplot[i][}\DecValTok{1}\NormalTok{]].set\_ylim(}\OperatorTok{{-}}\FloatTok{0.1}\NormalTok{,}\DecValTok{1}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:MSEsarima} \& Figure \ref{fig:MAPEsarima}}\label{figure-reffigmsesarima-figure-reffigmapesarima} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{sarima\_tune[}\StringTok{"mse\_improvement"}\NormalTok{] }\OperatorTok{=} + \BuiltInTok{round}\NormalTok{(}\DecValTok{100}\OperatorTok{*}\NormalTok{(sarima\_tune.mse\_pre }\OperatorTok{{-}} +\NormalTok{ sarima\_tune.mse\_post)}\OperatorTok{/}\NormalTok{sarima\_tune.mse\_pre)} +\NormalTok{sarima\_tune }\OperatorTok{=}\NormalTok{ pd.merge(sarima\_tune, sarimas, } +\NormalTok{ on }\OperatorTok{=}\NormalTok{ [}\StringTok{"order"}\NormalTok{, }\StringTok{"seasonal\_order"}\NormalTok{], } +\NormalTok{ how }\OperatorTok{=} \StringTok{"left"}\NormalTok{).sort\_values(}\StringTok{"id"}\NormalTok{)} + +\NormalTok{plot }\OperatorTok{=}\NormalTok{ sarima\_tune.groupby(} +\NormalTok{ [}\StringTok{"order"}\NormalTok{, }\StringTok{"seasonal\_order"}\NormalTok{, }\StringTok{"hour\_of\_day"}\NormalTok{], } +\NormalTok{ as\_index }\OperatorTok{=} \VariableTok{False}\NormalTok{).mean()} +\NormalTok{sns.lineplot(data }\OperatorTok{=}\NormalTok{ plot, x }\OperatorTok{=} \StringTok{\textquotesingle{}hour\_of\_day\textquotesingle{}}\NormalTok{, y }\OperatorTok{=} \StringTok{\textquotesingle{}mape\textquotesingle{}}\NormalTok{, } +\NormalTok{ hue }\OperatorTok{=} \StringTok{\textquotesingle{}id\textquotesingle{}}\NormalTok{, palette }\OperatorTok{=} \StringTok{\textquotesingle{}pastel\textquotesingle{}}\NormalTok{, alpha }\OperatorTok{=} \DecValTok{1}\NormalTok{, linestyle }\OperatorTok{=} \StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:ForestModel1}}\label{figure-reffigforestmodel1} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{sample }\OperatorTok{=}\NormalTok{ np.random.choice(}\BuiltInTok{len}\NormalTok{(y\_test), }\DecValTok{100}\NormalTok{, replace}\OperatorTok{=}\VariableTok{False}\NormalTok{)} +\NormalTok{x\_axis }\OperatorTok{=} \BuiltInTok{range}\NormalTok{(}\BuiltInTok{len}\NormalTok{(sample))} + +\NormalTok{plt.figure(figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{14}\NormalTok{, }\DecValTok{6}\NormalTok{))} +\NormalTok{sns.lineplot(x}\OperatorTok{=}\NormalTok{x\_axis, y}\OperatorTok{=}\NormalTok{y\_test.iloc[sample], } +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}Actual Demand\textquotesingle{}}\NormalTok{, color}\OperatorTok{=}\StringTok{\textquotesingle{}black\textquotesingle{}}\NormalTok{)} +\NormalTok{sns.lineplot(x}\OperatorTok{=}\NormalTok{x\_axis, y}\OperatorTok{=}\NormalTok{y\_pred\_original.iloc[sample], } +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}Original Forecast\textquotesingle{}}\NormalTok{, linestyle}\OperatorTok{=}\StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\NormalTok{sns.lineplot(x}\OperatorTok{=}\NormalTok{x\_axis, y}\OperatorTok{=}\NormalTok{y\_pred[sample], } +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}Model Predictions\textquotesingle{}}\NormalTok{, linestyle}\OperatorTok{=}\StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.title(}\StringTok{"Model vs Original Forecast Performance"}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{"Demand"}\NormalTok{)} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:ForestModel2}}\label{figure-reffigforestmodel2} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{sample }\OperatorTok{=}\NormalTok{ np.random.choice(}\BuiltInTok{len}\NormalTok{(y\_test), }\DecValTok{100}\NormalTok{, replace}\OperatorTok{=}\VariableTok{False}\NormalTok{)} +\NormalTok{x\_axis }\OperatorTok{=} \BuiltInTok{range}\NormalTok{(}\BuiltInTok{len}\NormalTok{(sample))} + +\NormalTok{plt.figure(figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{14}\NormalTok{, }\DecValTok{6}\NormalTok{))} +\NormalTok{sns.lineplot(x}\OperatorTok{=}\NormalTok{x\_axis, y}\OperatorTok{=}\NormalTok{y\_test.iloc[sample], } +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}Actual Demand\textquotesingle{}}\NormalTok{, color}\OperatorTok{=}\StringTok{\textquotesingle{}black\textquotesingle{}}\NormalTok{)} +\NormalTok{sns.lineplot(x}\OperatorTok{=}\NormalTok{x\_axis, y}\OperatorTok{=}\NormalTok{y\_pred\_original.iloc[sample], } +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}Original Forecast\textquotesingle{}}\NormalTok{, linestyle}\OperatorTok{=}\StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\NormalTok{sns.lineplot(x}\OperatorTok{=}\NormalTok{x\_axis, y}\OperatorTok{=}\NormalTok{y\_pred[sample], } +\NormalTok{ label}\OperatorTok{=}\StringTok{\textquotesingle{}Model Predictions\textquotesingle{}}\NormalTok{, linestyle}\OperatorTok{=}\StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.title(}\StringTok{"Model vs Original Forecast Performance"}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{"Demand"}\NormalTok{)} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:MAPEXGBoost}}\label{figure-reffigmapexgboost} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{output\_df }\OperatorTok{=}\NormalTok{ test\_df.copy()} +\NormalTok{output\_df[}\StringTok{"xgb\_prediction"}\NormalTok{] }\OperatorTok{=}\NormalTok{ y\_pred} + +\NormalTok{export\_cols }\OperatorTok{=}\NormalTok{ [}\StringTok{"date\_time\_future"}\NormalTok{, }\StringTok{"total\_demand"}\NormalTok{, } + \StringTok{"forecast\_demand"}\NormalTok{, }\StringTok{"xgb\_prediction"}\NormalTok{]} +\NormalTok{output\_df[export\_cols].to\_csv(}\StringTok{"finalresultsxgboost.csv"}\NormalTok{, index}\OperatorTok{=}\VariableTok{False}\NormalTok{)} + + +\CommentTok{\# Calculate absolute percentage error per row} +\NormalTok{output\_df[}\StringTok{"abs\_pct\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ np.}\BuiltInTok{abs}\NormalTok{((output\_df[}\StringTok{"total\_demand"}\NormalTok{] }\OperatorTok{{-}} +\NormalTok{ output\_df[}\StringTok{"xgb\_prediction"}\NormalTok{]) }\OperatorTok{/}\NormalTok{ output\_df[}\StringTok{"total\_demand"}\NormalTok{]) }\OperatorTok{*} \DecValTok{100} + +\CommentTok{\# Extract hour from datetime} +\NormalTok{output\_df[}\StringTok{"Hour"}\NormalTok{] }\OperatorTok{=}\NormalTok{ pd.to\_datetime(output\_df[}\StringTok{"date\_time\_future"}\NormalTok{]).dt.hour} + +\CommentTok{\# Group by hour and calculate mean error} +\NormalTok{hourly\_error }\OperatorTok{=} +\NormalTok{ output\_df.groupby(}\StringTok{"Hour"}\NormalTok{)[}\StringTok{"abs\_pct\_error"}\NormalTok{].mean().reset\_index()} + +\CommentTok{\# Plot} +\NormalTok{plt.figure(figsize}\OperatorTok{=}\NormalTok{(}\DecValTok{10}\NormalTok{, }\DecValTok{5}\NormalTok{))} +\NormalTok{plt.plot(hourly\_error[}\StringTok{"Hour"}\NormalTok{], hourly\_error[}\StringTok{"abs\_pct\_error"}\NormalTok{], marker}\OperatorTok{=}\StringTok{\textquotesingle{}o\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.title(}\StringTok{"MAPE by Hour of Day"}\NormalTok{)} +\NormalTok{plt.xlabel(}\StringTok{"Hour of Day"}\NormalTok{)} +\NormalTok{plt.ylabel(}\StringTok{"MAPE"}\NormalTok{)} +\NormalTok{plt.grid(}\VariableTok{True}\NormalTok{)} +\NormalTok{plt.xticks(}\BuiltInTok{range}\NormalTok{(}\DecValTok{0}\NormalTok{, }\DecValTok{24}\NormalTok{))} +\NormalTok{plt.tight\_layout()} +\NormalTok{plt.show()} +\end{Highlighting} +\end{Shaded} + +\subsection*{Figure \ref{fig:Cpmparison}}\label{figure-reffigcpmparison} + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{lm\_results }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{"data/results\_LM.csv"}\NormalTok{)} +\NormalTok{lm\_results[}\StringTok{"forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ lm\_results.total\_demand }\OperatorTok{{-}} +\NormalTok{ lm\_results.lm\_prediction} + +\NormalTok{sarima\_results }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{"data/results\_SARIMA.csv"}\NormalTok{)} +\NormalTok{sarima\_results[}\StringTok{"forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ sarima\_results.total\_demand }\OperatorTok{{-}} +\NormalTok{ sarima\_results.sarima\_prediction} + +\NormalTok{xgboost\_results }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{"data/results\_XGBoost.csv"}\NormalTok{)} +\NormalTok{xgboost\_results[}\StringTok{"forecast\_error"}\NormalTok{] }\OperatorTok{=}\NormalTok{ xgboost\_results.total\_demand }\OperatorTok{{-}} +\NormalTok{ xgboost\_results.xgb\_prediction} + +\NormalTok{decisionT\_results }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{\textquotesingle{}data/results\_DecisionTree.csv\textquotesingle{}}\NormalTok{)} +\NormalTok{decisionT\_results[}\StringTok{"forecast\_error"}\NormalTok{] }\OperatorTok{=} +\NormalTok{ decisionT\_results.total\_demand }\OperatorTok{{-}}\NormalTok{ decisionT\_results.model\_prediction} + +\NormalTok{results\_all }\OperatorTok{=}\NormalTok{ \{}\StringTok{"Linear"}\NormalTok{: lm\_results, } + \StringTok{"SARIMA"}\NormalTok{: sarima\_results, } + \StringTok{"XGBoost"}\NormalTok{: xgboost\_results,} + \StringTok{"Decision Tree"}\NormalTok{: decisionT\_results\}} + +\NormalTok{colors }\OperatorTok{=}\NormalTok{ [}\StringTok{\textquotesingle{}\#1f77b4\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}\#ff7f0e\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}g\textquotesingle{}}\NormalTok{, }\StringTok{\textquotesingle{}\#7f7f7f\textquotesingle{}}\NormalTok{]} + +\NormalTok{plt.subplots(}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, figsize }\OperatorTok{=}\NormalTok{ (}\DecValTok{16}\NormalTok{,}\DecValTok{7}\NormalTok{))} + +\NormalTok{plt.subplot(}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{,}\DecValTok{1}\NormalTok{)} +\ControlFlowTok{for}\NormalTok{ i, model }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{(results\_all):} +\NormalTok{ model\_result }\OperatorTok{=}\NormalTok{ results\_all[model]} +\NormalTok{ sns.kdeplot(model\_result.forecast\_error, label }\OperatorTok{=}\NormalTok{ model, } +\NormalTok{ color }\OperatorTok{=}\NormalTok{ colors[i], alpha }\OperatorTok{=} \FloatTok{0.8}\NormalTok{)} + +\NormalTok{sns.kdeplot(lm\_results.total\_demand }\OperatorTok{{-}} +\NormalTok{ lm\_results.forecast\_demand, label }\OperatorTok{=} \StringTok{"AEMO"}\NormalTok{, color }\OperatorTok{=} \StringTok{\textquotesingle{}r\textquotesingle{}}\NormalTok{, ls }\OperatorTok{=} \StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.xlim(}\OperatorTok{{-}}\DecValTok{1200}\NormalTok{, }\DecValTok{1200}\NormalTok{)}\OperatorTok{;} +\NormalTok{plt.ylim(}\DecValTok{0}\NormalTok{, }\FloatTok{0.0025}\NormalTok{)} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}Forecast Error\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.grid()} +\NormalTok{plt.legend()} +\NormalTok{plt.title(}\StringTok{"Distribution of Error"}\NormalTok{)}\OperatorTok{;} + +\NormalTok{plt.subplot(}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{)} +\ControlFlowTok{for}\NormalTok{ i, model }\KeywordTok{in} \BuiltInTok{enumerate}\NormalTok{(results\_all):} +\NormalTok{ model\_result }\OperatorTok{=}\NormalTok{ results\_all[model]} +\NormalTok{ sns.kdeplot(}\BuiltInTok{abs}\NormalTok{(model\_result.forecast\_error), } +\NormalTok{ label }\OperatorTok{=}\NormalTok{ model, color }\OperatorTok{=}\NormalTok{ colors[i], alpha }\OperatorTok{=} \FloatTok{0.8}\NormalTok{)} + +\NormalTok{sns.kdeplot(}\BuiltInTok{abs}\NormalTok{(lm\_results.total\_demand }\OperatorTok{{-}} +\NormalTok{ lm\_results.forecast\_demand), label }\OperatorTok{=} \StringTok{"AEMO"}\NormalTok{, color }\OperatorTok{=} \StringTok{\textquotesingle{}r\textquotesingle{}}\NormalTok{, } +\NormalTok{ ls }\OperatorTok{=} \StringTok{\textquotesingle{}{-}{-}\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.xlim(}\DecValTok{0}\NormalTok{, }\DecValTok{1000}\NormalTok{)}\OperatorTok{;} +\NormalTok{plt.ylim(}\DecValTok{0}\NormalTok{, }\FloatTok{0.0046}\NormalTok{)} +\NormalTok{plt.grid()} +\NormalTok{plt.legend()} +\NormalTok{plt.xlabel(}\StringTok{\textquotesingle{}abs(Forecast Error)\textquotesingle{}}\NormalTok{)} +\NormalTok{plt.title(}\StringTok{"Distribution of Absolute Error"}\NormalTok{)}\OperatorTok{;} +\end{Highlighting} +\end{Shaded} + + + + + + + + +\end{document} + diff --git a/report/elsarticle-harv.bst b/report/elsarticle-harv.bst new file mode 100644 index 000000000..3bf1acb43 --- /dev/null +++ b/report/elsarticle-harv.bst @@ -0,0 +1,1598 @@ +%% +%% This is file `elsarticle-harv.bst' (Version 2.1), +%% +%% Copyright 2009-2020 Elsevier Ltd +%% +%% This file is part of the 'Elsarticle Bundle'. +%% --------------------------------------------- +%% +%% It may be distributed under the conditions of the LaTeX Project Public +%% License, either version 1.2 of this license or (at your option) any +%% later version. The latest version of this license is in +%% http://www.latex-project.org/lppl.txt +%% and version 1.2 or later is part of all distributions of LaTeX +%% version 1999/12/01 or later. +%% +%% $Id: elsarticle-harv.bst 194 2020-11-23 11:29:27Z rishi $ +%% +%% $URL: https://lenova.river-valley.com/svn/elsarticle/trunk/elsarticle-harv.bst $ +%% + +ENTRY + { address + archive + author + booktitle + chapter + collaboration + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + url + doi + eprint + pubmed + } + {} + { label extra.label sort.label short.list } + +INTEGERS { output.state before.all mid.sentence after.sentence after.block } + +STRINGS { urlprefix doiprefix eprintprefix pubmedprefix } + +FUNCTION {init.web.variables} +{ + "\URLprefix " 'urlprefix := + "\DOIprefix" 'doiprefix := + "\ArXivprefix " 'eprintprefix := + "\Pubmedprefix " 'pubmedprefix := +} + +FUNCTION {init.state.consts} +{ #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +FUNCTION {output.comma} +{ ", " * write$} + +FUNCTION {output.nonnull} +{ 's := + output.state mid.sentence = + { ". " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { ", " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} +FUNCTION {output.commanull} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { ", " * write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {output.book.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry} +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\textit{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "Eds." } + +FUNCTION {bbl.editor} +{ "Ed." } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "ed." } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pp." } + +FUNCTION {bbl.page} +{ "p." } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Comput. Surv."} + +MACRO {acta} {"Acta Inf."} + +MACRO {cacm} {"Commun. ACM"} + +MACRO {ibmjrd} {"IBM J. Res. Dev."} + +MACRO {ibmsj} {"IBM Syst.~J."} + +MACRO {ieeese} {"IEEE Trans. Software Eng."} + +MACRO {ieeetc} {"IEEE Trans. Comput."} + +MACRO {ieeetcad} + {"IEEE Trans. Comput. Aid. Des."} + +MACRO {ipl} {"Inf. Process. Lett."} + +MACRO {jacm} {"J.~ACM"} + +MACRO {jcss} {"J.~Comput. Syst. Sci."} + +MACRO {scp} {"Sci. Comput. Program."} + +MACRO {sicomp} {"SIAM J. Comput."} + +MACRO {tocs} {"ACM Trans. Comput. Syst."} + +MACRO {tods} {"ACM Trans. Database Syst."} + +MACRO {tog} {"ACM Trans. Graphic."} + +MACRO {toms} {"ACM Trans. Math. Software"} + +MACRO {toois} {"ACM Trans. Office Inf. Syst."} + +MACRO {toplas} {"ACM Trans. Progr. Lang. Syst."} + +MACRO {tcs} {"Theor. Comput. Sci."} + +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + "\bibinfo{" swap$ * "}{" * swap$ * "}" * + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} + +STRINGS { bibinfo} + +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}{, jj}{, f{.}.}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + "," * + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + format.names +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names + duplicate$ empty$ 'skip$ + { collaboration "collaboration" bibinfo.check + duplicate$ empty$ 'skip$ + { " (" swap$ * ")" * } + if$ + * + } + if$ +} + +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + " " * + get.bbl.editor + capitalize + "(" swap$ * ")" * + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem} +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in %capitalize + ":" * + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + ", " swap$ * +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +%FUNCTION {format.pages} +%{ pages duplicate$ empty$ 'skip$ +% { duplicate$ multi.page.check +% { +% n.dashify +% } +% { +% } +% if$ +% "pages" bibinfo.check +% } +% if$ +%} + +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ", " * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + { "" } + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + " " * + get.bbl.editor + capitalize + "(" swap$ * "), " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.check format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +FUNCTION {print.url} + {url duplicate$ empty$ + { pop$ "" } + { new.sentence + urlprefix "\url{" * swap$ * "}" * + } + if$ + } + +FUNCTION {print.doi} + {doi duplicate$ empty$ + { pop$ "" } + { new.sentence + doiprefix "\doi{" * swap$ * "}" * + } + if$ + } + +FUNCTION {print.eprint} + {eprint duplicate$ empty$ + { pop$ "" } + { new.sentence + duplicate$ "\href{http://arxiv.org/abs/" swap$ * "}{{\tt arXiv:" * swap$ * "}}" * } + if$ + } + +FUNCTION {print.pubmed} + {pubmed duplicate$ empty$ + { pop$ "" } + { new.sentence + pubmedprefix "\Pubmed{" * swap$ * "}" * + } + if$ + } + +FUNCTION {webpage} +{ "%Type = Webpage" write$ + output.bibitem + format.authors "author" output.check + author format.key output + author empty$ + { + format.title "title" output.check + new.block + format.date "year" output.check + date.block + } + { + format.date "year" output.check + date.block + format.title "title" output.check + new.block +} + if$ + print.url output + fin.entry +} + + +FUNCTION {article} +{ "%Type = Article" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + "journal" output.check + add.blank + format.vol.num.pages output + } + { format.article.crossref output.nonnull + } + if$ + format.journal.pages + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {book} +{ "%Type = Book" write$ + output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + new.block + format.number.series output + format.edition output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {booklet} +{ "%Type = Booklet" write$ + output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {inbook} +{ "%Type = Inbook" write$ + output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.publisher.address output + format.bvolume output + format.chapter.pages "chapter and pages" output.check + new.block + format.number.series output + new.sentence + } + { + format.chapter.pages "chapter and pages" output.check + new.block + format.book.crossref output.nonnull + } + if$ + format.pages "pages" output.check + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {incollection} +{ "%Type = Incollection" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.book.check + new.sentence + crossref missing$ + { format.in.ed.booktitle "booktitle" output.book.check + format.edition output + format.publisher.address output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + format.pages "pages" output.check + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {inproceedings} +{ "%Type = Inproceedings" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.book.check + new.sentence + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + format.publisher.address output + } + if$ +% format.bvolume output +% format.number.series output +% format.pages output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + format.pages "pages" output.check + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {conference} { inproceedings } + +FUNCTION {manual} +{ "%Type = Manual" write$ + output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ "%Type = Masterthesis" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.btitle + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {misc} +{ "%Type = Misc" write$ + output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {phdthesis} +{ "%Type = Phdthesis" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ "%Type = Proceedings" write$ + output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {techreport} +{ "%Type = Techreport" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.btitle + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ "%Type = Unpublished" write$ + output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + format.title "title" output.check + new.block + print.url output + print.doi output + print.eprint output + print.pubmed output + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ f{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ +% { "{\natexlab{" swap$ * "}}" * } + { "" swap$ * "" * } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ + "\providecommand{\url}[1]{\texttt{#1}}" + write$ newline$ + "\providecommand{\href}[2]{#2}" + write$ newline$ + "\providecommand{\path}[1]{#1}" + write$ newline$ + "\providecommand{\DOIprefix}{doi:}" + write$ newline$ + "\providecommand{\ArXivprefix}{arXiv:}" + write$ newline$ + "\providecommand{\URLprefix}{URL: }" + write$ newline$ + "\providecommand{\Pubmedprefix}{pmid:}" + write$ newline$ + "\providecommand{\doi}[1]{\href{http://dx.doi.org/#1}{\path{#1}}}" + write$ newline$ + "\providecommand{\Pubmed}[1]{\href{pmid:#1}{\path{#1}}}" + write$ newline$ + "\providecommand{\bibinfo}[2]{#2}" + write$ newline$ + "\ifx\xfnm\relax \def\xfnm[#1]{\unskip,\space#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.state.consts} +EXECUTE {init.web.variables} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `elsarticle-harv.bst'. +%% +%% Change log: +%% ----------- +%% 22.04.2011 +%% +%% 10.08.2012 +%% a. doi, url, eprint, pmid added +%% b. Bibtype `webpage' defined +%% +%% 30.08.2012 +%% a. collaboration added. +%% + + diff --git a/report/elsarticle-num.bst b/report/elsarticle-num.bst new file mode 100644 index 000000000..0257b4fe7 --- /dev/null +++ b/report/elsarticle-num.bst @@ -0,0 +1,1517 @@ +%% +%% Copyright 2007, 2008, 2009 Elsevier Ltd +%% +%% This file is part of the 'Elsarticle Bundle'. +%% --------------------------------------------- +%% +%% It may be distributed under the conditions of the LaTeX Project Public +%% License, either version 1.2 of this license or (at your option) any +%% later version. The latest version of this license is in +%% http://www.latex-project.org/lppl.txt +%% and version 1.2 or later is part of all distributions of LaTeX +%% version 1999/12/01 or later. +%% +%% The list of all files belonging to the 'Elsarticle Bundle' is +%% given in the file `manifest.txt'. +%% +%%% Modification of BibTeX style file elsarticle-num.bst +%%% ... by urlbst, version 0.6 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added hyperref support. +%%% Original headers follow... + +%% +%% This is file `elsarticle-num.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `,seq-no,nm-init,ed-au,dt-end,yr-par,yrp-x,jttl-rm,thtit-a,vnum-sp,volp-blk,jdt-p,pp-last,jnm-x,btit-rm,bt-rm,pub-date,pub-xpar,pre-edn,url,url-nl,edpar,blk-com,in-col,pp,ed,abr,ednx,ord,jabr,and-xcom,xand,em-x,nfss') +%% After docstrip generation some manual changes were made (SP) + +%% ---------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label } + +INTEGERS { output.state before.all mid.sentence after.sentence after.block } + +STRINGS { urlintro eprinturl eprintprefix doiprefix doiurl openinlinelink closeinlinelink } % urlbst... +INTEGERS { hrefform inlinelinks makeinlinelink addeprints adddoiresolver } +FUNCTION {init.urlbst.variables} +{ + "Available from: " 'urlintro := % prefix before URL + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref + "http://dx.doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + % the following are internal state variables, not config constants + #0 'makeinlinelink := % state variable managed by setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} + +STRINGS { s t } + +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION {setup.inlinelink} +{ makeinlinelink + { hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := + } + { hrefform #2 = % hyperref + { "\href{" url * "}{" * 'openinlinelink := + "}" 'closeinlinelink := + } + 'skip$ + if$ % hrefform #2 = + } + if$ % hrefform #1 = + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets "cited " lastchecked * } + if$ +} + +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} + +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} + +FUNCTION {fin.entry.original} +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} + +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} + +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + add.blank +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} + +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} + +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} + +FUNCTION {new.block.checka} +{ empty$ + 'skip$ + 'new.block + if$ +} + +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} + +FUNCTION {new.sentence.checka} +{ empty$ + 'skip$ + 'new.sentence + if$ +} + +FUNCTION {new.sentence.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.sentence + if$ +} + +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} + +FUNCTION {emphasize} +{ skip$ } + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "Eds." } + +FUNCTION {bbl.editor} +{ "Ed." } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "Edition" } + +FUNCTION {bbl.volume} +{ "Vol." } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "no." } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pp." } + +FUNCTION {bbl.page} +{ "p." } + +FUNCTION {bbl.chapter} +{ "Ch." } + +FUNCTION {bbl.techrep} +{ "Tech. Rep." } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +FUNCTION {bbl.first} +{ "1st" } + +FUNCTION {bbl.second} +{ "2nd" } + +FUNCTION {bbl.third} +{ "3rd" } + +FUNCTION {bbl.fourth} +{ "4th" } + +FUNCTION {bbl.fifth} +{ "5th" } + +FUNCTION {bbl.st} +{ "st" } + +FUNCTION {bbl.nd} +{ "nd" } + +FUNCTION {bbl.rd} +{ "rd" } + +FUNCTION {bbl.th} +{ "th" } + +MACRO {jan} {"Jan."} + +MACRO {feb} {"Feb."} + +MACRO {mar} {"Mar."} + +MACRO {apr} {"Apr."} + +MACRO {may} {"May"} + +MACRO {jun} {"Jun."} + +MACRO {jul} {"Jul."} + +MACRO {aug} {"Aug."} + +MACRO {sep} {"Sep."} + +MACRO {oct} {"Oct."} + +MACRO {nov} {"Nov."} + +MACRO {dec} {"Dec."} + +FUNCTION {eng.ord} +{ duplicate$ "1" swap$ * + #-2 #1 substring$ "1" = + { bbl.th * } + { duplicate$ #-1 #1 substring$ + duplicate$ "1" = + { pop$ bbl.st * } + { duplicate$ "2" = + { pop$ bbl.nd * } + { "3" = + { bbl.rd * } + { bbl.th * } + if$ + } + if$ + } + if$ + } + if$ +} + +MACRO {acmcs} {"ACM Comput. Surv."} + +MACRO {acta} {"Acta Inf."} + +MACRO {cacm} {"Commun. ACM"} + +MACRO {ibmjrd} {"IBM J. Res. Dev."} + +MACRO {ibmsj} {"IBM Syst.~J."} + +MACRO {ieeese} {"IEEE Trans. Softw. Eng."} + +MACRO {ieeetc} {"IEEE Trans. Comput."} + +MACRO {ieeetcad} + {"IEEE Trans. Comput.-Aided Design Integrated Circuits"} + +MACRO {ipl} {"Inf. Process. Lett."} + +MACRO {jacm} {"J.~ACM"} + +MACRO {jcss} {"J.~Comput. Syst. Sci."} + +MACRO {scp} {"Sci. Comput. Programming"} + +MACRO {sicomp} {"SIAM J. Comput."} + +MACRO {tocs} {"ACM Trans. Comput. Syst."} + +MACRO {tods} {"ACM Trans. Database Syst."} + +MACRO {tog} {"ACM Trans. Gr."} + +MACRO {toms} {"ACM Trans. Math. Softw."} + +MACRO {toois} {"ACM Trans. Office Inf. Syst."} + +MACRO {toplas} {"ACM Trans. Prog. Lang. Syst."} + +MACRO {tcs} {"Theoretical Comput. Sci."} + +FUNCTION {write.url} +{ url empty$ + { skip$ } + { "\newline\urlprefix\url{" url * "}" * write$ newline$ } + if$ +} + +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 's := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{f.~}{vv~}{ll}{, jj}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + "," * + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} +FUNCTION {format.names.ed} +{ format.names } +FUNCTION {format.authors} +{ author empty$ + { "" } + { author format.names } + if$ +} + +FUNCTION {format.editors} +{ editor empty$ + { "" } + { editor format.names + editor num.names$ #1 > + { " (" * bbl.editors * ")" * } + { " (" * bbl.editor * ")" * } + if$ + } + if$ +} + +FUNCTION {format.in.editors} +{ editor empty$ + { "" } + { editor format.names.ed + editor num.names$ #1 > + { " (" * bbl.editors * ")" * } + { " (" * bbl.editor * ")" * } + if$ + } + if$ +} + +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * + } + if$ +} + +FUNCTION {format.title} +{ title empty$ + { "" } + { title "t" change.case$ + } + if$ +} + +FUNCTION {output.bibitem.original} +{ newline$ + "\bibitem{" write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in + ":" * + " " * } + +FUNCTION {format.date} +{ year empty$ + { month empty$ + { "" } + { "there's a month but no year in " cite$ * warning$ + month + } + if$ + } + { month empty$ + 'year + { month " " * year * } + if$ + } + if$ + duplicate$ empty$ + 'skip$ + { + before.all 'output.state := + " (" swap$ * ")" * + } + if$ +} + +FUNCTION{format.year} +{ year duplicate$ empty$ + { "empty year in " cite$ * warning$ pop$ "" } + { "(" swap$ * ")" * } + if$ +} + +FUNCTION {format.btitle} +{ title +} + +FUNCTION {tie.or.space.connect} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ * * +} + +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} + +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.connect + series empty$ + 'skip$ + { bbl.of space.word * series emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} + +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.connect + series empty$ + { "there's a number but no series in " cite$ * warning$ } + { bbl.in space.word * series * } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {is.num} +{ chr.to.int$ + duplicate$ "0" chr.to.int$ < not + swap$ "9" chr.to.int$ > not and +} + +FUNCTION {extract.num} +{ duplicate$ 't := + "" 's := + { t empty$ not } + { t #1 #1 substring$ + t #2 global.max$ substring$ 't := + duplicate$ is.num + { s swap$ * 's := } + { pop$ "" 't := } + if$ + } + while$ + s empty$ + 'skip$ + { pop$ s } + if$ +} + +FUNCTION {convert.edition} +{ edition extract.num "l" change.case$ 's := + s "first" = s "1" = or + { bbl.first 't := } + { s "second" = s "2" = or + { bbl.second 't := } + { s "third" = s "3" = or + { bbl.third 't := } + { s "fourth" = s "4" = or + { bbl.fourth 't := } + { s "fifth" = s "5" = or + { bbl.fifth 't := } + { s #1 #1 substring$ is.num + { s eng.ord 't := } + { edition 't := } + if$ + } + if$ + } + if$ + } + if$ + } + if$ + } + if$ + t +} + +FUNCTION {format.edition} +{ edition empty$ + { "" } + { output.state mid.sentence = + { convert.edition "l" change.case$ " " * bbl.edition * } + { convert.edition "t" change.case$ " " * bbl.edition * } + if$ + } + if$ +} + +INTEGERS { multiresult } + +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} + +FUNCTION {format.pages} +{ pages empty$ + { "" } + { pages multi.page.check + { bbl.pages pages n.dashify tie.or.space.connect } + { bbl.page pages tie.or.space.connect } + if$ + } + if$ +} + +FUNCTION {format.journal.pages} +{ pages empty$ + 'skip$ + { duplicate$ empty$ + { pop$ format.pages } + { + " " * + format.year * " " * + pages n.dashify * + } + if$ + } + if$ +} + +FUNCTION {format.vol.num.pages} +{ + % volume field.or.null + " " + volume empty$ + { pop$ "" } + { volume * } + if$ + number empty$ + 'skip$ + { + "~(" number * ")" * * + volume empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + { "" } + { type empty$ + { bbl.chapter } + { type "l" change.case$ } + if$ + chapter tie.or.space.connect + } + if$ +} + +FUNCTION {format.in.ed.booktitle} +{ booktitle empty$ + { "" } + { editor empty$ + { word.in booktitle * } + { word.in format.in.editors * ", " * + booktitle * } + if$ + } + if$ +} + +FUNCTION {empty.misc.check} +{ author empty$ title empty$ howpublished empty$ + month empty$ year empty$ note empty$ + and and and and and + { "all relevant fields are empty in " cite$ * warning$ } + 'skip$ + if$ +} + +FUNCTION {format.thesis.type} +{ type empty$ + 'skip$ + { pop$ + type "t" change.case$ + } + if$ +} + +FUNCTION {format.tr.number} +{ type empty$ + { bbl.techrep } + 'type + if$ + number empty$ + { "t" change.case$ } + { number tie.or.space.connect } + if$ +} + +FUNCTION {format.article.crossref} +{ + key empty$ + { journal empty$ + { "need key or journal for " cite$ * " to crossref " * crossref * + warning$ + "" + } + { word.in journal emphasize * } + if$ + } + { word.in key * " " *} + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.crossref.editor} +{ editor #1 "{vv~}{ll}" format.name$ + editor num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * editor #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {format.book.crossref} +{ volume empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + word.in + } + { bbl.volume volume tie.or.space.connect + bbl.of space.word * + } + if$ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { series empty$ + { "need editor, key, or series for " cite$ * " to crossref " * + crossref * warning$ + "" * + } + { series emphasize * } + if$ + } + { key * } + if$ + } + { format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.incoll.inproc.crossref} +{ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { booktitle empty$ + { "need editor, key, or booktitle for " cite$ * " to crossref " * + crossref * warning$ + "" + } + { word.in booktitle * } + if$ + } + { word.in key * " " *} + if$ + } + { word.in format.crossref.editor * " " *} + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.org.or.pub} +{ 't := + "" + year empty$ + { "empty year in " cite$ * warning$ } + 'skip$ + if$ + address empty$ t empty$ and + year empty$ and + 'skip$ + { + t empty$ + { address empty$ + 'skip$ + { address * } + if$ + } + { t * + address empty$ + 'skip$ + { ", " * address * } + if$ + } + if$ + year empty$ + 'skip$ + { t empty$ address empty$ and + 'skip$ + { ", " * } + if$ + year * + } + if$ + } + if$ +} + +FUNCTION {format.publisher.address} +{ publisher empty$ + { "empty publisher in " cite$ * warning$ + "" + } + { publisher } + if$ + format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization empty$ + { "" } + { organization } + if$ + format.org.or.pub +} + +FUNCTION {make.href.null} +{ + pop$ +} +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ +} + +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { setup.inlinelink % add some artificial link text here, as a fallback + "[link]" output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'setup.inlinelink if$ + format.title "title" output.check + inbrackets "online" output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + crossref missing$ + { journal + "journal" output.check + % add.blank + before.all 'output.state := + format.vol.num.pages output + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + format.journal.pages + format.note output + fin.entry + write.url +} + +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.btitle "title" output.check + crossref missing$ + { format.edition output + format.bvolume output + format.number.series output + format.publisher.address output + } + { + format.book.crossref output.nonnull + } + if$ + format.note output + fin.entry + write.url +} + +FUNCTION {booklet} +{ output.bibitem + format.authors output + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + howpublished output + address output + format.note output + format.date output + fin.entry + write.url +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.btitle "title" output.check + crossref missing$ + { + format.edition output + format.bvolume output + format.number.series output + format.publisher.address output + format.chapter.pages "chapter and pages" output.check + } + { + format.chapter.pages "chapter and pages" output.check + format.book.crossref output.nonnull + } + if$ + format.pages "pages" output.check + format.note output + fin.entry + write.url +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.publisher.address output + format.chapter.pages output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + format.pages "pages" output.check + format.note output + fin.entry + write.url +} + +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + publisher empty$ + { format.organization.address output } + { organization output + format.publisher.address output + } + if$ + } + { format.incoll.inproc.crossref output.nonnull + } + if$ + format.pages "pages" output.check + format.note output + fin.entry + write.url +} + +FUNCTION {conference} { inproceedings } + +FUNCTION {manual} +{ output.bibitem + author empty$ + { organization empty$ + 'skip$ + { organization output.nonnull + address output + } + if$ + } + { format.authors output.nonnull } + if$ + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.btitle "title" output.check + author empty$ + { organization empty$ + { + address output + } + 'skip$ + if$ + } + { + organization output + address output + } + if$ + format.edition output + format.note output + format.date output + fin.entry + write.url +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + bbl.mthesis format.thesis.type output.nonnull + school "school" output.check + address output + format.note output + format.date "year" output.check + fin.entry + write.url +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title output + howpublished output + format.note output + format.date output + fin.entry + write.url + empty.misc.check +} + +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + bbl.phdthesis format.thesis.type output.nonnull + school "school" output.check + address output + format.note output + format.date "year" output.check + fin.entry + write.url +} + +FUNCTION {proceedings} +{ output.bibitem + editor empty$ + { organization output } + { format.editors output.nonnull } + if$ + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + editor empty$ + { publisher empty$ + 'skip$ + { + format.publisher.address output + } + if$ + } + { publisher empty$ + { + format.organization.address output } + { + organization output + format.publisher.address output + } + if$ + } + if$ + format.note output + fin.entry + write.url +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + format.tr.number output.nonnull + institution "institution" output.check + address output + format.note output + format.date "year" output.check + fin.entry + write.url +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + title empty$ 'skip$ 'setup.inlinelink if$ % urlbst + format.title "title" output.check + format.note "note" output.check + format.date output + fin.entry + write.url +} + +FUNCTION {default.type} { misc } + +READ + +STRINGS { longest.label } + +INTEGERS { number.label longest.label.width } + +FUNCTION {initialize.longest.label} +{ "" 'longest.label := + #1 'number.label := + #0 'longest.label.width := +} + +FUNCTION {longest.label.pass} +{ number.label int.to.str$ 'label := + number.label #1 + 'number.label := + label width$ longest.label.width > + { label 'longest.label := + label width$ 'longest.label.width := + } + 'skip$ + if$ +} + +EXECUTE {initialize.longest.label} + +ITERATE {longest.label.pass} + +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" longest.label * "}" * + write$ newline$ + "\expandafter\ifx\csname url\endcsname\relax" + write$ newline$ + " \def\url#1{\texttt{#1}}\fi" + write$ newline$ + "\expandafter\ifx\csname urlprefix\endcsname\relax\def\urlprefix{URL }\fi" + write$ newline$ + "\expandafter\ifx\csname href\endcsname\relax" + write$ newline$ + " \def\href#1#2{#2} \def\path#1{#1}\fi" + write$ newline$ +} + +EXECUTE {begin.bib} + +EXECUTE {init.urlbst.variables} +EXECUTE {init.state.consts} + +ITERATE {call.type$} + +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} + +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `elsarticle-num.bst'. diff --git a/report/elsevier-with-titles.csl b/report/elsevier-with-titles.csl new file mode 100644 index 000000000..135329d2e --- /dev/null +++ b/report/elsevier-with-titles.csl @@ -0,0 +1,149 @@ + + diff --git a/report/header.tex b/report/header.tex new file mode 100644 index 000000000..2f4e76425 --- /dev/null +++ b/report/header.tex @@ -0,0 +1,200 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% A small sample UNSW Coursework Masters thesis file. +% Any questions to Ian Doust i.doust@unsw.edu.au and/or Gery Geenens ggeenens@unsw.edu.au +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% The first part pulls in a UNSW Thesis class file. This one is +% slightly nonstandard and has been set up to do a couple of +% things automatically +% + +%%%%%%%%%%%%%%%%% +%% Precisely one of the next four lines should be uncommented. +%% Choose the one which matches your degree, uncomment it, and comment out the other two! +%\documentclass[mfin,12pt]{unswthesis} %% For Master of Financial Mathematics +%\documentclass[mmath,12pt]{unswthesis} %% For Master of Mathematics +%\documentclass[mstat,12pt]{unswthesis} %% For Master of Statistics +%%%%%%%%%%%%%%%%% + + + +\linespread{1} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage{amsthm} +\usepackage{latexsym,amsmath} +\usepackage{graphicx} +\usepackage{afterpage} +\usepackage[colorlinks]{hyperref} + \hypersetup{ + colorlinks=true, + linkcolor=blue, + filecolor=blue, + citecolor= black, + urlcolor=cyan, + } +\usepackage{textcomp} +\usepackage{longtable} +\usepackage{url} +\usepackage{xurl} +\usepackage{booktabs} +\usepackage{float} +\let\origfigure\figure +\let\endorigfigure\endfigure +\renewenvironment{figure}[1][2] { + \expandafter\origfigure\expandafter[H] +} { + \endorigfigure +} +\usepackage{multirow} +\usepackage{caption} +\usepackage[normalem]{ulem} +\usepackage{hanging} +\useunder{\uline}{\ul}{} +\usepackage{multicol} +\usepackage{microtype} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% The following are some simple LaTeX macros to give some +% commonly used letters in funny fonts. You may need more or less of +% these +% +\newcommand{\R}{\mathbb{R}} +\newcommand{\Q}{\mathbb{Q}} +\newcommand{\C}{\mathbb{C}} +\newcommand{\N}{\mathbb{N}} +\newcommand{\F}{\mathbb{F}} +\newcommand{\PP}{\mathbb{P}} +\newcommand{\T}{\mathbb{T}} +\newcommand{\Z}{\mathbb{Z}} +\newcommand{\B}{\mathfrak{B}} +\newcommand{\BB}{\mathcal{B}} +\newcommand{\M}{\mathfrak{M}} +\newcommand{\X}{\mathfrak{X}} +\newcommand{\Y}{\mathfrak{Y}} +\newcommand{\CC}{\mathcal{C}} +\newcommand{\E}{\mathbb{E}} +\newcommand{\cP}{\mathcal{P}} +\newcommand{\cS}{\mathcal{S}} +\newcommand{\A}{\mathcal{A}} +\newcommand{\ZZ}{\mathcal{Z}} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% The following are much more esoteric commands that I have left in +% so that this file still processes. Use or delete as you see fit +% +\newcommand{\bv}[1]{\mbox{BV($#1$)}} +\newcommand{\comb}[2]{\left(\!\!\!\begin{array}{c}#1\\#2\end{array}\!\!\!\right) +} +\newcommand{\Lat}{{\rm Lat}} +\newcommand{\var}{\mathop{\rm var}} +\newcommand{\Pt}{{\mathcal P}} +\def\tr(#1){{\rm trace}(#1)} +\def\Exp(#1){{\mathbb E}(#1)} +\def\Exps(#1){{\mathbb E}\sparen(#1)} +\newcommand{\floor}[1]{\left\lfloor #1 \right\rfloor} +\newcommand{\ceil}[1]{\left\lceil #1 \right\rceil} +\newcommand{\hatt}[1]{\widehat #1} +\newcommand{\modeq}[3]{#1 \equiv #2 \,(\text{mod}\, #3)} +\newcommand{\rmod}{\,\mathrm{mod}\,} +\newcommand{\p}{\hphantom{+}} +\newcommand{\vect}[1]{\mbox{\boldmath $ #1 $}} +\newcommand{\reff}[2]{\ref{#1}.\ref{#2}} +\newcommand{\psum}[2]{\sum_{#1}^{#2}\!\!\!'\,\,} +\newcommand{\bin}[2]{\left( \begin{array}{@{}c@{}} + #1 \\ #2 + \end{array}\right) } +% +% Macros - some of these are in plain TeX (gasp!) +% +\newcommand{\be}{($\beta$)} +\newcommand{\eqp}{\mathrel{{=}_p}} +\newcommand{\ltp}{\mathrel{{\prec}_p}} +\newcommand{\lep}{\mathrel{{\preceq}_p}} +\def\brack#1{\left \{ #1 \right \}} +\def\bul{$\bullet$\ } +\def\cl{{\rm cl}} +\let\del=\partial +\def\enditem{\par\smallskip\noindent} +\def\implies{\Rightarrow} +\def\inpr#1,#2{\t \hbox{\langle #1 , #2 \rangle} \t} +\def\ip<#1,#2>{\langle #1,#2 \rangle} +\def\lp{\ell^p} +\def\maxb#1{\max \brack{#1}} +\def\minb#1{\min \brack{#1}} +\def\mod#1{\left \vert #1 \right \vert} +\def\norm#1{\left \Vert #1 \right \Vert} +\def\paren(#1){\left( #1 \right)} +\def\qed{\hfill \hbox{$\Box$} \smallskip} +\def\sbrack#1{\Bigl \{ #1 \Bigr \} } +\def\ssbrack#1{ \{ #1 \} } +\def\smod#1{\Bigl \vert #1 \Bigr \vert} +\def\smmod#1{\bigl \vert #1 \bigr \vert} +\def\ssmod#1{\vert #1 \vert} +\def\sspmod#1{\vert\, #1 \, \vert} +\def\snorm#1{\Bigl \Vert #1 \Bigr \Vert} +\def\ssnorm#1{\Vert #1 \Vert} +\def\sparen(#1){\Bigl ( #1 \Bigr )} + +\newcommand\blankpage{% + \null + \thispagestyle{empty}% + \addtocounter{page}{-1}% + \newpage} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% These environments allow you to get nice numbered headings +% for your Theorems, Definitions etc. +% +% Environments +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\newtheorem{theorem}{Theorem}[section] +\newtheorem{lemma}[theorem]{Lemma} +\newtheorem{proposition}[theorem]{Proposition} +\newtheorem{corollary}[theorem]{Corollary} +\newtheorem{conjecture}[theorem]{Conjecture} +\newtheorem{definition}[theorem]{Definition} +\newtheorem{example}[theorem]{Example} +\newtheorem{remark}[theorem]{Remark} +\newtheorem{question}[theorem]{Question} +\newtheorem{notation}[theorem]{Notation} +\numberwithin{equation}{section} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% If you've got some funny special words that LaTeX might not +% hyphenate properly, you can give it a helping hand: +% + +\hyphenation{Mar-cin-kie-wicz Rade-macher} + + +\newlength{\cslhangindent} +\setlength{\cslhangindent}{1.5em} +\newlength{\csllabelwidth} +\setlength{\csllabelwidth}{3em} +\newenvironment{CSLReferences}[2] % #1 hanging-ident, #2 entry spacing + {% don't indent paragraphs + \setlength{\parindent}{0pt} + % turn on hanging indent if param 1 is 1 + \ifodd #1 \everypar{\setlength{\hangindent}{\cslhangindent}}\ignorespaces\fi + % set entry spacing + \ifnum #2 > 0 + \setlength{\parskip}{#2\baselineskip} + \fi + }% + {} +\usepackage{calc} % for \widthof, \maxof +\newcommand{\CSLBlock}[1]{#1\hfill\break} +\newcommand{\CSLLeftMargin}[1]{\parbox[t]{\maxof{\widthof{#1}}{\csllabelwidth}}{#1}} +\newcommand{\CSLRightInline}[1]{\parbox[t]{\linewidth}{#1}} +\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1} + +\bibliographystyle{elsarticle-num} + + diff --git a/report/images/ACFForecastErrors.png b/report/images/ACFForecastErrors.png new file mode 100644 index 000000000..97ceea7ad Binary files /dev/null and b/report/images/ACFForecastErrors.png differ diff --git a/report/images/Comparison.png b/report/images/Comparison.png new file mode 100644 index 000000000..8017af35e Binary files /dev/null and b/report/images/Comparison.png differ diff --git a/report/images/ForecastErrorCorrelations.png b/report/images/ForecastErrorCorrelations.png new file mode 100644 index 000000000..2e71509a0 Binary files /dev/null and b/report/images/ForecastErrorCorrelations.png differ diff --git a/report/images/ForestModel1.png b/report/images/ForestModel1.png new file mode 100644 index 000000000..9d63093f7 Binary files /dev/null and b/report/images/ForestModel1.png differ diff --git a/report/images/ForestModel2.png b/report/images/ForestModel2.png new file mode 100644 index 000000000..5a5217213 Binary files /dev/null and b/report/images/ForestModel2.png differ diff --git a/report/images/MAPEXGboost.png b/report/images/MAPEXGboost.png new file mode 100644 index 000000000..e5f055389 Binary files /dev/null and b/report/images/MAPEXGboost.png differ diff --git a/report/images/MAPEtuning.png b/report/images/MAPEtuning.png new file mode 100644 index 000000000..834f534c9 Binary files /dev/null and b/report/images/MAPEtuning.png differ diff --git a/report/images/MSEArima.png b/report/images/MSEArima.png new file mode 100644 index 000000000..405d3c775 Binary files /dev/null and b/report/images/MSEArima.png differ diff --git a/report/images/MSEtuning.png b/report/images/MSEtuning.png new file mode 100644 index 000000000..405d3c775 Binary files /dev/null and b/report/images/MSEtuning.png differ diff --git a/report/images/MaterialMethods.png b/report/images/MaterialMethods.png new file mode 100644 index 000000000..4913c6fa4 Binary files /dev/null and b/report/images/MaterialMethods.png differ diff --git a/report/images/PACFForecastErrors.png b/report/images/PACFForecastErrors.png new file mode 100644 index 000000000..7a86dc4a9 Binary files /dev/null and b/report/images/PACFForecastErrors.png differ diff --git a/report/images/PortionErrorTemp.png b/report/images/PortionErrorTemp.png new file mode 100644 index 000000000..ec30cadff Binary files /dev/null and b/report/images/PortionErrorTemp.png differ diff --git a/report/images/Stationarity1.png b/report/images/Stationarity1.png new file mode 100644 index 000000000..0477205ab Binary files /dev/null and b/report/images/Stationarity1.png differ diff --git a/report/images/Stationarity2.png b/report/images/Stationarity2.png new file mode 100644 index 000000000..a270d0164 Binary files /dev/null and b/report/images/Stationarity2.png differ diff --git a/report/images/WeatherVForecast.png b/report/images/WeatherVForecast.png new file mode 100644 index 000000000..ff8b98208 Binary files /dev/null and b/report/images/WeatherVForecast.png differ diff --git a/report/images/WeathervDemand.png b/report/images/WeathervDemand.png new file mode 100644 index 000000000..e41796233 Binary files /dev/null and b/report/images/WeathervDemand.png differ diff --git a/report/images/WeekDemand.png b/report/images/WeekDemand.png new file mode 100644 index 000000000..05a7c225e Binary files /dev/null and b/report/images/WeekDemand.png differ diff --git a/report/images/demandHour.png b/report/images/demandHour.png new file mode 100644 index 000000000..e77986cfc Binary files /dev/null and b/report/images/demandHour.png differ diff --git a/report/images/demandMonth.png b/report/images/demandMonth.png new file mode 100644 index 000000000..73ab97bc7 Binary files /dev/null and b/report/images/demandMonth.png differ diff --git a/report/images/demandvtime.png b/report/images/demandvtime.png new file mode 100644 index 000000000..1735e9eb5 Binary files /dev/null and b/report/images/demandvtime.png differ diff --git a/report/images/forecastErrorTemp.png b/report/images/forecastErrorTemp.png new file mode 100644 index 000000000..6d3514c01 Binary files /dev/null and b/report/images/forecastErrorTemp.png differ diff --git a/report/images/forecastdemscatter.png b/report/images/forecastdemscatter.png new file mode 100644 index 000000000..96310a2c7 Binary files /dev/null and b/report/images/forecastdemscatter.png differ diff --git a/report/images/shap.jpg b/report/images/shap.jpg new file mode 100644 index 000000000..8d826fec8 Binary files /dev/null and b/report/images/shap.jpg differ diff --git a/report/images/tempvsdemand.png b/report/images/tempvsdemand.png new file mode 100644 index 000000000..dc55c0019 Binary files /dev/null and b/report/images/tempvsdemand.png differ diff --git a/report/images/xgboostprediction.jpg b/report/images/xgboostprediction.jpg new file mode 100644 index 000000000..559e7c75d Binary files /dev/null and b/report/images/xgboostprediction.jpg differ diff --git a/report/images/xgboosttuning.jpg b/report/images/xgboosttuning.jpg new file mode 100644 index 000000000..d9d35cd93 Binary files /dev/null and b/report/images/xgboosttuning.jpg differ diff --git a/report/references.bib b/report/references.bib new file mode 100644 index 000000000..bfc21e20e --- /dev/null +++ b/report/references.bib @@ -0,0 +1,444 @@ +@article{Koukaras2024, +AUTHOR = {Koukaras, Paraskevas and Mustapha, Akeem and Mystakidis, Aristeidis and Tjortjis, Christos}, +TITLE = {Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models}, +JOURNAL = {Energies}, +VOLUME = {17}, +YEAR = {2024}, +NUMBER = {6}, +ARTICLE-NUMBER = {1450}, +URL = {https://www.mdpi.com/1996-1073/17/6/1450}, +ISSN = {1996-1073}, +DOI = {10.3390/en17061450} +} + +@article{Suganthi2012, +title = {Energy models for demand forecasting—A review}, +journal = {Renewable and Sustainable Energy Reviews}, +volume = {16}, +number = {2}, +pages = {1223-1240}, +year = {2012}, +issn = {1364-0321}, +doi = {https://doi.org/10.1016/j.rser.2011.08.014}, +url = {https://www.sciencedirect.com/science/article/pii/S1364032111004242}, +author = {L. Suganthi and Anand A. Samuel}, +keywords = {Energy models, Forecasting model, Energy demand management, Econometric models, Demand side management} +} + +@article{Ghalehkhondabi2017, +author={Ghalehkhondabi, Iman +and Ardjmand, Ehsan +and Weckman, Gary R. +and Young, William A.}, +title={An overview of energy demand forecasting methods published in 2005--2015}, +journal={Energy Systems}, +year={2017}, +month={May}, +day={01}, +volume={8}, +number={2}, +pages={411-447}, +issn={1868-3975}, +doi={10.1007/s12667-016-0203-y}, +url={https://doi.org/10.1007/s12667-016-0203-y} +} + +@article{Rakpho2021, +title = {The forecasting power of economic policy uncertainty for energy demand and supply}, +journal = {Energy Reports}, +volume = {7}, +pages = {338-343}, +year = {2021}, +note = {2021 6th International Conference on Advances on Clean Energy Research}, +issn = {2352-4847}, +doi = {https://doi.org/10.1016/j.egyr.2021.06.059}, +url = {https://www.sciencedirect.com/science/article/pii/S2352484721004248}, +author = {Pichayakone Rakpho and Woraphon Yamaka}, +keywords = {Economic Policy Uncertainty (EPU), Demand energy, Supply energy, BVAR models} +} + +@article{Pinheiro2023, +title = {Short-term electricity load forecasting—A systematic approach from system level to secondary substations}, +journal = {Applied Energy}, +volume = {332}, +pages = {120493}, +year = {2023}, +issn = {0306-2619}, +doi = {https://doi.org/10.1016/j.apenergy.2022.120493}, +url = {https://www.sciencedirect.com/science/article/pii/S0306261922017500}, +author = {Marco G. Pinheiro and Sara C. Madeira and Alexandre P. Francisco}, +keywords = {Load forecasting, System level, Secondary substations, Ensemble model, Generalized additive models, Interpretable models} +} + +@article{Ahmad2018, +title = {Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment}, +journal = {Energy}, +volume = {160}, +pages = {1008-1020}, +year = {2018}, +issn = {0360-5442}, +doi = {https://doi.org/10.1016/j.energy.2018.07.084}, +url = {https://www.sciencedirect.com/science/article/pii/S0360544218313811}, +author = {Tanveer Ahmad and Huanxin Chen}, +keywords = {ANN-NAEMI, MLRM, AdaBoost, Energy prediction, Machine learning models} +} + +@article{Klyuev2022, +AUTHOR = {Klyuev, Roman V. and Morgoev, Irbek D. and Morgoeva, Angelika D. and Gavrina, Oksana A. and Martyushev, Nikita V. and Efremenkov, Egor A. and Mengxu, Qi}, +TITLE = {Methods of Forecasting Electric Energy Consumption: A Literature Review}, +JOURNAL = {Energies}, +VOLUME = {15}, +YEAR = {2022}, +NUMBER = {23}, +ARTICLE-NUMBER = {8919}, +URL = {https://www.mdpi.com/1996-1073/15/23/8919}, +ISSN = {1996-1073}, +DOI = {10.3390/en15238919} +} + +@misc{AEMO2022, +author = {Australian Energy Market Operator}, +year = {2022}, +title = {Forecasting Approach – Electricity Demand Forecasting Methodology}, +url = {https://aemo.com.au/-/media/files/electricity/nem/planning_and_forecasting/nem_esoo/2022/forecasting-approach-electricity-demand-forecasting-methodology.pdf}, +note = {Accessed 16 March 2025} +} + +@article{Deng2022, +title = {Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model}, +journal = {Energy Reports}, +volume = {8}, +pages = {8661-8674}, +year = {2022}, +issn = {2352-4847}, +doi = {https://doi.org/10.1016/j.egyr.2022.06.072}, +url = {https://www.sciencedirect.com/science/article/pii/S2352484722012124}, +author = {Xuzhi Deng and Aoshuang Ye and Jiashi Zhong and Dong Xu and Wangwang Yang and Zhaofang Song and Zitong Zhang and Jing Guo and Tao Wang and Yifan Tian and Hongguang Pan and Zhijing Zhang and Hui Wang and Chen Wu and Jiajia Shao and Xiaoyi Chen}, +keywords = {Ensemble learning, Extreme weather identification, Mutual information, Short-term load forecasting, XGBoost} +} + +@article{Divina2019, +AUTHOR = {Divina, Federico and García Torres, Miguel and Goméz Vela, Francisco A. and Vázquez Noguera, José Luis}, +TITLE = {A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings}, +JOURNAL = {Energies}, +VOLUME = {12}, +YEAR = {2019}, +NUMBER = {10}, +ARTICLE-NUMBER = {1934}, +URL = {https://www.mdpi.com/1996-1073/12/10/1934}, +ISSN = {1996-1073}, +DOI = {10.3390/en12101934} +} + +@article{Mystakidis2024, +AUTHOR = {Mystakidis, Aristeidis and Koukaras, Paraskevas and Tsalikidis, Nikolaos and Ioannidis, Dimosthenis and Tjortjis, Christos}, +TITLE = {Energy Forecasting: A Comprehensive Review of Techniques and Technologies}, +JOURNAL = {Energies}, +VOLUME = {17}, +YEAR = {2024}, +NUMBER = {7}, +ARTICLE-NUMBER = {1662}, +URL = {https://www.mdpi.com/1996-1073/17/7/1662}, +ISSN = {1996-1073}, +DOI = {10.3390/en17071662} +} + +@article{Liu2021, +title = {Temperature change and electricity consumption of the group living: A case study of college students}, +journal = {Science of The Total Environment}, +volume = {781}, +pages = {146574}, +year = {2021}, +issn = {0048-9697}, +doi = {https://doi.org/10.1016/j.scitotenv.2021.146574}, +url = {https://www.sciencedirect.com/science/article/pii/S0048969721016429}, +author = {Xiao-Qiao Liu and Chen Zhang and Yi Zhou and Hua Liao}, +keywords = {Extreme temperature, Electricity consumption, Climate change, Dwelling condition} +} + +@article { Anel2021, + author = "Juan A. Añel and Celia Pérez-Souto and Susana Bayo-Besteiro and Luis Prieto-Godino and Hannah Bloomfield and Alberto Troccoli and Laura de and la Torre", + title = "Extreme Weather Events and the Energy Sector in 2021", + journal = "Weather, Climate, and Society", + year = "2024", + publisher = "American Meteorological Society", + address = "Boston MA, USA", + volume = "16", + number = "3", + doi = "10.1175/WCAS-D-23-0115.1", + pages= "353 - 368", + url = "https://journals.ametsoc.org/view/journals/wcas/16/3/WCAS-D-23-0115.1.xml" +} + +@article{Maia-Silva2020, +author={Maia-Silva, Debora +and Kumar, Rohini +and Nateghi, Roshanak}, +title={The critical role of humidity in modeling summer electricity demand across the United States}, +journal={Nature Communications}, +year={2020}, +month={Apr}, +day={03}, +volume={11}, +number={1}, +pages={1686}, +issn={2041-1723}, +doi={10.1038/s41467-020-15393-8}, +url={https://doi.org/10.1038/s41467-020-15393-8} +} + + +@article{Singh2018, +AUTHOR = {Singh, Shailendra and Yassine, Abdulsalam}, +TITLE = {Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting}, +JOURNAL = {Energies}, +VOLUME = {11}, +YEAR = {2018}, +NUMBER = {2}, +ARTICLE-NUMBER = {452}, +URL = {https://www.mdpi.com/1996-1073/11/2/452}, +ISSN = {1996-1073}, +DOI = {10.3390/en11020452} +} + +@article{Boroojeni2017, +title = {A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon}, +journal = {Electric Power Systems Research}, +volume = {142}, +pages = {58-73}, +year = {2017}, +issn = {0378-7796}, +doi = {https://doi.org/10.1016/j.epsr.2016.08.031}, +url = {https://www.sciencedirect.com/science/article/pii/S0378779616303352}, +author = {Kianoosh G. Boroojeni and M. Hadi Amini and Shahab Bahrami and S.S. Iyengar and Arif I. Sarwat and Orkun Karabasoglu}, +keywords = {Autoregressive model, Moving-average model, Time-series forecasting, Electric power demand forecast, Akaike information criterion, Bayesian information criterion} +} + +@ARTICLE{Papalexopoulos1990, +author={Papalexopoulos, A.D. and Hesterberg, T.C.}, +journal={IEEE Transactions on Power Systems}, +title={A regression-based approach to short-term system load forecasting}, +year={1990}, +volume={5}, +number={4}, +pages={1535-1547}, +keywords={Load forecasting,Predictive models,Economic forecasting,Power system control,Power systems,Load flow,Power system security,Load modeling,Parameter estimation,Power system modeling}, +doi={10.1109/59.99410} +} + +@article{Ertugrul2021, +author={Ertu{\u{g}}rul, {\"O}mer Faruk +and Tekin, Hazret +and Tekin, Ramazan}, +title={A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid}, +journal={Electrical Engineering}, +year={2021}, +month={Feb}, +day={01}, +volume={103}, +number={1}, +pages={717-728}, +issn={1432-0487}, +doi={10.1007/s00202-020-01114-3}, +url={https://doi.org/10.1007/s00202-020-01114-3} +} + +@article{Tarmanini2023, +title = {Short term load forecasting based on ARIMA and ANN approaches}, +journal = {Energy Reports}, +volume = {9}, +pages = {550-557}, +year = {2023}, +note = {2022 The 3rd International Conference on Power, Energy and Electrical Engineering}, +issn = {2352-4847}, +doi = {https://doi.org/10.1016/j.egyr.2023.01.060}, +url = {https://www.sciencedirect.com/science/article/pii/S2352484723000653}, +author = {Chafak Tarmanini and Nur Sarma and Cenk Gezegin and Okan Ozgonenel}, +keywords = {Artificial Neural Network (ANN), Auto Regressive Integrated Moving Average (ARIMA), Smart grid, Short time load forecasting (STLF), Storage device} +} + +@article{Ediger2007, +title = {ARIMA forecasting of primary energy demand by fuel in Turkey}, +journal = {Energy Policy}, +volume = {35}, +number = {3}, +pages = {1701-1708}, +year = {2007}, +issn = {0301-4215}, +doi = {https://doi.org/10.1016/j.enpol.2006.05.009}, +url = {https://www.sciencedirect.com/science/article/pii/S0301421506002291}, +author = {Volkan Ş. Ediger and Sertaç Akar}, +keywords = {Primary energy demand, ARIMA forecasting, Turkey} +} + +@article{Ardakani2014, +title = {Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types}, +journal = {Energy}, +volume = {65}, +pages = {452-461}, +year = {2014}, +issn = {0360-5442}, +doi = {https://doi.org/10.1016/j.energy.2013.12.031}, +url = {https://www.sciencedirect.com/science/article/pii/S0360544213010888}, +author = {F.J. Ardakani and M.M. Ardehali}, +keywords = {Historical data type, Electrical energy consumption, Long-term forecasting} +} + + +@article{Kopyt2024, +AUTHOR = {Kopyt, Marcin and Piotrowski, Paweł and Baczyński, Dariusz}, +TITLE = {Short-Term Energy Generation Forecasts at a Wind Farm—A Multi-Variant Comparison of the Effectiveness and Performance of Various Gradient-Boosted Decision Tree Models}, +JOURNAL = {Energies}, +VOLUME = {17}, +YEAR = {2024}, +NUMBER = {23}, +ARTICLE-NUMBER = {6194}, +URL = {https://www.mdpi.com/1996-1073/17/23/6194}, +ISSN = {1996-1073}, +DOI = {10.3390/en17236194} +} + +@article{Wang2018, +AUTHOR = {Wang, Jidong and Li, Peng and Ran, Ran and Che, Yanbo and Zhou, Yue}, +TITLE = {A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree}, +JOURNAL = {Applied Sciences}, +VOLUME = {8}, +YEAR = {2018}, +NUMBER = {5}, +ARTICLE-NUMBER = {689}, +URL = {https://www.mdpi.com/2076-3417/8/5/689}, +ISSN = {2076-3417}, +DOI = {10.3390/app8050689} +} + +@article{Manno2022, +AUTHOR = {Manno, Andrea and Martelli, Emanuele and Amaldi, Edoardo}, +TITLE = {A Shallow Neural Network Approach for the Short-Term Forecast of Hourly Energy Consumption}, +JOURNAL = {Energies}, +VOLUME = {15}, +YEAR = {2022}, +NUMBER = {3}, +ARTICLE-NUMBER = {958}, +URL = {https://www.mdpi.com/1996-1073/15/3/958}, +ISSN = {1996-1073}, +DOI = {10.3390/en15030958} +} + +@Article{Kuo2018, +AUTHOR = {Kuo, Ping-Huan and Huang, Chiou-Jye}, +TITLE = {A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting}, +JOURNAL = {Energies}, +VOLUME = {11}, +YEAR = {2018}, +NUMBER = {1}, +ARTICLE-NUMBER = {213}, +URL = {https://www.mdpi.com/1996-1073/11/1/213}, +ISSN = {1996-1073}, +DOI = {10.3390/en11010213} +} + +@article{Ahmad2014, +title = {A review on applications of ANN and SVM for building electrical energy consumption forecasting}, +journal = {Renewable and Sustainable Energy Reviews}, +volume = {33}, +pages = {102-109}, +year = {2014}, +issn = {1364-0321}, +doi = {https://doi.org/10.1016/j.rser.2014.01.069}, +url = {https://www.sciencedirect.com/science/article/pii/S1364032114000914}, +author = {A.S. Ahmad and M.Y. Hassan and M.P. Abdullah and H.A. Rahman and F. Hussin and H. Abdullah and R. Saidur}, +keywords = {Forecasting, Building energy consumption, Artificial Neural Networks, GMDH, LSSVM} +} + +@Article{Ahmad2020, +AUTHOR = {Ahmad, Waqas and Ayub, Nasir and Ali, Tariq and Irfan, Muhammad and Awais, Muhammad and Shiraz, Muhammad and Glowacz, Adam}, +TITLE = {Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine}, +JOURNAL = {Energies}, +VOLUME = {13}, +YEAR = {2020}, +NUMBER = {11}, +ARTICLE-NUMBER = {2907}, +URL = {https://www.mdpi.com/1996-1073/13/11/2907}, +ISSN = {1996-1073}, +DOI = {10.3390/en13112907} +} + +@InProceedings{Abbasi2019, +author={Abbasi, Raza Abid +and Javaid, Nadeem +and Ghuman, Muhammad Nauman Javid +and Khan, Zahoor Ali +and Ur Rehman, Shujat +and Amanullah}, +editor={Barolli, Leonard +and Takizawa, Makoto +and Xhafa, Fatos +and Enokido, Tomoya}, +title={Short Term Load Forecasting Using XGBoost}, +booktitle={Web, Artificial Intelligence and Network Applications}, +year={2019}, +publisher={Springer International Publishing}, +address={Cham}, +pages={1120--1131}, +doi={ https://doi.org/10.1007/978-3-030-15035-8_108} +} + +@Article{Savic2014, +author={Savi{\'{c}}, Stevan +and Selakov, Aleksandar +and Milo{\v{s}}evi{\'{c}}, Dragan}, +title={Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas}, +journal={Natural Hazards}, +year={2014}, +month={Sep}, +day={01}, +volume={73}, +number={2}, +pages={373-387}, +issn={1573-0840}, +doi={10.1007/s11069-014-1074-y}, +url={https://doi.org/10.1007/s11069-014-1074-y} +} + +@InProceedings{Andronikos2023, +author={Andronikos, Achilleas +and Tzelepi, Maria +and Tefas, Anastasios}, +editor={Iliadis, Lazaros +and Maglogiannis, Ilias +and Alonso, Serafin +and Jayne, Chrisina +and Pimenidis, Elias}, +title={Residual Error Learning for Electricity Demand Forecasting}, +booktitle={Engineering Applications of Neural Networks}, +year={2023}, +publisher={Springer Nature Switzerland}, +address={Cham}, +pages={391--402}, +isbn={978-3-031-34204-2} +} + +@article{Amara2019, +title = {A residual load modeling approach for household short-term load forecasting application}, +journal = {Energy and Buildings}, +volume = {187}, +pages = {132-143}, +year = {2019}, +issn = {0378-7788}, +doi = {https://doi.org/10.1016/j.enbuild.2019.01.009}, +url = {https://www.sciencedirect.com/science/article/pii/S0378778818309228}, +author = {Fatima Amara and Kodjo Agbossou and Yves Dubé and Sousso Kelouwani and Alben Cardenas and Sayed Saeed Hosseini}, +keywords = {Electric load forecasting, Residual demand modeling, Daily electricity usage, Kernel density, Occupant behavior modeling} +} + +@article{Zhang2022, +title = {Extreme temperatures and residential electricity consumption: Evidence from Chinese households}, +journal = {Energy Economics}, +volume = {107}, +pages = {105890}, +year = {2022}, +issn = {0140-9883}, +doi = {https://doi.org/10.1016/j.eneco.2022.105890}, +url = {https://www.sciencedirect.com/science/article/pii/S014098832200072X}, +author = {Shaohui Zhang and Qinxin Guo and Russell Smyth and Yao Yao} +} \ No newline at end of file diff --git a/report/template.tex b/report/template.tex new file mode 100644 index 000000000..ca01e72d7 --- /dev/null +++ b/report/template.tex @@ -0,0 +1,77 @@ +\documentclass[mstat,12pt]{unswthesis} + +$if(highlighting-macros)$ +$highlighting-macros$ +$endif$ + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% OK...Now we get to some actual input. The first part sets up +% the title etc that will appear on the front page +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\title{$title$} + +\authornameonly{$for(author)$$author$ $endfor$} + +\author{\Authornameonly} + +\copyrightfalse +\figurespagefalse +\tablespagefalse + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% And now the document begins +% The \beforepreface and \afterpreface commands puts the +% contents page etc in +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +\input{header.tex} + + +\begin{document} + +\beforepreface + +\prefacesection{Abstract} + +$Abstract$ + +\afterpreface + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% Now we can start on the first chapter +% Within chapters we have sections, subsections and so forth +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%\afterpage{\blankpage} + + +$body$ + + + + + + + +\end{document} + + diff --git a/report/university-of-south-wales-harvard.csl b/report/university-of-south-wales-harvard.csl new file mode 100644 index 000000000..dcd0fe2e1 --- /dev/null +++ b/report/university-of-south-wales-harvard.csl @@ -0,0 +1,262 @@ + + diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/demandvtemp-11.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/demandvtemp-11.pdf new file mode 100644 index 000000000..2552a3086 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/demandvtemp-11.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/errorvtemp-1.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/errorvtemp-1.pdf new file mode 100644 index 000000000..c32c8468b Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/errorvtemp-1.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/errorvtemp-15.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/errorvtemp-15.pdf new file mode 100644 index 000000000..0784572c4 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/errorvtemp-15.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/extremetemp-3.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/extremetemp-3.pdf new file mode 100644 index 000000000..0fba82b86 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/extremetemp-3.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/forecastvtemp-1.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/forecastvtemp-1.pdf new file mode 100644 index 000000000..d7c9155c8 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/forecastvtemp-1.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/forecastvtemp-13.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/forecastvtemp-13.pdf new file mode 100644 index 000000000..abf2f6dd3 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/forecastvtemp-13.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/monthdemand-3.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/monthdemand-3.pdf new file mode 100644 index 000000000..e238aa83e Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/monthdemand-3.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/monthdemand-7.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/monthdemand-7.pdf new file mode 100644 index 000000000..b2e422e63 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/monthdemand-7.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/relerrorvtemp-17.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/relerrorvtemp-17.pdf new file mode 100644 index 000000000..c098a080b Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/relerrorvtemp-17.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/relerrorvtemp-3.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/relerrorvtemp-3.pdf new file mode 100644 index 000000000..9233c05d3 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/relerrorvtemp-3.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/tempvstime-1.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/tempvstime-1.pdf new file mode 100644 index 000000000..8804c5923 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/tempvstime-1.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/weekdemand-5.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/weekdemand-5.pdf new file mode 100644 index 000000000..aa956c0da Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/weekdemand-5.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/weekdemand-9.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/weekdemand-9.pdf new file mode 100644 index 000000000..e918c1d4a Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/weekdemand-9.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/yeardemand-1.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/yeardemand-1.pdf new file mode 100644 index 000000000..4d54bbb2f Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/yeardemand-1.pdf differ diff --git a/report/unsw-ZZSC9020-report-template_files/figure-latex/yeardemand-5.pdf b/report/unsw-ZZSC9020-report-template_files/figure-latex/yeardemand-5.pdf new file mode 100644 index 000000000..5546e7ab1 Binary files /dev/null and b/report/unsw-ZZSC9020-report-template_files/figure-latex/yeardemand-5.pdf differ diff --git a/report/unsw-logo.png b/report/unsw-logo.png new file mode 100644 index 000000000..83535a3e0 Binary files /dev/null and b/report/unsw-logo.png differ diff --git a/report/unswthesis.cls b/report/unswthesis.cls new file mode 100644 index 000000000..79c3339c6 --- /dev/null +++ b/report/unswthesis.cls @@ -0,0 +1,835 @@ +%----------------------------------------------------------------------- +% UNSWThesis.cls : A thesis style of UNSW modified from ADFAThesis.cls +%----------------------------------------------------------------------- +\NeedsTeXFormat{LaTeX2e}[1995/12/01] +\ProvidesClass{unswthesis}[1999/11/10 \space v2.42 \space% + UNSW thesis class] + +%----------------------------------------------------------------------- +% 12-Apr-89 New flag for draft printing +% At this stage all I do is reset the page style +%----------------------------------------------------------------------- +\newif\ifATdr@ft % Enable various commands during initial write-up +\ATdr@ftfalse % Not draft, by default, must be set in BASE file +\newif\ifATh@nours % Honours report not PhD or Masters? +\ATh@noursfalse % Not Honours by default, must be set in BASE file +\newif\ifATugpr@ject % Third Year Project report, not PhD or Masters? +\ATugpr@jectfalse % Not Third Year Project by default, must be set in BASE file +\newif\ifATmbi@o % Master of Biostatistics report, not PhD or Masters? +\ATmbi@ofalse % Not Master of Biostatistics by default, must be set in BASE file +\newif\ifATmf@in % Master of Financial Mathematics report, not PhD or Masters? +\ATmf@infalse % Not Master of Fin. Maths. by default, must be set in BASE file +\newif\ifATmm@th % Master of Mathematics report, not PhD or Masters? +\ATmm@thfalse % Not Master of Mathematics by default, must be set in BASE file +\newif\ifATmst@t % Master of Statistics report, not PhD or Masters? +\ATmst@tfalse % Not Master of Statistics by default, must be set in BASE file + +\newcommand{\ptsize}{} +\DeclareOption{draft} + {\ATdr@fttrue + \PassOptionsToClass{draft}{report}} +\DeclareOption{honours} + {\ATh@nourstrue} +\DeclareOption{ugproject} + {\ATugpr@jecttrue} +\DeclareOption{mbio} + {\ATmbi@otrue} +\DeclareOption{mfin} + {\ATmf@intrue} +\DeclareOption{mmath} + {\ATmm@thtrue} +\DeclareOption{mstat} + {\ATmst@ttrue} +% Make 12 point text the default +\DeclareOption{10pt}{\renewcommand{\ptsize}{10pt}} +\DeclareOption{11pt}{\renewcommand{\ptsize}{11pt}} +\DeclareOption{12pt}{\renewcommand{\ptsize}{12pt}} +\DeclareOption*{\PassOptionsToClass{\CurrentOption}{report}} +\ExecuteOptions{12pt} +\ProcessOptions\relax +\LoadClass[a4paper,\ptsize]{report} + +%----------------------------------------------------------------------- +% The size of the paper shall approximate A4 (297mm x 210 mm). +% The margins on each sheet shall be not less than 40mm on the left +% hand side, 20mm on the right hand side, 30mm at the top and 20mm at +% the bottom. +% TeX has default margins of 1 inch (25.4mm) at the top and left. +% +% Use the code from size12.clo to set \textheight to an integer +% multiple of \baselineskips. User \raggedbottom and add one +% \baselineskip to \topskip to allow pagelength to vary. +%----------------------------------------------------------------------- + +\usepackage[a4paper, + bindingoffset=0in, + left=1in, + right=1in, + top=0.9in, + bottom=0.9in, + footskip=.25in]{geometry} + + + +%----------------------------------------------------------------------- +% Set spacing for space and a half, using values from setspace.sty. +% Use the new \linespread command rather than +% \renewcommand{\baselinestretch}{1.25} etc. +%----------------------------------------------------------------------- +\ifcase \@ptsize \relax % 10pt + \linespread{1.25}% +\or % 11pt + \linespread{1.213}% +\or % 12pt + \linespread{1.6}% +\fi + +%----------------------------------------------------------------------- +% Next two sections taken from setspace. +%----------------------------------------------------------------------- +\newcommand{\displayskipstretch}{\baselinestretch} +\newcommand{\setdisplayskipstretch}[1]{\renewcommand{\displayskipstretch}{#1}} + +% +% Fix up spacing before and after displayed math +% (arraystretch seems to do a fine job for inside LaTeX displayed math, +% since array and eqnarray seem to be affected as expected). +% Changing \baselinestretch and doing a font change also works if done here, +% but then you have to change @setsize to remove the call to @nomath) +% +\everydisplay\expandafter{% + \the\everydisplay + \abovedisplayskip \displayskipstretch\abovedisplayskip + \belowdisplayskip \displayskipstretch\belowdisplayskip + \abovedisplayshortskip \displayskipstretch\abovedisplayshortskip + \belowdisplayshortskip \displayskipstretch\belowdisplayshortskip +} +%----------------------------------------------------------------------- +% Following changed by Stephen Harker, October 1993 to: +% (i) Make Chapter title centred, and modify size to \Large not +% \Huge, use small caps for `chapter' and rules above and +% below. Rule thickness defined by new length \chaprule. +% To change this use \setlength. +% (ii) Make corresponding reductions to size of section, +% subsection and subsubsection headers. +% (iii) Rename Bibliography section to References. +%----------------------------------------------------------------------- +\newlength{\chaprule} % Forced to be less than 6 points below! +\newlength{\ATchapskip} +\setlength{\chaprule}{0.4\p@} +\setlength{\ATchapskip}{10\p@} +\advance \ATchapskip by -1\chaprule +\renewcommand{\@makechapterhead}[1]{% + \ifdim\chaprule>6\p@ \setlength{\chaprule}{6\p@}\fi + \vspace*{\ATchapskip}% + \noindent\rule{\textwidth}{\chaprule}\par% + \vskip 10\p@ + {\parindent \z@ \centering \normalfont + \ifnum \c@secnumdepth >\m@ne + {\Large\scshape \@chapapp\space \thechapter} + \par\nobreak + \vskip 8\p@ + \fi + \interlinepenalty\@M + \Large #1\par\nobreak + \vskip 10\p@ + \noindent\rule{\textwidth}{\chaprule}\par% + \vskip\ATchapskip + }} +\renewcommand{\@makeschapterhead}[1]{% + \ifdim\chaprule>6\p@ \setlength{\chaprule}{6\p@}\fi + \vspace*{\ATchapskip}% + \noindent\rule{\textwidth}{\chaprule}\par% + \vskip 10\p@ + {\parindent \z@ \centering + \normalfont + \interlinepenalty\@M + \Large #1\par\nobreak + \vskip 10\p@ + \noindent\rule{\textwidth}{\chaprule}\par% + \vskip\ATchapskip + }} + +\renewcommand{\thesection}{\thechapter.\arabic{section}} +\renewcommand{\thesubsection}{\thesection.\arabic{subsection}} + +\renewcommand{\section}{\@startsection{section}{1}{\z@}% + {-1.5ex \@plus-1ex \@minus -.2ex}{0.8ex \@plus.2ex}% + {\normalfont\large\raggedright}} +\renewcommand{\subsection}{\@startsection{subsection}{2}{\z@}% + {-1.2ex \@plus -.5ex \@minus-.2ex}{0.5ex \@plus.1ex}% + {\normalfont\normalsize\itshape\raggedright}} +\renewcommand{\subsubsection}{\@startsection{subsubsection}{3}{\z@}% + {-1.0ex\@plus -.5ex \@minus -.2ex}{0.3ex \@plus .1ex}% + {\normalfont\normalsize\itshape\raggedright}} +\renewcommand{\paragraph}{\@startsection{paragraph}{4}{\z@}% + {1.0ex \@plus.5ex \@minus.2ex}{-1em}% + {\normalfont\normalsize\itshape\raggedright}} +\renewcommand{\subparagraph}{\@startsection{subparagraph}{5}{\parindent}% + {1.0ex \@plus.5ex \@minus .2ex}{-1em}% + {\normalfont\normalsize\itshape\raggedright}} + +\renewcommand{\bibname}{References} + +%----------------------------------------------------------------------- +% Taken from sober.sty, Nico Poppelier and rapport1.cls (NTG classes). +% Makes list (enumerate and itemize) more reasonable in vertical space, +% by adjusting the spacing between items. +% Unfortunately in the size*.clo files \small etc also redefine these +% values. We could redefine \small etc, but they are size dependent! +% Leave alone, since \small is not usually used as an environment, at +% least not for large sections of a document. +%----------------------------------------------------------------------- +\def\@listi{\leftmargin\leftmargini + \labelsep .5em% + \labelwidth\leftmargini + \advance\labelwidth-\labelsep + \parsep \z@ + \topsep 0.4ex \@plus\p@ + \itemsep 0\p@ \@plus1\p@} +\let\@listI\@listi +\@listi +\def\@listii{\leftmargin\leftmarginii + \labelsep .5em% + \labelwidth\leftmarginii + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus\p@ + \parsep \z@ \@plus\p@ + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelsep .5em% + \labelwidth\leftmarginiii + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus\p@ + \parsep \z@ + \partopsep \z@ \@plus\p@ + \itemsep \topsep} +\def\@listiv{\leftmargin\leftmarginiv + \labelsep .5em% + \labelwidth\leftmarginiv + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus\p@ + \parsep \z@ + \partopsep \z@ \@plus\p@ + \itemsep \topsep} +\def\@listv{\leftmargin\leftmarginv + \labelsep .5em% + \labelwidth\leftmarginv + \advance\labelwidth-\labelsep% + \topsep 0\p@ \@plus\p@ + \parsep \z@ + \itemsep \z@ \@plus\p@} +\def\@listvi{\leftmargin\leftmarginvi + \labelsep .5em + \labelwidth\leftmarginvi + \advance\labelwidth{-\labelsep}% + \topsep 0\p@ \@plus\p@ + \parsep \z@ + \itemsep \z@ \@plus\p@} + +%----------------------------------------------------------------------- +% Re-define \cleardoublepage as recommended by Piet van Oostrum in the +% documentation for fancyhdr.sty page 15. This is to avoid blank pages +% having headers or footers. +%----------------------------------------------------------------------- +\renewcommand{\cleardoublepage}{\clearpage\if@twoside \ifodd\c@page\else + \thispagestyle{empty} + \hbox{}\newpage\if@twocolumn\hbox{}\newpage\fi\fi\fi} +\providecommand{\tightlist}{% + \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} +%----------------------------------------------------------------------- +% Reduce widow/orphan problems, mainly from a posting from Donald +% Arsenau on comp.text.tex, 24 Sep 1995. +% Updated to follow comments from Michael Downes on comp.text.tex, +% 31 Aug 1998. +%----------------------------------------------------------------------- +\doublehyphendemerits=10000 % No consecutive line hyphens. +\brokenpenalty=4991 % Reduce broken words across columns/pages. +\widowpenalty=9999 % Almost no widows at bottom of page. +\clubpenalty=9996 % Almost no orphans at top of page. +\interfootnotelinepenalty=9999 % Almost never break footnotes. +\predisplaypenalty=10000 % Default value +\postdisplaypenalty=1549 % Few breaks between display and widows +\displaywidowpenalty=1602 % At least as high as \postdisplaypenalty +%----------------------------------------------------------------------- +% Change float placement parameters to reduce problems. Based on +% values posted by Donald Arsenau on comp.text.tex at various times. +% See in particular 17th Nov 1997. +%----------------------------------------------------------------------- +\renewcommand{\topfraction}{.85} +\renewcommand{\bottomfraction}{.7} +\renewcommand{\textfraction}{.15} +\renewcommand{\floatpagefraction}{.66} +\renewcommand{\dbltopfraction}{.66} +\renewcommand{\dblfloatpagefraction}{.66} +\setcounter{topnumber}{9} +\setcounter{bottomnumber}{9} +\setcounter{totalnumber}{20} +\setcounter{dbltopnumber}{9} + +%----------------------------------------------------------------------- +% Make tables and figures default to small text and be single spaced, +% and modify caption macro to allow this to take effect in the caption. +% Use this version rather than previous redefinition of \@xfloat, see +% setspace.sty for an improved example of the latter. +% From comp.text.tex, Donald Arsenau 25 July 1996. +% Also reverse \abovecaptionskip and \belowcaptionskip for tables. +%----------------------------------------------------------------------- +\renewenvironment{table} + {\setlength{\abovecaptionskip}{0\p@} + \setlength{\belowcaptionskip}{10\p@} + \linespread{1}\normalfont\small\@float{table}} + {\end@float} +\renewenvironment{table*} + {\setlength{\abovecaptionskip}{0\p@} + \setlength{\belowcaptionskip}{10\p@} + \linespread{1}\normalfont\small\@dblfloat{table}} + {\end@dblfloat} +\renewenvironment{figure} + {\linespread{1}\normalfont\small\@float{figure}} + {\end@float} +\renewenvironment{figure*} + {\linespread{1}\normalfont\small\@dblfloat{figure}} + {\end@dblfloat} +\long\def\@caption#1[#2]#3{\par\addcontentsline{\csname + ext@#1\endcsname}{#1}{\protect\numberline{\csname + the#1\endcsname}{\ignorespaces #2}}\begingroup + \@parboxrestore + \if@minipage + \@setminipage + \fi +%% \normalsize % Remove this so we can get \small captions. + \@makecaption{\csname fnum@#1\endcsname}{\ignorespaces #3}\par + \endgroup} + +% Also Donald Arsenau's modified \@makecaption which fixes problems +% with spacing of captions before tables. Taken from comp.text.tex +% 21 May 1997. Regular version (acts like regular caption, but with +% Donald Arsenau's improvements). + +\def\onecaptflag{268 } +\renewcommand{\@makecaption}[2]{\let\@tempa\relax + \ifdim\prevdepth>-99\p@ \vskip\abovecaptionskip \relax + \else \def\@tempa{\vbox to\topskip{}}\fi + {#1: }\@tempa \vadjust{\penalty \onecaptflag}#2\par + \ifnum\lastpenalty=\onecaptflag + \unpenalty \setbox\@tempboxa\lastbox + \nointerlineskip + \hbox to\hsize{\hskip\parfillskip\unhbox\@tempboxa}% + \fi \vskip\belowcaptionskip} +%----------------------------------------------------------------------- +% Number figures, tables and equations by chapter. Re-define footnotes +% and minipage footnotes to be single spaced. Make new macros needed +% for thesis definitions. +%----------------------------------------------------------------------- + +\renewcommand{\thefigure}{\thechapter.\@arabic\c@figure} +\renewcommand{\thetable}{\thechapter.\@arabic\c@table} +\renewcommand{\theequation}{\thechapter.\@arabic\c@equation} + +% Re-define \@footnotetext and \@mpfootnotetext to use single spacing +% rather than the space-and-a-half that is the default elsewhere. + +\renewcommand{\@footnotetext}[1]{\insert\footins{% + \linespread{1}\normalfont\footnotesize% + \interlinepenalty\interfootnotelinepenalty + \splittopskip\footnotesep + \splitmaxdepth \dp\strutbox \floatingpenalty \@MM + \hsize\columnwidth \@parboxrestore + \protected@edef\@currentlabel{% + \csname p@footnote\endcsname\@thefnmark}% + \color@begingroup + \@makefntext{% + \rule\z@\footnotesep\ignorespaces#1\@finalstrut\strutbox}% + \color@endgroup}} + +\renewcommand{\@mpfootnotetext}[1]{% + \global\setbox\@mpfootins\vbox{% + \unvbox \@mpfootins + \linespread{1}\normalfont\footnotesize% + \hsize\columnwidth + \@parboxrestore + \protected@edef\@currentlabel{% + \csname p@mpfootnote\endcsname\@thefnmark}% + \color@begingroup + \@makefntext{% + \rule\z@\footnotesep\ignorespaces#1\@finalstrut\strutbox}% + \color@endgroup}} + + +%----------------------------------------------------------------------- +% Define thesis related commands. +% Another change is to add \thesistype which can be defined +% as appropriate for Masters or Doctoral thesis (default Doctoral). +%----------------------------------------------------------------------- +\newcommand{\dept}[1]{\gdef\@dept{#1}} +\newcommand{\thesistype}[1]{\gdef\@thesistype{#1}} +\newcommand{\degreetype}[1]{\gdef\@degreetype{#1}} +\newcommand{\principaladviser}[1]{\gdef\@principaladviser{#1}} +\newcommand{\advis@r}{Adviser} +\newcommand{\principaladvisor}[1]{\gdef\@principaladviser{#1}% + \gdef\advis@r{Advisor}} +\newcommand{\firstreader}[1]{\gdef\@firstreader{#1}} +\newcommand{\secondreader}[1]{\gdef\@secondreader{#1}} +\newcommand{\submitdate}[1]{\gdef\@submitdate{#1}} +\newcommand{\copyrightyear}[1]{\gdef\@copyrightyear{#1}} % \author, \title + % in report + +\renewcommand{\@title}{} +\renewcommand{\@author}{} +\newcommand{\@dept}{Mathematics and Statistics} +\newcommand{\@thesistype}{Doctor of Philosophy} +\newcommand{\@degreetype}{Science} +\newcommand{\@principaladviser}{} +\newcommand{\@firstreader}{} +\newcommand{\@secondreader}{} +\newcommand{\@submitdate}{\ifcase\the\month\or + January\or February\or March\or April\or May\or June\or + July\or August\or September\or October\or November\or December\fi + \space \number\the\year} +\ifnum\month=12 + \@tempcnta=\year \advance\@tempcnta by 1 + \edef\@copyrightyear{\number\the\@tempcnta} +\else + \newcommand{\@copyrightyear}{\number\the\year} +\fi + +\newif\ifcopyright +\newif\iffigurespage +\newif\iftablespage +\copyrighttrue +\figurespagetrue +\tablespagetrue +%----------------------------------------------------------------------- +% A new definition, mainly for the DECLARATION. +%----------------------------------------------------------------------- +\newcommand{\authornameonly}[1]{\gdef\Authornameonly{#1}} +%----------------------------------------------------------------------- +% Title page, copyrightpage and declaration page definitions. +% Add re-definition for Honours reports rather than Higher Degree +% theses. +%----------------------------------------------------------------------- +\newcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + \textsc{A thesis submitted for the degree of \\ + \expandafter{\@thesistype}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize By\\ + \@author}\\ + \end{center} + \vfill + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept},\\ + UNSW Sydney.} + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vskip1cm + \newpage} +\ifATh@nours\renewcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize \@author}\\ + \end{center} +\begin{figure} \begin{center} +\includegraphics[width=5cm]{unsw-logo} +\end{center} \end{figure} + \vskip1cm + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept}\\ + UNSW Sydney} \\ + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vfill + \begin{center} + \newpage}\fi +\ifATugpr@ject\renewcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize \@author}\\ + \end{center} +\begin{figure} \begin{center} +\includegraphics[width=5cm]{unsw-logo} +\end{center} \end{figure} + \vskip1cm + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept}\\ + UNSW Sydney} \\ + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vfill + \begin{center} + \small\textsc{Third Year Project Report} + \end{center} +\newpage}\fi +\ifATmbi@o\renewcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize \@author}\\ + \end{center} +\begin{figure} \begin{center} +\includegraphics[width=5cm]{unsw-logo} +\end{center} \end{figure} + \vskip1cm + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept}\\ + UNSW Sydney} \\ + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vfill + \begin{center} + \small\textsc{Submitted in partial fulfilment of the requirements of + the degree of\\Master of Biostatistics} + \end{center} +\newpage}\fi +\ifATmf@in\renewcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize \@author}\\ + \end{center} +\begin{figure} \begin{center} +\includegraphics[width=5cm]{unsw-logo} +\end{center} \end{figure} + \vskip1cm + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept}\\ + UNSW Sydney} \\ + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vfill + \begin{center} + \small\textsc{Submitted in partial fulfilment of the requirements of + the degree of\\Master of Financial Mathematics} + \end{center} +\newpage}\fi +\ifATmm@th\renewcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize \@author}\\ + \end{center} +\begin{figure} \begin{center} +\includegraphics[width=5cm]{unsw-logo} +\end{center} \end{figure} + \vskip1cm + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept}\\ + UNSW Sydney} \\ + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vfill + \begin{center} + \small\textsc{Submitted in partial fulfilment of the requirements of + the degree of\\Master of Mathematics} + \end{center} +\newpage}\fi +\ifATmst@t\renewcommand{\titlep}{% + \pagestyle{empty}% + \null\vskip2.5cm% + \begin{center} + {\rmfamily\Large\uppercase\expandafter{\@title}} + \end{center} + \vfill + \begin{center} + {\rmfamily\normalsize \@author}\\ + \end{center} +\begin{figure} \begin{center} +\includegraphics[width=5cm]{unsw-logo} +\end{center} \end{figure} + \vskip1cm + \begin{center} % Department changed to School July 1995 + {\rmfamily\normalsize School of \expandafter{\@dept}\\ + UNSW Sydney} \\ + \vskip1cm + {\rmfamily\normalsize \@submitdate}\\ + \end{center} + \vfill + \begin{center} + \small\textsc{Submitted in partial fulfilment of the requirements of\\ + the capstone course ZZSC9020} + \end{center} +\newpage}\fi + +\newcommand{\copyrightpage}{% + \null\vfill + \begin{center} + {\Large\copyright\ Copyright \@copyrightyear\\ + by\\ + \@author}\\ + \end{center} + \vfill\newpage} + +%----------------------------------------------------------------------- +% Add definitions for \beforepreface, \prefacesection and \afterpreface +% to allow page numbering and headerstyle to be changed. +%----------------------------------------------------------------------- +\newcommand{\beforepreface}{% + \pagestyle{empty} + \titlep + \if@twoside\cleardoublepage\fi + \pagenumbering{roman} + \ifATdr@ft\pagestyle{draft}\else\pagestyle{plain}\fi + \setcounter{page}\@ne% Reset the page number to 1, i.e. titlepage is page 0 + \ifcopyright\copyrightpage\fi + } + +\newcommand{\prefacesection}[1]{ + \chapter*{#1} + %\addcontentsline{toc}{chapter}{#1} + } + +\newcommand{\afterpreface}{% + \if@twoside + \cleardoublepage + \else\newpage + \fi + \tableofcontents + \if@twoside + \cleardoublepage + \else\newpage + \fi + \iftablespage + {\addvspace{10\p@} + \let\saveaddvspace=\addvspace + \def\addvspace##1{} + \listoftables + \let\addvspace=\saveaddvspace} + \if@twoside + \cleardoublepage + \else\newpage + \fi + \fi + \iffigurespage + {\addvspace{10\p@} + \let\saveaddvspace=\addvspace + \def\addvspace##1{} + \listoffigures + \let\addvspace=\saveaddvspace} + \if@twoside + \cleardoublepage + \else\newpage + \fi + \fi + \pagenumbering{arabic} + \ifATdr@ft\pagestyle{draft}\else\pagestyle{plain}\fi} +%----------------------------------------------------------------------- +% Create a brand new page style to include the date in the page header. +%----------------------------------------------------------------------- +\newcommand{\ps@draft}{%\let\@mkboth\@gobbletwo + \renewcommand{\@oddfoot}{\@empty}% + \renewcommand{\@oddhead}{\rmfamily\slshape\today\hfil\thepage}% + \renewcommand{\@evenhead}{\rmfamily\slshape\thepage\hfil\today}% + \renewcommand{\@evenfoot}{\@oddfoot}} +%----------------------------------------------------------------------- +% Start with pagestyle{plain} in case front matter isn't processed +%----------------------------------------------------------------------- +\pagestyle{plain} + +%*********************************************************************** +% Modify Table of contents entry for chapter to normal font not bold. +% Second use word Chapter/Appendix before number. Use \appendixname +% rather than \@chapapp to set width for this element as it is longer! +%*********************************************************************** +\newlength{\@chapwidth}% +\renewcommand*\l@chapter[2]{% + \ifnum \c@tocdepth >\m@ne + \addpenalty{-\@highpenalty}% + \vskip 1.0em \@plus\p@ + \settowidth{\@chapwidth}{\appendixname}% not \@chapapp + \addtolength{\@chapwidth}{\@pnumwidth} + \setlength\@tempdima{\@chapwidth}% + \begingroup + \parindent \z@ \rightskip \@pnumwidth + \parfillskip -\@pnumwidth + \leavevmode \normalfont + \advance\leftskip\@tempdima + \hskip -\leftskip + #1\nobreak\hfil \nobreak\hb@xt@\@pnumwidth{\hss #2}\par + \penalty\@highpenalty + \endgroup + \fi} +\def\@chapter[#1]#2{% + \ifnum \c@secnumdepth >\m@ne + \refstepcounter{chapter}% + \typeout{\@chapapp\space\thechapter.}% + \addcontentsline{toc}{chapter}% + {\protect\numberline{\expandafter\@chapapp\space\thechapter}#1}% + \else + \addcontentsline{toc}{chapter}{#1}% + \fi + \chaptermark{#1}% + \addtocontents{lof}{\protect\addvspace{10\p@}}% + \addtocontents{lot}{\protect\addvspace{10\p@}}% + \if@twocolumn + \@topnewpage[\@makechapterhead{#2}]% + \else + \@makechapterhead{#2}% + \@afterheading + \fi} + +\endinput +%---------------------------------------------------------------------------- +% Documentation +%---------------------------------------------------------------------------- +\documentclass{article} +\newcommand{\bs}{\char '134 } % A backslash character for \tt font +\title{The \texttt{UNSWthesis} class} +\author{Stephen Harker} +\date{25 May 1997} + +\begin{document} +\maketitle + +Some basic information on +the use of \texttt{UNSWthesis} (a modification of the \texttt{adfathesis} style +which was a modification of the \texttt{SUthesis} style) follows. This class +file can only be used under \LaTeXe. Firstly an example of use: + +\begin{quote} +\small +\begin{verbatim} +\documentclass[a4paper,12pt,openright,twoside]{UNSWthesis} + +% Control which chapters are LaTeX'd in this run with + +\includeonly{chapter1,chapter2,chapter3} + +\title{How to Write Theses\\ + With Two Line Titles} +\authornameonly{John Henry Candidate} +\author{\Authornameonly \\ B.Sc.(Hons)} +\copyrightfalse % No copyright page +\figurespagefalse % No List of Figures +\tablespagefalse % No List of Tables + +\begin{document} +\afterpreface + +\include{chapter1} % Introduction + ... +\include{chapter6} % Conclusions + ... +\appendix +\include{appendix1} % A Long Proof + ... +\clearpage % Needed to get page +\addcontentsline{toc}{chapter}{References} % in TOC correct. +\bibliographystyle{UNSWthesis} +\bibliography{mybib} +\end{document} +\end{verbatim} +\end{quote} + +\textbf{Documentation}: This class modifies the standard report class +to meet the \textsc{UNSW} requirements given in the `\emph{University + College Handbook}'. It sets the margins, interline spacing, the +figure and table numbering style, and disallows page breaks at +hyphens. + +The `\texttt{\bs{}beforepreface}' command creates the title page, a +copyright page (optionally), and the table of contents. Then the user +should put preface section(s), using the command +`\texttt{\bs{}prefacesection\{}\emph{Section Title}\texttt{\}}', this +should include the declaration page. The tables of tables and figures +are then produced by the `\texttt{\bs{}afterpreface}' command, which +also sets things up to start the main body (on arabic page~1). + +The following commands can control what goes in the front matter +material: +\begin{description} +\small +\item{\ttfamily\bs{}title\{\emph{thesis title}\}} Title of the thesis. +\item{\ttfamily\bs{}authornameonly\{\emph{name}\}} The author's name + without degrees earned, needed for the declaration. +\item{\ttfamily\bs{}author\{\emph{name}\}} The author's name with + degrees earned, for the titlepage. +\item{\ttfamily\bs{}dept\{\emph{department}\}} The default value is + School of `\emph{Mathematics}'. +\item{\ttfamily\bs{}thesistype\{\emph{Type of thesis}\}} The default + value is `\emph{Doctor of Philosophy}', for an Honours report this should + be the faculty (e.g.\ `\emph{Science}'). +\item{\ttfamily\bs{}degreetype\{\emph{Faculty for degree}\}} The default + value is `\emph{Science}', used for Honours only. +\item{\ttfamily\bs{}submitdate\{\emph{date}\}} Month and year in which + submitted; date \LaTeX{}'d if omitted. +\item{\ttfamily\bs{}copyrightyear\{\emph{year}\}} Year degree + conferred, or year \LaTeX{}'d if omitted (next year if in December). +\item{\ttfamily\bs{}declaration} Produce the required declaration that + the thesis is all the author's own work. +\item{\ttfamily\bs{}copyrighttrue} Produce or + \texttt{\bs{}copyrightfalse} don't produce a `\emph{copyright}' page + (true by default). +\item{\ttfamily\bs{}figurespagetrue} Produce or + \texttt{\bs{}figurespagefalse} don't produce a `\emph{List of + Figures}' page (true by default). +\item{\ttfamily\bs{}tablespagetrue} Produce or + \texttt{\bs{}tablespagefalse} don't produce a `\emph{List of + Tables}' page (true by default). +\end{description} + +This class uses space and a half interline spacing, except in +footnote, figure and table environments where normal spacing is used. +The command: `\texttt{\bs{}linespread\}\-\{1.655\}}' can be used to +change this (use whatever you want instead of 1.655). For 12 point +Computer Modern fonts 1.241 corresponds to space and a half, and 1.655 +to double spacing. This command should be given in the preamble +(i.e.\ before the `\texttt{\bs{}begin\{document\}}'). + +The example given shows the \textsf{12pt} option being used. This is +required by the 1997 handbook, but may be omitted (at your own risk) +to get smaller print. There are three options which may be declared +in \texttt{\bs{}documentclass[a4paper,12pt]\{UNSWthesis\}}: +\begin{itemize} +\item \texttt{draft} which changes the pagestyle to include the date + on the page header. +\item \textsf{honours} which changes the titlepage to one more + appropriate to an Honours report. +\end{itemize} + +To get the correct page number for the bibliography in the table of +contents you need to put a `\texttt{\bs{}clearpage}' command before +the `\texttt{\bs{}addcontentsline}' command. The thickness of the +rules used for the chapter headings is controlled by +`\texttt{\bs{}chaprule}' and can be set to another value, say 0~pt, by +the command `\texttt{\bs{}setlength\{\bs{}chaprule\}\{0pt\}}' in the +preamble. There is a maximum value of 6~pt for +`\texttt{\bs{}chaprule}'. + +\end{document} diff --git a/src/Actual VS forecast including tempreture.ipynb b/src/Actual VS forecast including tempreture.ipynb new file mode 100644 index 000000000..139be77ec --- /dev/null +++ b/src/Actual VS forecast including tempreture.ipynb @@ -0,0 +1,472 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-03-29T11:39:03.509395400Z", + "start_time": "2025-03-29T11:38:38.347504Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading datasets...\n", + "Loaded forecast data: 10906019 rows\n", + "Loaded demand data: 196513 rows\n", + "Loaded temperature data: 220326 rows\n", + "\n", + "Sample forecast datetime: 2010-01-01 00:00:00\n", + "Sample demand datetime: 1/1/2010 0:00\n", + "Sample temperature DATETIME: 1/1/2010 0:00\n", + "Filtered forecast data for PERIODID 24: 196505 rows\n", + "Forecast datetime format appears to be: ISO\n", + "Demand datetime format appears to be: Australian\n", + "Parsing dates...\n", + "\n", + "After parsing:\n", + "Sample forecast datetime: 2010-01-01 00:00:00\n", + "Sample demand datetime: 2010-01-01 00:00:00\n", + "Sample temperature datetime: 2010-01-01 00:00:00\n", + "Using temperature column: TEMPERATURE\n", + "Merging forecast with demand data...\n", + "Merged forecast and demand: 196505 rows\n", + "Aggregating temperature data by hour...\n", + "Merging with temperature data...\n", + "Final merged dataset: 196505 rows\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12212\\1206430117.py:135: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " df_temperature['hour'] = df_temperature['DATETIME'].dt.floor('H')\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12212\\1206430117.py:142: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " merged_df['hour'] = merged_df['DATETIME'].dt.floor('H')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Overall Forecast Accuracy Metrics for PERIODID 24:\n", + "Mean Absolute Error (MAE): 169.09 MW\n", + "Mean Absolute Percentage Error (MAPE): 2.05%\n", + "Root Mean Square Error (RMSE): 234.87 MW\n", + "R-squared (R²): 0.9673\n", + "\n", + "Analyzing temperature impact on forecast accuracy...\n", + "Rows with temperature data: 196141 out of 196505 total rows\n", + "\n", + "Forecast Accuracy by Temperature Range:\n", + " MAE MAPE Count R-squared\n", + "TEMP_RANGE \n", + "< 0°C 162.318750 2.090544 16 0.960227\n", + "0-5°C 132.277295 1.653575 2710 0.975757\n", + "5-10°C 137.099084 1.678702 19550 0.983940\n", + "10-15°C 160.561842 1.919153 42344 0.977542\n", + "15-20°C 150.157787 1.936071 62603 0.963830\n", + "20-25°C 175.763657 2.162140 51527 0.941278\n", + "25-30°C 261.557903 2.840032 14269 0.930041\n", + "30-35°C 348.152960 3.455496 2568 0.912743\n", + "35-40°C 441.745868 3.869580 484 0.810509\n", + "> 40°C 475.275714 3.866062 70 0.807524\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12212\\1206430117.py:209: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " full_df_with_temp['TEMP_RANGE'] = pd.cut(full_df_with_temp['TEMPERATURE'], bins=temp_bins, labels=temp_labels)\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12212\\1206430117.py:222: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " accuracy_by_temp = full_df_with_temp.groupby('TEMP_RANGE').agg({\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12212\\1206430117.py:229: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12212\\1206430117.py:229: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Creating visualizations...\n", + "\n", + "Analysis complete! Results saved to CSV and visualizations saved as PNG files.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import datetime\n", + "\n", + "# File paths\n", + "forecast_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\forecastdemand_nsw.csv\" #Change path\n", + "demand_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\totaldemand_nsw.csv\"#Change path\n", + "temperature_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\temperature_nsw.csv\" # Change path\n", + "target_periodid = 24\n", + "\n", + "# Load datasets\n", + "\n", + "df_forecast = pd.read_csv(forecast_path)\n", + "df_demand = pd.read_csv(demand_path)\n", + "df_temperature = pd.read_csv(temperature_path)\n", + "\n", + "print(f\"Loaded forecast data: {len(df_forecast)} rows\")\n", + "print(f\"Loaded demand data: {len(df_demand)} rows\")\n", + "print(f\"Loaded temperature data: {len(df_temperature)} rows\")\n", + "\n", + "# Display samples to check datetime formats\n", + "print(\"\\nSample forecast datetime:\", df_forecast['DATETIME'].iloc[0] if len(df_forecast) > 0 else \"No data\")\n", + "print(\"Sample demand datetime:\", df_demand['DATETIME'].iloc[0] if len(df_demand) > 0 else \"No data\")\n", + "temp_dt_col = 'date_time' if 'date_time' in df_temperature.columns else 'DATETIME'\n", + "print(f\"Sample temperature {temp_dt_col}:\", df_temperature[temp_dt_col].iloc[0] if len(df_temperature) > 0 else \"No data\")\n", + "\n", + "# Filter forecast data for PERIODID 24\n", + "df_forecast = df_forecast[df_forecast['PERIODID'] == target_periodid]\n", + "print(f\"Filtered forecast data for PERIODID {target_periodid}: {len(df_forecast)} rows\")\n", + "\n", + "# Check if the forecast and demand datetime are already in ISO format (YYYY-MM-DD)\n", + "forecast_date_format = \"ISO\" if '-' in str(df_forecast['DATETIME'].iloc[0]) else \"Australian\"\n", + "demand_date_format = \"ISO\" if '-' in str(df_demand['DATETIME'].iloc[0]) else \"Australian\"\n", + "print(f\"Forecast datetime format appears to be: {forecast_date_format}\")\n", + "print(f\"Demand datetime format appears to be: {demand_date_format}\")\n", + "\n", + "# Parse dates - with appropriate handling for existing formats\n", + "print(\"Parsing dates...\")\n", + "\n", + "# For forecast data\n", + "if forecast_date_format == \"ISO\":\n", + " # Already in ISO format, just parse\n", + " df_forecast['DATETIME'] = pd.to_datetime(df_forecast['DATETIME'], errors='coerce')\n", + "else:\n", + " # Australian format, convert to ISO\n", + " df_forecast['DATETIME'] = pd.to_datetime(df_forecast['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# For demand data\n", + "if demand_date_format == \"ISO\":\n", + " # Already in ISO format, just parse\n", + " df_demand['DATETIME'] = pd.to_datetime(df_demand['DATETIME'], errors='coerce')\n", + "else:\n", + " # Australian format, convert to ISO\n", + " df_demand['DATETIME'] = pd.to_datetime(df_demand['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# For temperature data\n", + "if 'date_time' in df_temperature.columns:\n", + " # First convert from Australian format to datetime\n", + " df_temperature['date_time'] = pd.to_datetime(df_temperature['date_time'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + " # Then create a DATETIME column in the same format as forecast/demand\n", + " df_temperature['DATETIME'] = df_temperature['date_time']\n", + "else:\n", + " # Directly parse the DATETIME column\n", + " df_temperature['DATETIME'] = pd.to_datetime(df_temperature['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# Drop rows with invalid dates\n", + "df_forecast = df_forecast.dropna(subset=['DATETIME'])\n", + "df_demand = df_demand.dropna(subset=['DATETIME'])\n", + "df_temperature = df_temperature.dropna(subset=['DATETIME'])\n", + "\n", + "# Print sample of parsed dates to verify format consistency\n", + "print(\"\\nAfter parsing:\")\n", + "print(\"Sample forecast datetime:\", df_forecast['DATETIME'].iloc[0] if len(df_forecast) > 0 else \"No data\")\n", + "print(\"Sample demand datetime:\", df_demand['DATETIME'].iloc[0] if len(df_demand) > 0 else \"No data\")\n", + "print(\"Sample temperature datetime:\", df_temperature['DATETIME'].iloc[0] if len(df_temperature) > 0 else \"No data\")\n", + "\n", + "# Identify temperature column\n", + "temp_column = 'temperature' if 'temperature' in df_temperature.columns else 'TEMPERATURE'\n", + "print(f\"Using temperature column: {temp_column}\")\n", + "\n", + "# Merge forecast and demand data\n", + "print(\"Merging forecast with demand data...\")\n", + "merged_df = pd.merge(\n", + " df_forecast,\n", + " df_demand[['DATETIME', 'TOTALDEMAND', 'REGIONID']],\n", + " on=['DATETIME', 'REGIONID'],\n", + " how='inner'\n", + ")\n", + "print(f\"Merged forecast and demand: {len(merged_df)} rows\")\n", + "\n", + "# If merge failed, debug by examining values more closely\n", + "if len(merged_df) == 0:\n", + " print(\"\\nDEBUG: Merge failed - examining DATETIME values\")\n", + "\n", + " # Convert all to strings in ISO format for comparison\n", + " df_forecast['DATETIME_STR'] = df_forecast['DATETIME'].dt.strftime('%Y-%m-%d %H:%M:%S')\n", + " df_demand['DATETIME_STR'] = df_demand['DATETIME'].dt.strftime('%Y-%m-%d %H:%M:%S')\n", + "\n", + " # Print some samples for comparison\n", + " print(\"\\nForecast DATETIME samples:\")\n", + " print(df_forecast['DATETIME_STR'].head(5).tolist())\n", + " print(\"\\nDemand DATETIME samples:\")\n", + " print(df_demand['DATETIME_STR'].head(5).tolist())\n", + "\n", + " # Check if there are any exact matches\n", + " forecast_set = set(df_forecast['DATETIME_STR'].tolist())\n", + " demand_set = set(df_demand['DATETIME_STR'].tolist())\n", + " common = forecast_set.intersection(demand_set)\n", + " print(f\"\\nNumber of common datetime values: {len(common)}\")\n", + "\n", + " # Try a more flexible merge on date only\n", + " print(\"\\nTrying a more flexible merge on date only...\")\n", + " df_forecast['DATE'] = df_forecast['DATETIME'].dt.date\n", + " df_demand['DATE'] = df_demand['DATETIME'].dt.date\n", + "\n", + " date_merged = pd.merge(\n", + " df_forecast,\n", + " df_demand[['DATE', 'TOTALDEMAND', 'REGIONID']],\n", + " on=['DATE', 'REGIONID'],\n", + " how='inner'\n", + " )\n", + " print(f\"Date-only merge produced {len(date_merged)} rows\")\n", + "\n", + " if len(date_merged) > 0:\n", + " merged_df = date_merged\n", + " print(\"Using date-only merge for analysis\")\n", + " else:\n", + " print(\"Analysis cannot continue without matching data\")\n", + " exit(1)\n", + "\n", + "# Aggregate temperature by hour to handle multiple readings per hour\n", + "print(\"Aggregating temperature data by hour...\")\n", + "df_temperature['hour'] = df_temperature['DATETIME'].dt.floor('H')\n", + "hourly_temp = df_temperature.groupby('hour')[temp_column].mean().reset_index()\n", + "hourly_temp.rename(columns={temp_column: 'TEMPERATURE'}, inplace=True)\n", + "\n", + "# Merge with temperature data\n", + "print(\"Merging with temperature data...\")\n", + "# Create an hour column in the merged data for joining\n", + "merged_df['hour'] = merged_df['DATETIME'].dt.floor('H')\n", + "full_df = pd.merge(\n", + " merged_df,\n", + " hourly_temp,\n", + " on='hour',\n", + " how='left'\n", + ")\n", + "print(f\"Final merged dataset: {len(full_df)} rows\")\n", + "\n", + "# If no temperature data was merged, try a different approach\n", + "if full_df['TEMPERATURE'].isna().all():\n", + " print(\"\\nDEBUG: Temperature merge failed - trying date-based match\")\n", + "\n", + " # Create date columns\n", + " merged_df['DATE'] = merged_df['DATETIME'].dt.date\n", + " df_temperature['DATE'] = df_temperature['DATETIME'].dt.date\n", + "\n", + " # Aggregate temperature by date\n", + " daily_temp = df_temperature.groupby('DATE')[temp_column].mean().reset_index()\n", + " daily_temp.rename(columns={temp_column: 'TEMPERATURE'}, inplace=True)\n", + "\n", + " # Merge on date\n", + " full_df = pd.merge(\n", + " merged_df,\n", + " daily_temp,\n", + " on='DATE',\n", + " how='left'\n", + " )\n", + " print(f\"Date-based temperature merge: {len(full_df)} rows with non-null temperature: {full_df['TEMPERATURE'].notna().sum()}\")\n", + "\n", + "# Calculate error metrics\n", + "full_df['ABS_ERROR'] = abs(full_df['FORECASTDEMAND'] - full_df['TOTALDEMAND'])\n", + "full_df['PERC_ERROR'] = 100 * full_df['ABS_ERROR'] / full_df['TOTALDEMAND']\n", + "\n", + "# Overall accuracy metrics\n", + "mae = full_df['ABS_ERROR'].mean()\n", + "mape = full_df['PERC_ERROR'].mean()\n", + "rmse = np.sqrt((full_df['FORECASTDEMAND'] - full_df['TOTALDEMAND']).pow(2).mean())\n", + "\n", + "# Calculate R-squared (coefficient of determination)\n", + "ss_total = ((full_df['TOTALDEMAND'] - full_df['TOTALDEMAND'].mean()) ** 2).sum()\n", + "ss_residual = ((full_df['TOTALDEMAND'] - full_df['FORECASTDEMAND']) ** 2).sum()\n", + "r_squared = 1 - (ss_residual / ss_total)\n", + "\n", + "print(\"\\nOverall Forecast Accuracy Metrics for PERIODID 24:\")\n", + "print(f\"Mean Absolute Error (MAE): {mae:.2f} MW\")\n", + "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")\n", + "print(f\"Root Mean Square Error (RMSE): {rmse:.2f} MW\")\n", + "print(f\"R-squared (R²): {r_squared:.4f}\")\n", + "\n", + "# Save the forecast vs actual data regardless of temperature\n", + "full_df[['DATETIME', 'REGIONID', 'FORECASTDEMAND', 'TOTALDEMAND', 'ABS_ERROR', 'PERC_ERROR']].to_csv(\n", + " 'forecast_vs_actual_periodid24.csv', index=False\n", + ")\n", + "\n", + "# Check if we have temperature data before continuing with temperature analysis\n", + "if full_df['TEMPERATURE'].notna().sum() > 0:\n", + " # Analyze impact of temperature on forecast accuracy\n", + " print(\"\\nAnalyzing temperature impact on forecast accuracy...\")\n", + "\n", + " # Remove rows with missing temperature values\n", + " full_df_with_temp = full_df.dropna(subset=['TEMPERATURE'])\n", + " print(f\"Rows with temperature data: {len(full_df_with_temp)} out of {len(full_df)} total rows\")\n", + "\n", + " # Create temperature bins\n", + " temp_bins = [-10, 0, 5, 10, 15, 20, 25, 30, 35, 40, 50]\n", + " temp_labels = ['<= 0°C', '0-5°C', '5-10°C', '10-15°C', '15-20°C', '20-25°C', '25-30°C', '30-35°C', '35-40°C', '> 40°C']\n", + " full_df_with_temp['TEMP_RANGE'] = pd.cut(full_df_with_temp['TEMPERATURE'], bins=temp_bins, labels=temp_labels)\n", + "\n", + " # Function to calculate R-squared for a group\n", + " def calculate_r_squared(group):\n", + " if len(group) < 3:\n", + " return np.nan\n", + " ss_total = ((group['TOTALDEMAND'] - group['TOTALDEMAND'].mean()) ** 2).sum()\n", + " if ss_total == 0:\n", + " return np.nan\n", + " ss_residual = ((group['TOTALDEMAND'] - group['FORECASTDEMAND']) ** 2).sum()\n", + " return 1 - (ss_residual / ss_total)\n", + "\n", + " # Group by temperature range and calculate accuracy metrics\n", + " accuracy_by_temp = full_df_with_temp.groupby('TEMP_RANGE').agg({\n", + " 'ABS_ERROR': 'mean',\n", + " 'PERC_ERROR': 'mean',\n", + " 'TEMPERATURE': 'count'\n", + " }).rename(columns={'ABS_ERROR': 'MAE', 'PERC_ERROR': 'MAPE', 'TEMPERATURE': 'Count'})\n", + "\n", + " # Calculate R-squared for each temperature range\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n", + " accuracy_by_temp['R-squared'] = r_squared_by_temp\n", + "\n", + " print(\"\\nForecast Accuracy by Temperature Range:\")\n", + " print(accuracy_by_temp)\n", + "\n", + " # Save temperature analysis to CSV\n", + " full_df_with_temp.to_csv('forecast_vs_actual_with_temp_periodid24.csv', index=False)\n", + " accuracy_by_temp.reset_index().to_csv('accuracy_by_temperature_periodid24.csv', index=False)\n", + "\n", + " # Create visualizations\n", + " # Due to saving time , plotting are being saved to machine\n", + "\n", + " # 1. Scatter plot of Forecasted vs Actual Demand colored by temperature\n", + " plt.figure(figsize=(10, 8))\n", + " scatter = plt.scatter(\n", + " full_df_with_temp['TOTALDEMAND'],\n", + " full_df_with_temp['FORECASTDEMAND'],\n", + " c=full_df_with_temp['TEMPERATURE'],\n", + " cmap='coolwarm',\n", + " alpha=0.7\n", + " )\n", + " plt.colorbar(scatter, label='Temperature (°C)')\n", + " plt.plot([full_df_with_temp['TOTALDEMAND'].min(), full_df_with_temp['TOTALDEMAND'].max()],\n", + " [full_df_with_temp['TOTALDEMAND'].min(), full_df_with_temp['TOTALDEMAND'].max()],\n", + " 'k--', label='Perfect Forecast')\n", + " plt.title(f'Forecast vs Actual Demand (PERIODID {target_periodid})')\n", + " plt.xlabel('Actual Demand (MW)')\n", + " plt.ylabel('Forecasted Demand (MW)')\n", + " plt.legend()\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('forecast_vs_actual_periodid24.png')\n", + " plt.close()\n", + "\n", + " # 2. Error distribution by temperature range\n", + " plt.figure(figsize=(12, 8))\n", + " sns.boxplot(x='TEMP_RANGE', y='PERC_ERROR', data=full_df_with_temp)\n", + " plt.title(f'Forecast Error Distribution by Temperature Range (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature Range')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.xticks(rotation=45)\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('error_by_temperature_periodid24.png')\n", + " plt.close()\n", + "\n", + " # 3. Correlation between temperature and forecast error\n", + " if len(full_df_with_temp) > 10:\n", + " plt.figure(figsize=(10, 6))\n", + " plt.scatter(full_df_with_temp['TEMPERATURE'], full_df_with_temp['PERC_ERROR'], alpha=0.5)\n", + " plt.title(f'Temperature vs Forecast Error (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature (°C)')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.grid(True, alpha=0.3)\n", + "\n", + " # Add trend line\n", + " if len(full_df_with_temp) > 2: # Need at least 3 points for a trend line\n", + " z = np.polyfit(full_df_with_temp['TEMPERATURE'], full_df_with_temp['PERC_ERROR'], 1)\n", + " p = np.poly1d(z)\n", + " temp_range = np.linspace(full_df_with_temp['TEMPERATURE'].min(), full_df_with_temp['TEMPERATURE'].max(), 100)\n", + " plt.plot(temp_range, p(temp_range), \"r--\", linewidth=2)\n", + "\n", + " corr = full_df_with_temp['TEMPERATURE'].corr(full_df_with_temp['PERC_ERROR'])\n", + " plt.annotate(f'Correlation: {corr:.4f}',\n", + " xy=(0.05, 0.95),\n", + " xycoords='axes fraction',\n", + " bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\", ec=\"gray\", alpha=0.8))\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('temperature_vs_error_periodid24.png')\n", + " plt.close()\n", + "\n", + " # 4. R-squared by temperature range\n", + " if 'R-squared' in accuracy_by_temp.columns and not accuracy_by_temp['R-squared'].isna().all():\n", + " plt.figure(figsize=(12, 6))\n", + " sns.barplot(x=accuracy_by_temp.index, y=accuracy_by_temp['R-squared'])\n", + " plt.title(f'R-squared by Temperature Range (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature Range')\n", + " plt.ylabel('R-squared (R²)')\n", + " plt.ylim(0, 1) # R-squared is typically between 0 and 1\n", + " plt.xticks(rotation=45)\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('r_squared_by_temperature_periodid24.png')\n", + " plt.close()\n", + "else:\n", + " print(\"\\nNo temperature data was successfully matched with forecast/demand data.\")\n", + " print(\"Skipping temperature-related analysis.\")\n", + "\n", + "# Create the time series visualization for forecast vs actual\n", + "# Get a sample period for better visualization (most recent 14 days)\n", + "full_df_sorted = full_df.sort_values('DATETIME')\n", + "sample_period = full_df_sorted.tail(min(24*14, len(full_df_sorted)))\n", + "\n", + "plt.figure(figsize=(14, 7))\n", + "plt.plot(sample_period['DATETIME'], sample_period['TOTALDEMAND'], 'b-', label='Actual Demand')\n", + "plt.plot(sample_period['DATETIME'], sample_period['FORECASTDEMAND'], 'r--', label='Forecast Demand')\n", + "plt.title(f'Forecast vs Actual Demand Over Time (PERIODID {target_periodid})')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Demand (MW)')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('time_series_forecast_actual_periodid24.png')\n", + "plt.close()\n", + "\n", + "print(\"\\nAnalysis complete! Results saved to CSV and visualizations saved as PNG files.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/Coding Template.ipynb b/src/Coding Template.ipynb new file mode 100644 index 000000000..f9e668668 --- /dev/null +++ b/src/Coding Template.ipynb @@ -0,0 +1,234 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Template" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "pd.options.mode.chained_assignment = None " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idperiod_idforecast_demanddate_time_currentdate_time_futuredate_time_current_roundedtotal_demandtemperature_futuretemperature_currentforecast_interval
45772772014092648226736.282014-09-27 03:30:542014-09-27 14:30:002014-09-27 03:30:006751.1818.713.80 days 11:00:00
60388612016032947158500.592016-03-30 03:01:152016-03-30 10:30:002016-03-30 03:00:008393.2224.618.80 days 07:30:00
7472832010101710277728.242010-10-17 08:30:512010-10-17 22:00:002010-10-17 08:30:007788.0914.115.00 days 13:30:00
92179192019071145507017.722019-07-12 02:01:252019-07-13 03:00:002019-07-12 02:00:006877.0015.28.31 days 01:00:00
22339162012042923167831.512012-04-29 15:01:242012-04-29 23:00:002012-04-29 15:00:007773.6310.718.10 days 08:00:00
\n", + "
" + ], + "text/plain": [ + " id period_id forecast_demand date_time_current \\\n", + "4577277 2014092648 22 6736.28 2014-09-27 03:30:54 \n", + "6038861 2016032947 15 8500.59 2016-03-30 03:01:15 \n", + "747283 2010101710 27 7728.24 2010-10-17 08:30:51 \n", + "9217919 2019071145 50 7017.72 2019-07-12 02:01:25 \n", + "2233916 2012042923 16 7831.51 2012-04-29 15:01:24 \n", + "\n", + " date_time_future date_time_current_rounded total_demand \\\n", + "4577277 2014-09-27 14:30:00 2014-09-27 03:30:00 6751.18 \n", + "6038861 2016-03-30 10:30:00 2016-03-30 03:00:00 8393.22 \n", + "747283 2010-10-17 22:00:00 2010-10-17 08:30:00 7788.09 \n", + "9217919 2019-07-13 03:00:00 2019-07-12 02:00:00 6877.00 \n", + "2233916 2012-04-29 23:00:00 2012-04-29 15:00:00 7773.63 \n", + "\n", + " temperature_future temperature_current forecast_interval \n", + "4577277 18.7 13.8 0 days 11:00:00 \n", + "6038861 24.6 18.8 0 days 07:30:00 \n", + "747283 14.1 15.0 0 days 13:30:00 \n", + "9217919 15.2 8.3 1 days 01:00:00 \n", + "2233916 10.7 18.1 0 days 08:00:00 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read and format data\n", + "path = 'data/combined_data.csv' #change path\n", + "\n", + "df_all = pd.read_csv(path)\n", + "df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded\n", + "df_all[\"forecast_error\"] = df_all.total_demand - df_all.forecast_demand" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2010-01-01\n", + "1 2010-01-01\n", + "2 2010-01-01\n", + "3 2010-01-01\n", + "4 2010-01-01\n", + " ... \n", + "10853882 2021-03-17\n", + "10853883 2021-03-17\n", + "10853884 2021-03-17\n", + "10853885 2021-03-17\n", + "10853886 2021-03-17\n", + "Name: date_time_current, Length: 10853887, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Tip: use pandas' in-built dateTime to manipulate/extract date variable (example below)\n", + "df_all.date_time_current.dt.date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/Forecast Exploration.ipynb b/src/Forecast Exploration.ipynb new file mode 100644 index 000000000..c55058cc7 --- /dev/null +++ b/src/Forecast Exploration.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chamath\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", + " \n" + ] + } + ], + "source": [ + "df_forecast = pd.read_csv('data/forecastdemand_nsw.csv', names = ['id', 'region_id', 'period_id', 'forecast_demand', 'date_time_curent', 'date_time_future'], skiprows = 1)\n", + "df_forecast.date_time_future = pd.to_datetime(df_forecast.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_forecast.date_time_curent = pd.to_datetime(df_forecast.date_time_curent, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_forecast[\"forecast_interval\"] = df_forecast.date_time_future - df_forecast.date_time_curent\n", + "df_forecast.forecast_interval_hrs = df_forecast.forecast_interval.apply(lambda x: x.total_seconds()/(60*60))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Regions = {'NSW1'}\n", + "Forecast Date Min = 2010-01-01 00:00:00 | Forecast Date Max = 2021-03-18 00:00:00\n", + "Predict Date Min = 2009-12-30 12:31:49 | Predict Date Max = 2021-03-17 23:31:33\n", + "Forecast Demand Min = 4422.46 | Forecast Demand Max = 14736.66\n", + "\n", + " id region_id period_id forecast_demand date_time_curent \\\n", + "0 2009123018 NSW1 71 7832.04 2009-12-30 12:31:49 \n", + "1 2009123019 NSW1 70 7832.04 2009-12-30 13:01:43 \n", + "2 2009123020 NSW1 69 7832.03 2009-12-30 13:31:36 \n", + "3 2009123021 NSW1 68 7832.03 2009-12-30 14:01:44 \n", + "4 2009123022 NSW1 67 7830.96 2009-12-30 14:31:35 \n", + "\n", + " date_time_future \n", + "0 2010-01-01 \n", + "1 2010-01-01 \n", + "2 2010-01-01 \n", + "3 2010-01-01 \n", + "4 2010-01-01 \n", + "\n", + "Rows = 10906019\n" + ] + } + ], + "source": [ + "# Exploration - Forecast Dataset\n", + "print(\"Regions = {}\".format(set(df_forecast.region_id)))\n", + "#print(\"Periods = {}\".format(set(df_forecast.period_id)))\n", + "print(\"Forecast Date Min = {} | Forecast Date Max = {}\".format(df_forecast.date_time_future.min(), df_forecast.date_time_future.max()))\n", + "print(\"Predict Date Min = {} | Predict Date Max = {}\".format(df_forecast.date_time_curent.min(), df_forecast.date_time_curent.max()))\n", + "print(\"Forecast Demand Min = {} | Forecast Demand Max = {}\\n\".format(df_forecast.forecast_demand.min(), df_forecast.forecast_demand.max()))\n", + "print(df_forecast.head())\n", + "print(\"\\nRows = {}\".format(len(df_forecast)))" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAActUlEQVR4nO3de7QU5Znv8e9PiHjnIlsPAeeAhsRBlzGGqNFkYsSjyDjCnGjEZRQNDseMt2SOSXScE42XWbpMRuMk6nAUwcSIhHEiMSoSrzM5EdkoIhcveInsyMh2QJLRKME85496eyg23bD3C7ubZv8+a/Xqqqfeqnrfqt797Lr0W4oIzMzMcuzQ6AqYmVnzchIxM7NsTiJmZpbNScTMzLI5iZiZWbbeja5AvQ0cODCGDh3a6GqYmTWV+fPnvxURLR3jPS6JDB06lNbW1kZXw8ysqUj6dbW4T2eZmVk2JxEzM8vmJGJmZtmcRMzMLJuTiJmZZXMSMTOzbE4iZmaWzUnEzMyyOYmYmVm2HveL9W3Zl84+hxVvrdkoPmhgX3506y1Nve5Gta3WeuuxbrOeoNuSiKQpwAnAyog4MMWuA/4CWAu8DJwVEW+naZcAE4EPgAsiYnaKjwa+B/QCbo2Ia1J8GDAdGAA8DZweEWu7qz31sOKtNbSMuWDj+P03Nv26G9W2Wuutx7rNeoLuPJ01FRjdITYHODAiDgJeBC4BkDQCGA8ckOa5SVIvSb2AHwDHAyOAU1NZgGuB6yNiOLCaIgGZmVkddVsSiYgngFUdYg9FxLo0+iQwJA2PBaZHxPsR8SqwDDg0vZZFxCvpKGM6MFaSgKOBmWn+acC47mqLmZlV18gL618GHkjDg4HlpWltKVYrvifwdikhVeJVSZokqVVSa3t7+1aqvpmZNSSJSLoUWAfcWQlVKRYZ8aoiYnJEjIyIkS0tG3WHb2Zmmep+d5akCRQX3EdFROWLvw3Yp1RsCPBGGq4WfwvoJ6l3Ohoplzczszqp65FIutPqm8CJEfFuadIsYLykPumuq+HAU8A8YLikYZJ2pLj4Pisln0eBk9L8E4B769UOMzMrdFsSkXQX8CvgY5LaJE0Evg/sDsyRtEDSLQARsRiYASwBHgTOjYgP0lHGecBsYCkwI5WFIhn9jaRlFNdIbuuutpiZWXXddjorIk6tEq75RR8RVwNXV4nfD9xfJf4Kxd1bZmbWIO72xMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWVzEjEzs2xOImZmls1JxMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWVzEjEzs2xOImZmls1JxMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWVzEjEzs2xOImZmls1JxMzMsnVbEpE0RdJKSYtKsQGS5kh6Kb33T3FJulHSMkkLJR1SmmdCKv+SpAml+CclPZfmuVGSuqstZmZWXXceiUwFRneIXQw8HBHDgYfTOMDxwPD0mgTcDEXSAS4DDgMOBS6rJJ5UZlJpvo7rMjOzbtZtSSQingBWdQiPBaal4WnAuFL8jig8CfSTNAg4DpgTEasiYjUwBxidpu0REb+KiADuKC3LzMzqpN7XRPaOiBUA6X2vFB8MLC+Va0uxTcXbqsSrkjRJUquk1vb29i1uhJmZFbaVC+vVrmdERryqiJgcESMjYmRLS0tmFc3MrKN6J5E306ko0vvKFG8D9imVGwK8sZn4kCpxMzOro3onkVlA5Q6rCcC9pfgZ6S6tw4E16XTXbOBYSf3TBfVjgdlp2u8kHZ7uyjqjtCwzM6uT3t21YEl3AUcBAyW1UdxldQ0wQ9JE4HXg5FT8fmAMsAx4FzgLICJWSboSmJfKXRERlYv1X6G4A2xn4IH0MjOzOuq2JBIRp9aYNKpK2QDOrbGcKcCUKvFW4MAtqaOZmW2ZbeXCupmZNSEnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWbbejVippK8BZwMBPAecBQwCpgMDgKeB0yNiraQ+wB3AJ4H/AE6JiNfSci4BJgIfABdExOzurPeXzj6HFW+t2Sj+2ssvMnS/j25x/PkXX6JlzMbrXbpkMaPGndrp5TRy3Y1aflfXuzXXPWhgX3506y3VV2K2nat7EpE0GLgAGBERv5c0AxgPjAGuj4jpkm6hSA43p/fVEfERSeOBa4FTJI1I8x0AfBj4haSPRsQH3VX3FW+toWXMBRvFF173la0TX/yVquv9Q+zQpeU0ct0NW34X17s1173i/htrrsNse9eo01m9gZ0l9QZ2AVYARwMz0/RpwLg0PDaNk6aPkqQUnx4R70fEq8Ay4NA61d/MzGhAEomI3wDfAV6nSB5rgPnA2xGxLhVrAwan4cHA8jTvulR+z3K8yjwbkDRJUquk1vb29q3bIDOzHqzuSURSf4qjiGEUp6F2BY6vUjQqs9SYViu+cTBickSMjIiRLS0tXa+0mZlV1YjTWccAr0ZEe0T8AbgHOALol05vAQwB3kjDbcA+AGl6X2BVOV5lHjMzq4NGJJHXgcMl7ZKubYwClgCPAielMhOAe9PwrDROmv5IRESKj5fUR9IwYDjwVJ3aYGZmNODurIiYK2kmxW2864BngMnAz4Hpkq5KsdvSLLcBP5S0jOIIZHxazuJ0Z9eStJxzu/POLDMz21hDficSEZcBl3UIv0KVu6si4j3g5BrLuRq4eqtX0MzMOsW/WDczs2xOImZmls1JxMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWXrVBKRdGRnYmZm1rN09kjkHzsZMzOzHmST3Z5I+jRFD7stkv6mNGkPoFd3VszMzLZ9m+s7a0dgt1Ru91L8t6zvcdfMzHqoTSaRiHgceFzS1Ij4dZ3qZGZmTaKzvfj2kTQZGFqeJyKO7o5KmZlZc+hsEvkJcAtwK+BndpiZGdD5JLIuIm7u1pqYmVnT6ewtvj+T9NeSBkkaUHl1a83MzGyb19kjkcozzr9eigWw79atjpmZNZNOJZGIGNbdFTEzs+bTqSQi6Yxq8Yi4Y+tWx8zMmklnT2d9qjS8EzAKeBpwEjEz68E6ezrr/PK4pL7AD7ulRmZm1jRyu4J/Fxi+NStiZmbNp7PXRH5GcTcWFB0v/ikwo7sqZWZmzaGz10S+UxpeB/w6ItpyVyqpH8Wv3w+kSE5fBl4A7qboWuU14IsRsVqSgO8BYyiOgM6MiKfTciYAf5cWe1VETMutk5mZdV2nTmeljhifp+jJtz+wdgvX+z3gwYjYH/g4sBS4GHg4IoYDD6dxgOMpTp0NByYBNwOkHzteBhwGHApcJqn/FtbLzMy6oLNPNvwi8BRwMvBFYK6krK7gJe0B/BlwG0BErI2It4GxQOVIYhowLg2PBe6IwpNAP0mDgOOAORGxKiJWA3OA0Tl1MjOzPJ09nXUp8KmIWAkgqQX4BTAzY537Au3A7ZI+DswHLgT2jogVABGxQtJeqfxgYHlp/rYUqxU3M7M66ezdWTtUEkjyH12Yt6PewCHAzRHxCeAd1p+6qkZVYrGJ+MYLkCZJapXU2t7e3tX6mplZDZ1NBA9Kmi3pTElnAj8H7s9cZxvQFhFz0/hMiqTyZjpNRXpfWSq/T2n+IcAbm4hvJCImR8TIiBjZ0tKSWW0zM+tok0lE0kckHRkRXwf+CTiI4kL4r4DJOSuMiH8Hlkv6WAqNApYAs1jf0eME4N40PAs4Q4XDgTXptNds4FhJ/dMF9WNTzMzM6mRz10RuAP4WICLuAe4BkDQyTfuLzPWeD9wpaUfgFeAsioQ2Q9JE4HWKi/hQHPGMAZZR3OJ7VqrPKklXAvNSuSsiYlVmfczMLMPmksjQiFjYMRgRrZKG5q40IhYAI6tMGlWlbADn1ljOFGBKbj3MzGzLbO6ayE6bmLbz1qyImZk1n80lkXmS/qpjMJ1ymt89VTIzs2axudNZXwX+RdJprE8aI4Edgb/szoqZmdm2b5NJJCLeBI6Q9HmKfq4Afh4Rj3R7zczMbJvX2eeJPAo82s11MTOzJpP7q3MzMzMnETMzy+ckYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsW8OSiKRekp6RdF8aHyZprqSXJN0taccU75PGl6XpQ0vLuCTFX5B0XGNaYmbWczXySORCYGlp/Frg+ogYDqwGJqb4RGB1RHwEuD6VQ9IIYDxwADAauElSrzrV3czMaFASkTQE+HPg1jQu4GhgZioyDRiXhsemcdL0Uan8WGB6RLwfEa8Cy4BD69MCMzODxh2J3AB8A/hjGt8TeDsi1qXxNmBwGh4MLAdI09ek8v8VrzLPBiRNktQqqbW9vX1rtsPMrEerexKRdAKwMiLml8NVisZmpm1qng2DEZMjYmREjGxpaelSfc3MrLbeDVjnkcCJksYAOwF7UByZ9JPUOx1tDAHeSOXbgH2ANkm9gb7AqlK8ojyPmZnVQd2PRCLikogYEhFDKS6MPxIRpwGPAielYhOAe9PwrDROmv5IRESKj093bw0DhgNP1akZZmZGY45EavkmMF3SVcAzwG0pfhvwQ0nLKI5AxgNExGJJM4AlwDrg3Ij4oP7VNjPruRqaRCLiMeCxNPwKVe6uioj3gJNrzH81cHX31dDMzDbFv1g3M7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iZiZWTYnETMzy+YkYmZm2ZxEzMwsm5OImZllcxIxM7NsdU8ikvaR9KikpZIWS7owxQdImiPppfTeP8Ul6UZJyyQtlHRIaVkTUvmXJE2od1vMzHq6RhyJrAP+d0T8KXA4cK6kEcDFwMMRMRx4OI0DHA8MT69JwM1QJB3gMuAw4FDgskriMTOz+qh7EomIFRHxdBr+HbAUGAyMBaalYtOAcWl4LHBHFJ4E+kkaBBwHzImIVRGxGpgDjK5jU8zMeryGXhORNBT4BDAX2DsiVkCRaIC9UrHBwPLSbG0pVitebT2TJLVKam1vb9+aTTAz69EalkQk7Qb8M/DViPjtpopWicUm4hsHIyZHxMiIGNnS0tL1ypqZWVUNSSKSPkSRQO6MiHtS+M10mor0vjLF24B9SrMPAd7YRNzMzOqkEXdnCbgNWBoR/1CaNAuo3GE1Abi3FD8j3aV1OLAmne6aDRwrqX+6oH5sipmZWZ30bsA6jwROB56TtCDF/ha4BpghaSLwOnBymnY/MAZYBrwLnAUQEaskXQnMS+WuiIhV9WmCmZlBA5JIRPwb1a9nAIyqUj6Ac2ssawowZevVzszMusK/WDczs2xOImZmls1JxMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWVzEjEzs2xOImZmls1JxMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWVzEjEzs2xOImZmls1JxMzMsjmJmJlZNicRMzPL5iRiZmbZnETMzCybk4iZmWVzEjEzs2xNn0QkjZb0gqRlki5udH3MzHqSpk4iknoBPwCOB0YAp0oa0dhamZn1HE2dRIBDgWUR8UpErAWmA2MbXCczsx5DEdHoOmSTdBIwOiLOTuOnA4dFxHkdyk0CJqXRjwEv1LWiXTMQeKvRlegG22u7wG1rVm5b1/z3iGjpGOy9lVdSb6oS2ygrRsRkYHL3V2fLSWqNiJGNrsfWtr22C9y2ZuW2bR3NfjqrDdinND4EeKNBdTEz63GaPYnMA4ZLGiZpR2A8MKvBdTIz6zGa+nRWRKyTdB4wG+gFTImIxQ2u1pZqitNuGbbXdoHb1qzctq2gqS+sm5lZYzX76SwzM2sgJxEzM8vmJNJgknpJekbSfWl8mKS5kl6SdHe6YaDpSOonaaak5yUtlfRpSQMkzUltmyOpf6PrmUPS1yQtlrRI0l2SdmrW/SZpiqSVkhaVYlX3kwo3pi6GFko6pHE137wabbsufSYXSvoXSf1K0y5JbXtB0nGNqfXmVWtXadpFkkLSwDTe7fvMSaTxLgSWlsavBa6PiOHAamBiQ2q15b4HPBgR+wMfp2jjxcDDqW0Pp/GmImkwcAEwMiIOpLihYzzNu9+mAqM7xGrtp+OB4ek1Cbi5TnXMNZWN2zYHODAiDgJeBC4BSN0ljQcOSPPclLpV2hZNZeN2IWkf4H8Ar5fC3b7PnEQaSNIQ4M+BW9O4gKOBmanINGBcY2qXT9IewJ8BtwFExNqIeJuiS5ppqVhTti3pDewsqTewC7CCJt1vEfEEsKpDuNZ+GgvcEYUngX6SBtWnpl1XrW0R8VBErEujT1L8tgyKtk2PiPcj4lVgGUW3StucGvsM4HrgG2z4g+tu32dOIo11A8VO/2Ma3xN4u/QhbwMGN6JiW2hfoB24PZ2qu1XSrsDeEbECIL3v1chK5oiI3wDfofhvbwWwBpjP9rHfKmrtp8HA8lK5Zm/nl4EH0nBTt03SicBvIuLZDpO6vV1OIg0i6QRgZUTML4erFG3Ge7B7A4cAN0fEJ4B3aMJTV9Wk6wNjgWHAh4FdKU4ZdNSM+21ztpfPJ5IuBdYBd1ZCVYo1Rdsk7QJcCnyr2uQqsa3aLieRxjkSOFHSaxS9Dx9NcWTSL50mgebtxqUNaIuIuWl8JkVSebNyKJ3eVzaoflviGODViGiPiD8A9wBHsH3st4pa+2m76GZI0gTgBOC0WP9DuWZu234U/9Q8m75PhgBPS/pv1KFdTiINEhGXRMSQiBhKcUHvkYg4DXgUOCkVmwDc26AqZouIfweWS/pYCo0CllB0STMhxZqybRSnsQ6XtEu6hlVpW9Pvt5Ja+2kWcEa64+dwYE3ltFezkDQa+CZwYkS8W5o0CxgvqY+kYRQXop9qRB27KiKei4i9ImJo+j5pAw5Jf4fdv88iwq8Gv4CjgPvS8L4UH95lwE+APo2uX2abDgZagYXAT4H+FNd8HgZeSu8DGl3PzLZ9G3geWAT8EOjTrPsNuIvi2s4f0pfPxFr7ieLUyA+Al4HnKO5Qa3gbuti2ZRTXCBak1y2l8pemtr0AHN/o+nelXR2mvwYMrNc+c7cnZmaWzaezzMwsm5OImZllcxIxM7NsTiJmZpbNScTMzLI5iWznUo+e3y2NXyTp8q207KmSTtp8yS1ez8mpJ+BHO8SHSvq9pAWl1zbRe66kgyWNaeD6z5TUXtoudzSqLh1JGpc6PKw27XJJF21m/rps2/T52qinXNuQk8j2733gf1a6ht5WdLGH1InAX0fE56tMezkiDi691nZy/d39aOiDgbolkRrtubu0Xc7YwmVtTeOAqkmkk7q8bevQph7LSWT7t47iectf6zih45GEpP9M70dJelzSDEkvSrpG0mmSnpL0nKT9Sos5RtK/pnInpPl7pec2zEvPMPhfpeU+KunHFD986lifU9PyF0m6NsW+BXwGuEXSdZ1psIrnYfw0rftJSQel+OWSJkt6CLijVj1T2W+kujwr6ZoU+6tU9llJ/5z6LKocKS1K8SfS0dAVwCnpKOAUSZ8rHRU8I2n3DnUequI5F9NSXWaWlv/JtD/mS5qt9V2SPCbp7yU9TvFIgc5sm4PTNqk8T6PyrJANliWpJbVxXnodmcrtJun2tG0WSvpCit8sqVXFc1a+XVrfNZKWpLLfkXQEcCJwXdoW+1WpZmXexyRdmz53L0r6bI1tu6uKZ2zMS9t2bJr/TEk/kfQz4CEVz3kZU1r+VElfSNv+XyU9nV5HVKnLAakeC1Jbhndme/cIjf71pV/d+wL+E9iD4lesfYGLgMvTtKnASeWy6f0o4G1gEMWvsX8DfDtNuxC4oTT/gxT/jAyn+PXsThTPLfi7VKYPxS/Xh6XlvgMMq1LPD1N0KdJC0YHjI8C4NO0xqvzSFhgK/J71vz7+QYr/I3BZGj4aWJCGL6focXfnNF6rnscD/w/YJU2r/GJ7z9K6rwLOT8PPAYPTcL/0fibw/VL5nwFHpuHdgN5V2hKlMlPSvvpQqktLip8CTCltl5tq7PczKXpSrmybs1J8IfC5NHxFaV9usCzgx8Bn0vCfAEvT8LWVedJ4/w7bqFda1kHAAIpff6vDtplK6XPXod6XAxeV6vTdNDwG+EWNbfv3wJcq66B4TsiuqVxbqW5/CUxLwztS/HJ9Z4ru/HdK8eFAa2mfLCp9pk4rzbtzo/+2t5WXD/F6gIj4rYpz4hdQfOl2xrxIfexIehl4KMWfA8qnlWZExB+BlyS9AuwPHAscpPVHOX0p/jjXAk9F8byGjj4FPBYR7Wmdd1I8k+Snm6nnyxFxcIfYZ4AvAETEI5L2lNQ3TZsVEZVtUKuexwC3R+pbKSIqz244UNJVFF9UuwGzU/yXwFRJMyg6ZKzml8A/pHbdExFtVcosj4hfpuEfUeyvB4EDgTmSoPiSLvd9dHeN9UFxOuu8ykjaBv0i4vEUmkbRRUu1ZR0DjEjrBNgjHT0dQ9HXGwARsToNflHSJIp/AAZRnK5aArwH3Crp58B9m6hrLZXtOZ/iS72aYyk6M61cS9mJIvEBzCntvweAGyX1oXio0xMR8fu0Xb4v6WDgA+CjVdbxK+BSFc8AuiciXspoy3bJSaTnuAF4Gri9FFtHOqWp4tuifFH6/dLwH0vjf2TDz03HfnOCor+e8yNidnmCpKMojkSqqdZlda5NdX/9Tody1eo5murdZU+lODp6VtKZFEdWRMQ5kg6jeMDYgvRltOHKI65JX6RjgCclHRMRz9eoY3lcwOKI+HSV+nRsz5YqL2sH4NOlhAv81+ckOsSGURw1fSoiVkuaSvGf/TpJh1J0UjkeOI/iyLArKp+7D6j9fSXgCxHxQod6HVZuU0S8J+kx4DiKI7q70qSvAW9SPIFzB4rEt4GI+LGkuRT7eLaksyPikS62ZbvkayI9RPpvbAYbPrb1NeCTaXgsxamTrjpZ0g7p3Pa+FKcvZgNfkfQhAEkfVfFQqk2ZC3xO0kAVF91PBR7fzDy1PAGcltZ9FPBWRPy2Srla9XwI+HLpmsSAVH53YEUqf1plIZL2i4i5EfEt4C2Krrd/l8qXyzwXEddSnDbbv0p9/kRSJVmcCvwbxfZsqcQlfUjSAV3eIkBErAFWS/psCp1O7W38EMWXfqX+B9eI96c4XfoOsEbS3qTnq0jaDegbEfcDX6W4IA4dtk2GjvPPBs5PCQ5Jn9jEvNOBs4DPsv5Isi+wIh1Rn05xtLcBSfsCr0TEjRQ94x60BfXfrjiJ9CzfBcp3af1fii/up4AN/mvrghcovogeAM6JiPcoHve7hOKZBouAf2IzR73p1NklFF2qPws8HRG53alfDoyUtBC4hvXdmndUtZ4R8SDFF0WrpAUU/2UD/B+KZDeHohffiuvSheZFFAns2dSOEZWLv8BXlS6+U5xSfICNLQUmpHoPoHio11qKLuavTfMuoHh+Sa4Jqb4LKb7Ur6hR7gLSNpS0BDgnxa8C+pfa8vkonqb3DLCY4lpO5ZTc7sB9aV2Ps/7mjunA19NF8JoX1jeh47a9kuIfoIVpH1y5iXkfojhN+otYfyffTRTb/UmKU1nV/g5OARalz8P+wDZzy3SjuRdfs22ApKEUjwM4sMFVMesSH4mYmVk2H4mYmVk2H4mYmVk2JxEzM8vmJGJmZtmcRMzMLJuTiJmZZfv/qLPtzKA4j04AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de5xcdX3/8dd7s8mSACGEBIVciCDyU6lVjDysrYpiLSiC+vNaLyBYKvWCWkUottKf+qui1mpboSjKRQsCXgBbK1RFpQoaEDCQAGGFXDaQCySBXDY7u5/+8f3uZLKZy5llZ2cv7+c+5rEz55zvOd+Z+Z7zOd/LnKOIwMzMDKCj3RkwM7Oxw0HBzMzKHBTMzKzMQcHMzMocFMzMrMxBwczMyhwURpGkCyX97Qita6GkJyRNya9vkvTukVh3Xt8PJZ08UutrYrufkrRB0sOjve1qJIWkp7dp20dI+q2kxyV9oB15GIva+Z1MBg4KI0TSg5K25x14k6RfSnqPpPJnHBHviYhPFlzXK+otExErI2KfiOgfgbyfJ+mbQ9Z/fERc+mTX3WQ+FgB/DTwrIp5aZf4x+YDwr0Om3yzplFHK5mg6C7gpIvaNiC8PnZlPBHbkk4PBxx+1IZ9NGwsHdkmnSLq5yvSG+99E5qAwsl4TEfsChwCfAT4GXDzSG5HUOdLrHCMOATZGxLo6y2wF3ilp0ajkaIQM8zs7BLi7wTLvyycHg49fNZkvVZ64WOuMl/3WhaEFImJzRFwHvBk4WdKRAJIukfSp/HyOpB/kWsWjkn4hqUPS5cBC4Pp85neWpEX5zOo0SSuBn1RMqyxoh0n6taTNkq6VNDtv6xhJqyvzOHg2JOk44G+AN+ft3Znnl5ujcr4+LukhSeskXSZpvzxvMB8nS1qZm37OrfXZSNovp1+f1/fxvP5XADcCB+d8XFJjFZuAS4BP1Fj/brWeoZ9Tfl+fyjW5JyRdL+kASd+StEXSb6oEnFdJ6s7v7XOVB1FJp0paJukxST+SdEjFvJD0Xkn3A/fXyO+Jku7O5eAmSc/M038CvAz4l5zPZ9T6TGus90X5vWzO/19UMe8mSZ+W9D/ANuDQ/L1cLGmtpDX5M5pSkeYv8vt8XNI9ko7K08+W9EDF9NdVpHm6pJ/lPGyQ9O08/ed5kTvze3tzrf2hzlvc4zuR1JXT/kFFHg5UqsHPbebzq0hfr+zX3K/y8/MkXSPpm5K2AKcMJw+jLiL8GIEH8CDwiirTVwJn5OeXAJ/Kz/8BuBCYmh8vBlRtXcAiIIDLgL2B6RXTOvMyNwFrgCPzMt8BvpnnHQOsrpVf4LzBZSvm3wS8Oz8/FVgBHArsA3wXuHxI3r6a8/WHQC/wzBqf02XAtcC+Oe19wGm18jkk7THAauCpwBbgiDz9ZuCUau+lxue0AjgM2A+4J+fhFUBnzt83KtIH8FNgNilY31fxubw2r+uZOe3HgV8OSXtjTju9yvt5Bqnm86e5DJyV1zdt6HdQ4/OoOj9v7zHgHTlfb82vD6hItxJ4dp4/Ffg+8G+ksnMg8GvgL/PybySVrRcAAp4OHFIx72DSCeab8/s5KM+7Ajg3z9sL+JMhn83TK17X3B+qvL9638lXgM9WLHsmcH2N9ZwC3FxvX6Z+2T+GxvtVXy4nHdXKwFh8uKbQej2kwjtUH3AQaefqi4hfRC5JdZwXEVsjYnuN+ZdHxNKI2Ar8LfCmyrO9J+FtwD9GRHdEPAGcA7xFu9dS/j4itkfEncCdpOCwm5yXNwPnRMTjEfEg8AXSwauwiHiYdAD5f8N6N+mg/0BEbAZ+CDwQEf8dESXgauB5Q5b/bEQ8GhErgX8iHWQB/hL4h4hYltP+f+C5lbWFPP/RGt/Zm4H/iIgbI6IP+DwpsL6oyrK1fDmfXW+SdHue9mrg/oi4PCJKEXEFsBx4TUW6SyLi7pzv2cDxwAdz+VoHfBF4S1723cD5EfGbSFZExEMAEXF1RPRExEBEfJtUIzo6p+sjNYEdHBE7ImKP9vsKze4Ptb6TS4E/r6hlvAO4vM56Xljx+W2StIkUaAYVKfv1/Coivp8/n1r77ZjioNB684BHq0z/HOkM5IZcDT67wLpWNTH/IdIZ15xCuazv4Ly+ynV3Ak+pmFY5Wmgb6axqqDnAtCrrmjeMPH0W+DNJewSfAh6peL69yuuheR/6uR6cnx8CfKniYPIo6Ux6Xo20Q+32uUbEQF6+mc/jAxExKz+OqrbeinzXytchpLKytuK9/BupxgCwAHig2sYlvVPSHRXpjmRXmTuL9Hn8OjeRnVrnfTS7P1T9TiLiVlJt5aWS/g+pVnNdnfXcUvH5zYqIWaRa1KAiZb9oPscFB4UWkvQC0o64xxlSPlP+64g4lHQG92FJxw7OrrHKRjWJBRXPF5LOvjaQdpIZFfmaAlS2sTZabw/pwFG57hK7H0yL2MCus8fKda1pcj1ExEbSGeLQ0Vy7vVdSU9OTNfRz7cnPV5GaWCoPKtMj4peVWa2z3t0+V0nK22r686i33op8V663Ml+rSE1+cyrex8yIeHbF/MOGbiTXiL4KvI/UNDULWEoKBETEwxHxFxFxMKlW9RXVGHHUYH+optZ3Aqm28HZSLeGaiNhRZz2N1Cv7jfYraLxvjTkOCi0gaaakE4ArSe3bv6uyzAm5I06k9vH+/IBU4A4dxqbfLulZkmaQmlauiTRk9T5gL0mvljSV1PbdVZHuEWBRnY69K4APSXqapH1IzSTfzk0PheW8XAV8WtK++aDyYeCb9VPW9I+kppZnVky7A3iJ0u849iNV95+sj0raX2nI7JnAt/P0C4FzJD0byp3ob2xivVcBr5Z0bP5e/pp0cP5l/WQN/SfwDEl/LqlT0puBZwE/qLZwRKwFbgC+kMtuh6TDJL00L/I14COSnq/k6fm725t00FsPIOldpJoC+fUbJc3PLx/Ly1Yt4w32h2pqfSeQmoteRwoMl9X7oAqoV/Yb7VfjkoPCyLpe0uOkM6tzSQetd9VY9nDgv4EngF8BX4mIm/K8fwA+nqvkH2li+5eTOrMfJnXsfQDSaCjgr0g79xrSGU7lqImr8/+NFe3Slb6e1/1z4PfADuD9TeSr0vvz9rtJNah/z+tvWkRsAc6nos8mIm4kHSDuAm6jxoGwSdfmdd0B/Ad5mHFEfI/UjHVlHl2ylNQ2XzT/95IOXP9MqkW9hjSseeeTyWyuRZ1ACjIbSc04J0TEhjrJ3klq2ruHdAC/htTGT0RcDXya9F09TuqUnh0R95D6hH5FOsj/AfA/Fet8AXCrpCdITThnRsTv87zzgEtzGX8T9feHaqp+Jzm/q4HbSUHoF3XWUUTNsl9gvxqXBke7mJlNGJK+DvRExMfbnZfxZlz8mMLMrCil35m8nj1HkVkBbj4yswlD0idJzXifq2iqsia4+cjMzMpcUzAzs7Jx3acwZ86cWLRoUbuzYWY2rtx2220bIqLq9aDGdVBYtGgRS5YsaXc2zMzGFUlDf/Fe5uYjMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzMgcFMzMrm5BBYdWqVbzhDW9g1apxd9MjM7O2mpBB4fzzz+fxxx/n/PPPb3dWzMzGlZYFBUlfl7RO0tIq8z4iKSTNya8l6cuSVki6S9JRe66xuLPOOot9992Xs84668msxsxs0mllTeES4LihE/Pt8/6U3W+OfTzpzkuHA6cDFzyZDS9YsIBrrrmGBQsWNF7YzMzKWhYUIuLnwKNVZn2RdHvAymt2nwRcFsktwCxJB7Uqb2ZmVt2o9ilIOhFYExF3Dpk1j3Rf40Gr87Rq6zhd0hJJS9avX9+inJqZTU6jFhQkzSDdzP7vqs2uMq3q3X8i4qKIWBwRi+fOrXrlVzMzG6bRvHT2YcDTgDslAcwHbpd0NKlmUNkBMB/oGcW8mZkZo1hTiIjfRcSBEbEoIhaRAsFREfEwcB3wzjwK6YXA5ohYO1p5MzOzpJVDUq8AfgUcIWm1pNPqLP6fQDewAvgq8FetypeZmdXWsuajiHhrg/mLKp4H8N5W5cXMzIqZkL9oNjOz4XFQMDOzMgcFMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzMgcFMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzMgcFMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzMgcFMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzspYFBUlfl7RO0tKKaZ+TtFzSXZK+J2lWxbxzJK2QdK+kP2tVvszMrLZW1hQuAY4bMu1G4MiIeA5wH3AOgKRnAW8Bnp3TfEXSlBbmzczMqmhZUIiInwOPDpl2Q0SU8stbgPn5+UnAlRHRGxG/B1YAR7cqb2ZmVl07+xROBX6Yn88DVlXMW52n7UHS6ZKWSFqyfv36FmfRzGxyaUtQkHQuUAK+NTipymJRLW1EXBQRiyNi8dy5c1uVRTOzSalztDco6WTgBODYiBg88K8GFlQsNh/oGe28mZlNdqNaU5B0HPAx4MSI2FYx6zrgLZK6JD0NOBz49WjmzczMWlhTkHQFcAwwR9Jq4BOk0UZdwI2SAG6JiPdExN2SrgLuITUrvTci+luVNzMzq067WnDGn8WLF8eSJUvanQ0zs3FF0m0RsbjaPP+i2czMyhwUzMyszEHBzMzKHBTMzKzMQcHMzMocFMzMrMxBwczMyhwUzMyszEHBzMzKHBTMzKzMQcHMzMocFMzMrMxBwczMyhwUzMyszEHBzMzKHBTMzKzMQcHMzMocFMzMrMxBwczMyhwUzMyszEHBzMzKHBTMzKzMQcHMzMocFMzMrMxBwczMyhwUzMyszEHBzMzKWhYUJH1d0jpJSyumzZZ0o6T78//983RJ+rKkFZLuknRUq/JlZma1tbKmcAlw3JBpZwM/jojDgR/n1wDHA4fnx+nABS3Ml5mZ1dCyoBARPwceHTL5JODS/PxS4LUV0y+L5BZglqSDWpU3MzOrbrT7FJ4SEWsB8v8D8/R5wKqK5VbnaXuQdLqkJZKWrF+/vqWZNTObbMZKR7OqTItqC0bERRGxOCIWz507t8XZMjObXEY7KDwy2CyU/6/L01cDCyqWmw/0jHLezMwmvdEOCtcBJ+fnJwPXVkx/Zx6F9EJg82Azk5mZjZ7OVq1Y0hXAMcAcSauBTwCfAa6SdBqwEnhjXvw/gVcBK4BtwLtalS8zM6utZUEhIt5aY9axVZYN4L2tyouZmRUzVjqazcxsDHBQMDOzMgcFMzMrc1AwM7MyBwUzMysrFBQknSlpZv4dwcWSbpf0ylZnzszMRlfRmsKpEbEFeCUwl/Q7gs+0LFdmZtYWRYPC4LWJXgV8IyLupPr1iszMbBwrGhRuk3QDKSj8SNK+wEDrsmVmZu1Q9BfNpwHPBbojYpukA/ClKMzMJpyiNYUbI+L2iNgEEBEbgS+2LltmZtYOdWsKkvYCZpAuarc/u/oRZgIHtzhvZmY2yho1H/0l8EFSALiNXUFhC/CvLcyXmZm1Qd2gEBFfAr4k6f0R8c+jlCczM2uTQh3NEfHPkl4ELKpMExGXtShfZmbWBoWCgqTLgcOAO4D+PDkABwUzswmk6JDUxcCz8s1wzMxsgio6JHUp8NRWZsTMzNqvaE1hDnCPpF8DvYMTI+LEluTKzMzaomhQOK+VmTAzs7Gh6Oijn7U6I2Zm1n5FRx89ThptBDANmApsjYiZrcqYmZmNvqI1hX0rX0t6LXB0S3JkZmZtM6zbcUbE94GXj3BezMyszYo2H72+4mUH6XcL/s2CmdkEU3T00WsqnpeAB4GTRjw3ZmbWVkX7FEb0hjqSPgS8m1Tb+B3phj0HAVcCs4HbgXdExM6R3K6ZmdVXqE9B0nxJ35O0TtIjkr4jaf5wNihpHvABYHFEHAlMAd4CfBb4YkQcDjxGutubmZmNoqIdzd8AriPdV2EecH2eNlydwHRJnaSb+KwldVxfk+dfCrz2SazfzMyGoWhQmBsR34iIUn5cAswdzgYjYg3weWAlKRhsJt3AZ1NElPJiq0nBx8zMRlHRjuYNkt4OXJFfvxXYOJwN5tt6ngQ8DdgEXA0cX2XRqqObJJ0OnA6wcOHC4WRh3Ljgggvo7u4GoKenh+3bt1ddbvr06Rx8cLo76qGHHsoZZ5wxank0s4mlaFA4FfgX4Iukg/UvSZ3Dw/EK4PcRsR5A0neBFwGzJHXm2sJ8oKda4oi4CLgIYPHixRN6WGx3dzf3LruLubNgx1boK1VfTgNbeXTtBtZvGt38mdnEUzQofBI4OSIeA5A0m9QEdOowtrkSeKGkGcB24FhgCfBT4A2kEUgnA9cOY91j0nDP+AHmzoI3vWxKoe1c9dP+xguZmdVRNCg8ZzAgAETEo5KeN5wNRsStkq4hDTstAb8lnfn/B3ClpE/laRcPZ/1jUXd3N/ctu4un7id6nwhKNc74e/u3sqVnIw9vntAVIDMbw4oGhQ5J+w+pKRRNu4eI+ATwiSGTu5mg11Pq6emhtwRrN+1+sC/lE/vOXBHYmZfZ2Z/SDNYazMxGS9ED+xeAX+Yz/ADeBHy6ZbmaYGbOnFm1yagvT+vsmr7b9M6cxsxstBX9RfNlkpaQfksg4PURcU9LczYGDbdv4IILLqi6jgceeACAww47rLxs5cihj370oyP/JszM6ijcBJSDwKQLBJW6u7u5/567WDBzCqWtAwz0V2/7L5W2sWP1Y6zaUr/jd6+99mpFNs3Mhm3Y/QKT0WDfwNCD/c78clruG+jtD1Zt6ae3lNJU8m8IzGwsm7RBYbAZp5lmoMq+gd7eXgYGBgAYiPS/dyD9QLyjo4Ouri6md7lvwMzGl0kbFLq7u1lxzz10dXYwUKrezNPf38fONSVWbn4CoGbfwJo1awCYNy9dmcO/Kjaz8WrSBoWenh52lEoEu34Y1tefzvinTkln/DtK/Ty0+XF6S/1uBjKzSWHSBoVqw0QH8uuOrt07gN0MZGaTxbgPCsPpGzjjjDPKTUHNDBE1M5voxn1QSH0Dy+jqnMpAqa/qMv39wc41j7By86N11+UhomY22Y37oLCrb2CXvv7UcTx1Suov2FHq46HNj9JbKrlvwMysjnEfFAb7BqoNEc0vK4aITnPfgJlNGE/mCsy1jPugUK1vwENEzWwySM3ny1m431z6t26v04QOO9dsZOXm9Q3XOe6DwiAf9M1ssknN5308NORg39efrs8/dUo6xA8u01vq26MJfagJExTMzCabWldgHtiegkJH19Tdpk/vmtqwCd1BwcyszUb7CswXXnhhzbw4KJiZtVnqG7iXhTOfSv/WXgb6q9+esb/Uy87Vm1m55eG663syw+sdFMzM2iz1DezkoSEH+z36BvrTMr2lnS0bXu+gYGbWZrX7BtJooo6u3Q/V07s6Wza83kHBzGwEDefSO8PtG2gFBwUzsxF08803s2HDhrrLbN26tbxMT09PzQN9Oy6946BgZjaCqjUFDb6ePn161eUrtfs3Vw4KZmZVTNYrMDsomJlV0d3dzf33rKBrShcD+QZcQ5VK/exYvZNVW1bWXdd4ugKzg4KZWRU9PT30lnZAxTWYd/an0UDTpqRfCvf272DVlofoLfVOmCswOyiYmVVRrW8gtqcaQ0dXx27Tp3dNnzBXYHZQMLMJbbL2DQxXW4KCpFnA14AjSXWzU4F7gW8Di4AHgTdFxGPtyJ+ZTRzjfYjoaGtXTeFLwH9FxBskTQNmAH8D/DgiPiPpbOBs4GNtyp+ZTRDjfYjoaBv1oCBpJvAS4BSAiNgJ7JR0EnBMXuxS4CYcFMwsczPQ6GhHTeFQYD3wDUl/CNwGnAk8JSLWAkTEWkkHVkss6XTgdICFCxeOTo7NrO26u7tZvnwFUzu76OuL6gtFPxvX9bH+0YfqrmsyNAMNVzuCQidwFPD+iLhV0pdITUWFRMRFwEUAixcvrlEyzGyi6enpoa9vR+UIUUr9OwHonDINgL6+XtZvfIi+0o4JM0R0tHU0XmTErQZWR8St+fU1pCDxiKSDAPL/dW3Im5mNUTNnzmTGjOlMnabyAwaAgd2mTZ0mZsyYOENER9uo1xQi4mFJqyQdERH3AscC9+THycBn8v9rRztvZtZ6Re4y5r6B9mnX6KP3A9/KI4+6gXeRai1XSToNWAm8sU15M7MW6u7uZtnyFcw6YCFbt/VTKu15CYmB6Gft+p1s2jhxLh8xXrQlKETEHcDiKrOOHe28mNno6unpodS3g00bd3UG95fS5SOmdKbLR5RKaX6pb+JcPmK88C+azWxYhtsMVPm7gd7eXgYGBhgYyLWFXGvo6Oigq6uLaVPdNzDaHBTMbFi6u7tZuvx+ug5YwM5tJaJKM9COKLF1/Q56N64qT6t2l7E1a9YAMG/ePMB9A+3koDCG9fT0sGUzXPXT/kLLr9sEO6Kn8YJmI6Cnp4eBvt7dDvgDpTREtKMzDRGNUpo/UKUZCNwUNBY5KJjZsFS9fEQp/Yhg+tQho93dDDRuOCiMYQcffDB7aQNvetmUQstf9dN+Zh90cItzZRPNkx0iWrkODxEd/xwUzCa57u5u7lp+H1MOOIiBbTuJ0p7NldtiJ4+tf5z+jWvrrstDRMc/BwWzSa6npwf6enc/4OchouQholHamea7b2DCc1Awm+Sq9w2kDuPpU4c0XU6d4b6BCc5BwWyCcN+AjQQHBbMJwncYs5HgoGA2QTzZO4yB+wbMQcFszHEzkLWTg4LZGJOGiC5HB8whtm3fNRKowtaAjes3EBvrNxe5Gcia5aBgNsakIaJ9ex7wS6X0v7MTSnl+X5+HiNqIclAwG2Oq9Q0AbM9BYfrUqbsmTp3qIaI2ohwUzFpkJPsGYPe7jLlvwFrFQcGsRUZyiCi4f8BGh4OCWYvUbAaqMUzUQ0RtLHBQMGvAzUA2mTgomDWQhogugwP2g21P7BoFVGFr9LNhfQ9s3NxwfW4GsrHMQcGsgTREtAQbN+0+Y/AS051TUqDYuAn6+j1E1MY1BwWzBmoPEc19A1O7dk2cWr1vwGy8cFCwScN9A2aNOSjYpJH6Bu6BA/aBbdt2Nf9U2Bp9bFhfgo1PNFyf+wZsInJQsEljV9/A47vPKA2k/50dKVBsfNx9AzZpOSjYuFOkGQh2NQUNNu1U9g309vYyMJCCwcBAANBRCjo6Oujq6nLfgE1aDgo27qRmoKUwZy/YvhP6Bqout5VeNix/tPy6Vt/AmjVrAJg3b577BmzSa1tQkDQFWAKsiYgTJD0NuBKYDdwOvCMidrYrfzZ2pWagAdiwY/cZlc1AkJbpG3AzkFkTOtq47TOBZRWvPwt8MSIOBx4DTmtLrmzMmzlzJnvP2Ju9p07f7dGR/3abPmNvNwOZNaEtNQVJ84FXA58GPixJwMuBP8+LXAqcB1xQdQU2IQy3b6DIEFHwXcbMhqNdzUf/BJwF7JtfHwBsiojB6wesBuZVSyjpdOB0gIULF7Y4m9ZK3d3d/G75XUw9AErbIPa8egQA22MrG5fXv9ooeIio2UgY9aAg6QRgXUTcJumYwclVFo1q6SPiIuAigMWLF1ddxsaHnp4eog/6Nu4+fTA4qHPX6+jDfQNmo6AdNYU/Bk6U9CpgL2AmqeYwS1Jnri3MB/Y8AtiYNNJDRCP/Vyl1eXV0dNA1o8t9A2ajYNSDQkScA5wDkGsKH4mIt0m6GngDaQTSycC1o503G57u7m7uXn4X+8yG7dugf8/7zAPQF1t5rKIZqMgQUXDfgNloGku/U/gYcKWkTwG/BS5uc36soJ6eHvr74IkhzUAD+SoSHVPS//4+6C+5GchsLGtrUIiIm4Cb8vNu4Oh25seGp9EdxvaaVnGHsWn+pbDZWDaWagrWZh4iamYOClbW3d3NsmV3sd/+8MTWqjcYA6B/YCvLlnmIqNlE5KBgZT09PZRKsOmx3af3576BKblvoFRy34DZROWgYGWN+ga63DdgNuE5KExAzfYNQGrvd9+AmTkoTEA333wzGzY0bvPfunVrebmenp6aB3r3DZhNHg4KE1CjZqDp06fvMW9oU5BrAmaTk4PCGLd+E1z1093vJbwp3z541j57Ljv7oNq/FHYzkJk14qAwhh166KFVpz+WD+6zDzpst+mzD6qdBtwMZGaNOSiMYZVn8ZVn/EPVO+N3TcDMmuGgMA75jN/MWsVBYZzwGb+ZjYZ23qPZzMzGGAcFMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzMgcFMzMrU0S0Ow/DJmk98FCN2XOAxtePdjqnm/jpxkMenW500x0SEXOrzomICfkAljid0znd+Mij042ddG4+MjOzMgcFMzMrm8hB4SKnczqnG/VtOd04TzeuO5rNzGxkTeSagpmZNclBwczMyiZkUJB0nKR7Ja2QdHbBNF+XtE7S0ia3tUDSTyUtk3S3pDMLpttL0q8l3ZnT/X0T25wi6beSftBkXh+U9DtJd0haUjDNLEnXSFqe3+MfFUhzRN7G4GOLpA8W3N6H8uexVNIVkgrdZk7SmTnN3fW2Ve17ljRb0o2S7s//9y+Y7o15ewOSFjexvc/lz/MuSd+TNKtguk/mNHdIukHSwUXSVcz7iKSQNKfg9s6TtKbie3xV0e1Jen/eB++WdH7B7X27YlsPSrqjYLrnSrplsFxLOrpguj+U9Ku8T1wvaWaVdFX370Zlpk66mmWmTpq65aVOuoblparhjGMdyw9gCvAAcCgwDbgTeFaBdC8BjgKWNrm9g4Cj8vN9gfsKbk/APvn5VOBW4IUFt/lh4N+BHzSZ1weBOU2muRR4d34+DZg1jO/jYdKPZRotOw/4PTA9v74KOKVAuiOBpcAM0t0E/xs4vOj3DJwPnJ2fnw18tmC6ZwJHADcBi5vY3iuBzvz8s01sb2bF8w8AFxYtx8AC4EekH3vuUQZqbO884CMNPvtq6V6Wv4Ou/PrAovmsmP8F4O8Kbu8G4Pj8/FXATQXT/QZ4aX5+KvDJKumq7t+NykyddDXLTJ00dctLnXQNy0u1x0SsKRwNrIiI7ojYCVwJnNQoUUT8HHi02Y1FxNqIuD0/fxxYRjq4NUoXEfFEfs6EmJ8AAAZaSURBVDk1Pxr2+kuaD7wa+FqzeW1WPnN6CXAxQETsjIhNTa7mWOCBiKj1y/OhOoHpkjpJB/meAmmeCdwSEdsiogT8DHhdtQVrfM8nkYIf+f9ri6SLiGURcW+9jNVId0POJ8AtwPyC6bZUvNybKuWlTjn+InBWtTQN0tVVI90ZwGciojcvs66Z7UkS8CbgioLpAhg8y9+PKmWmRrojgJ/n5zcC/7dKulr7d90yUytdvTJTJ03d8lInXcPyUs1EDArzgFUVr1dT4CA9EiQtAp5HOusvsvyUXEVeB9wYEUXS/RNp5x4YRhYDuEHSbZJOL7D8ocB64BtKzVVfk7R3k9t8C1V27qqZi1gDfB5YCawFNkfEDQWSLgVeIukASTNIZ4sLmsjjUyJibc7DWuDAJtI+WacCPyy6sKRPS1oFvA34u4JpTgTWRMSdw8jf+3ITxNerNavV8AzgxZJulfQzSS9ocpsvBh6JiPsLLv9B4HP5c/k8cE7BdEuBE/PzN9KgzAzZvwuXmWaPCw3S1C0vQ9MNp7xMxKCgKtNaPu5W0j7Ad4APDonQNUVEf0Q8lxT5j5Z0ZINtnACsi4jbhpnNP46Io4DjgfdKekmD5TtJVe4LIuJ5wFZSVbkQSdNIO93VBZffn3QG9jTgYGBvSW9vlC4ilpGq1TcC/0VqMizVTTQGSDqXlM9vFU0TEedGxIKc5n0FtjEDOJeCB4QhLgAOA55LCtJfKJiuE9gfeCHwUeCqfPZf1FspeCKRnQF8KH8uHyLXbAs4lbQf3EZqdtlZa8Hh7N/DTVcrTaPyUi1ds+UFJmZQWM3uEX8+xZoghk3SVNKX8a2I+G6z6XOTzE3AcQ0W/WPgREkPkprFXi7pm01spyf/Xwd8j9TUVs9qYHVFDeYaUpAo6njg9oh4pODyrwB+HxHrI6IP+C7woiIJI+LiiDgqIl5CaiYoepYJ8IikgwDy/z2aO0aapJOBE4C3RW70bdK/U6W5o4rDSEH2zlxu5gO3S3pqo4QR8Ug+cRkAvkrj8jJoNfDd3ET6a1Ktdo/O7Wpys+HrgW8X3BbAyaSyAukEpFA+I2J5RLwyIp5PCkIP1MhTtf27YZkZznGhVppG5aXAtoqWlwkZFH4DHC7paflM9S3Ada3aWD4DuhhYFhH/2ES6uYOjCCRNJx0Ql9dLExHnRMT8iFhEel8/iYiGZ9J5G3tL2nfwOanzqu5Iq4h4GFgl6Yg86VjgniLby5o941sJvFDSjPy5HktqH21I0oH5/0LSQaWZ7V5HOrCQ/1/bRNqmSToO+BhwYkRsayLd4RUvT6RBeQGIiN9FxIERsSiXm9WkTsmHC2zvoIqXr6NBeanwfeDleR3PIA1QKHqVz1cAyyNidcHlIZ30vTQ/fzkFTwgqykwH8HHgwirL1Nq/65aZ4RwXaqVpVF7qpGu6vAATb/RR7BqBcB8p8p9bMM0VpCpyH2nHOa1guj8hNU/dBdyRH68qkO45wG9zuqVUGWnRIP0xNDH6iNQ/cGd+3N3E5/JcYEnO5/eB/QummwFsBPZr8n39fS68S4HLySNYCqT7BSlg3Qkc28z3DBwA/Jh0MPkxMLtgutfl573AI8CPCqZbQer3Giwv1UYRVUv3nfy53AVcT+pMbKocU2MEWo3tXQ78Lm/vOuCggummAd/Meb0deHnRfAKXAO9p8vv7E+C2/N3fCjy/YLozSceJ+4DPkK/wUGT/blRm6qSrWWbqpKlbXuqka1heqj18mQszMyubiM1HZmY2TA4KZmZW5qBgZmZlDgpmZlbmoGBmZmUOCmZNkLRITV5J12w8cVAwa7P8K16zMcFBwax5UyR9NV+7/gZJ07Xrmv6D17zfH0DSTcrXzZc0J19qAkmnSLpa0vWkSz+bjQkOCmbNOxz414h4NrCJdE2Zy4CPRcRzSL8C/kSB9fwRcHJEvLxlOTVrkoOCWfN+HxGDdwW7jXTRuVkR8bM87VLSfSgauTEimr6HgVkrOSiYNa+34nk/sMftNCuU2LWfDb216NaRzJTZSHBQMHvyNgOPSXpxfv0O0t3fIF2A7vn5+RtGOV9mTfOoB7ORcTJwYb6pTTfwrjz986SbzLwD+Em7MmdWlK+SamZmZW4+MjOzMgcFMzMrc1AwM7MyBwUzMytzUDAzszIHBTMzK3NQMDOzsv8Fzz1vwWeNTmUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "forecasts_per_interval = df_forecast.groupby(\"date_time_future\", as_index = False).count().rename({\"id\" : \"counts\"}, axis = 1)[[\"counts\", \"date_time_future\"]]\n", + "sns.histplot(forecasts_per_interval.counts, bins = 50);\n", + "plt.xlabel(\"Number of Forecasts per Forecast Intervals\")\n", + "\n", + "plt.figure()\n", + "forecasts_per_interval[\"hour\"] = forecasts_per_interval.date_time_future.dt.hour\n", + "sns.boxplot(data = forecasts_per_interval, x = \"hour\", y = \"counts\", fliersize = 1)\n", + "#E.g. gives the distribution of counts made for future times at a particular hour of the day. Median count for a 3am forecast (future) is ~80, with some future times getting 140 predictions.\n", + "plt.title(\"Distribution of Number of Forecasts by Hour\");" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3, 4, 5, 6]), )" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_forecast_count_per_date = df_forecast.loc[df_forecast.date_time_future.dt.year < 2021].copy()\n", + "df_forecast_count_per_date[\"date\"] = df_forecast.date_time_future.dt.normalize()\n", + "df_forecast_count_per_date = df_forecast_count_per_date.groupby(\"date\", as_index = False).count().rename({\"id\" : \"counts\"}, axis = 1)[[\"date\", \"counts\"]]\n", + "df_forecast_count_per_date[\"day_of_week\"] = df_forecast_count_per_date.date.dt.dayofweek\n", + "df_forecast_count_per_date[\"day_of_week_name\"] = df_forecast_count_per_date.date.dt.day_name()\n", + "\n", + "plt.figure()\n", + "sns.boxplot(data = df_forecast_count_per_date.sort_values(\"day_of_week\"), x = \"day_of_week_name\", y = \"counts\")\n", + "#E.g. gives the distribution of counts made for future times on Friday. Median count for a Friday forecast (future) times is ~55, with some future times getting 140 predictions.\n", + "plt.title(\"Distribution of Number of Forecasts by Forecast Intervals\");\n", + "plt.xticks(rotation = 90)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_forecast_count_per_date = df_forecast.loc[df_forecast.date_time_future.dt.year < 2021].copy()\n", + "df_forecast_count_per_date[\"date\"] = df_forecast.date_time_future.dt.normalize()\n", + "df_forecast_count_per_date = df_forecast_count_per_date.groupby([\"date\", as_index = False).count().rename({\"id\" : \"counts\"}, axis = 1)[[\"date\", \"counts\"]]\n", + "df_forecast_count_per_date[\"day_of_week\"] = df_forecast_count_per_date.date.dt.dayofweek\n", + "df_forecast_count_per_date[\"day_of_week_name\"] = df_forecast_count_per_date.date.dt.day_name()\n", + "\n", + "plt.figure()\n", + "sns.boxplot(data = df_forecast_count_per_date.sort_values(\"day_of_week\"), x = \"day_of_week_name\", y = \"counts\")\n", + "#E.g. gives the distribution of counts made for future times on Friday. Median count for a Friday forecast (future) times is ~55, with some future times getting 140 predictions.\n", + "plt.title(\"Distribution of Number of Forecasts by Forecast Intervals\");\n", + "plt.xticks(rotation = 90)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/Forecast Intervals.ipynb b/src/Forecast Intervals.ipynb new file mode 100644 index 000000000..a0bc8fcb6 --- /dev/null +++ b/src/Forecast Intervals.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "pd.options.mode.chained_assignment = None " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df_forecast = pd.read_csv('data/forecastdemand_nsw.csv', names = ['id', 'region_id', 'period_id', 'forecast_demand', 'date_time_current', 'date_time_future'], skiprows = 1)\n", + "df_forecast.date_time_future = pd.to_datetime(df_forecast.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_forecast.date_time_current = pd.to_datetime(df_forecast.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def create_forecast_interval(df_forecast, interval, interval_min, interval_max):\n", + " df_forecast_interval = df_forecast.loc[(df_forecast.forecast_interval > interval_min) & (df_forecast.forecast_interval < interval_max)]\n", + " df_forecast_interval[\"difference_from_interval\"] = abs(df_forecast_interval.forecast_interval - interval)\n", + " df_forecast_interval = df_forecast_interval.loc[df_forecast_interval.groupby(\"date_time_future\")[\"difference_from_interval\"].idxmin()]\n", + " df_forecast_interval = df_forecast_interval.rename({\"forecast_demand\": \"forecast_demand_{}h\".format(interval)}, axis = 1)\n", + " return df_forecast_interval\n", + "\n", + "#Forecast_interval is the number of hours between prediction and it's forecast (in hours)\n", + "df_forecast[\"forecast_interval\"] = df_forecast.date_time_future - df_forecast.date_time_current\n", + "df_forecast.forecast_interval = df_forecast.forecast_interval.apply(lambda x: x.total_seconds()/(60*60))\n", + "\n", + "df_forecast_24h = create_forecast_interval(df_forecast, interval = 24, interval_min = 24, interval_max = 25)\n", + "df_forecast_18h = create_forecast_interval(df_forecast, interval = 18, interval_min = 18, interval_max = 19)\n", + "df_forecast_12h = create_forecast_interval(df_forecast, interval = 12, interval_min = 12, interval_max = 13)\n", + "df_forecast_6h = create_forecast_interval(df_forecast, interval = 6, interval_min = 6, interval_max = 6.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Missing Forecasts**\n", + "24h: 35.42%\n", + "18h: 10.42%\n", + "12h: 0.0%\n", + "6h: 0.04%\n" + ] + } + ], + "source": [ + "df_forecast_set_intervals = pd.DataFrame({\"date_time_future\" : df_forecast.date_time_future.unique()}).sort_values(\"date_time_future\")\n", + "df_forecast_set_intervals = pd.merge(df_forecast_set_intervals, df_forecast_24h[[\"date_time_future\", \"forecast_demand_24h\"]], how = 'left', on = \"date_time_future\")\n", + "df_forecast_set_intervals = pd.merge(df_forecast_set_intervals, df_forecast_18h[[\"date_time_future\", \"forecast_demand_18h\"]], how = 'left', on = \"date_time_future\")\n", + "df_forecast_set_intervals = pd.merge(df_forecast_set_intervals, df_forecast_12h[[\"date_time_future\", \"forecast_demand_12h\"]], how = 'left', on = \"date_time_future\")\n", + "df_forecast_set_intervals = pd.merge(df_forecast_set_intervals, df_forecast_6h[[\"date_time_future\", \"forecast_demand_6h\"]], how = 'left', on = \"date_time_future\")\n", + "\n", + "intervals_count = len(df_forecast_set_intervals)\n", + "print(\"**Missing Forecasts**\\n24h: {}%\\n18h: {}%\\n12h: {}%\\n6h: {}%\".format(round(100*df_forecast_set_intervals.forecast_demand_24h.isna().sum()/intervals_count,2),\n", + " round(100*df_forecast_set_intervals.forecast_demand_18h.isna().sum()/intervals_count,2),\n", + " round(100*df_forecast_set_intervals.forecast_demand_12h.isna().sum()/intervals_count,2),\n", + " round(100*df_forecast_set_intervals.forecast_demand_6h.isna().sum()/intervals_count,2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observation: similar distributions\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_forecast_set_intervals_explore = df_forecast_set_intervals.copy()\n", + "df_forecast_set_intervals_explore[\"update_24h_to_18h\"] = df_forecast_set_intervals_explore.forecast_demand_18h - df_forecast_set_intervals_explore.forecast_demand_24h\n", + "df_forecast_set_intervals_explore[\"update_18h_to_12h\"] = df_forecast_set_intervals_explore.forecast_demand_12h - df_forecast_set_intervals_explore.forecast_demand_18h\n", + "df_forecast_set_intervals_explore[\"update_12h_to_6h\"] = df_forecast_set_intervals_explore.forecast_demand_6h - df_forecast_set_intervals_explore.forecast_demand_12h\n", + "\n", + "plt.figure(figsize = (8,4))\n", + "sns.histplot(df_forecast_set_intervals_explore.update_24h_to_18h, alpha = 0.2, label = 'Update from 24h -> 18h', kde = True)\n", + "sns.histplot(df_forecast_set_intervals_explore.update_18h_to_12h, alpha = 0.2, label = 'Update from 18h -> 12h', kde = True)\n", + "sns.histplot(df_forecast_set_intervals_explore.update_12h_to_6h, alpha = 0.2, label = \"Update from 12h -> 6h\", kde = True)\n", + "plt.xlim(-750, 750)\n", + "plt.xlabel(\"Demand Update\")\n", + "plt.legend()\n", + "print(\"Observation: similar distributions\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/Making temprature range equal sample size Analysis.ipynb b/src/Making temprature range equal sample size Analysis.ipynb new file mode 100644 index 000000000..bdad4933f --- /dev/null +++ b/src/Making temprature range equal sample size Analysis.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-03-25T18:29:34.757662800Z", + "start_time": "2025-03-25T18:29:16.208549100Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "Demand data: 196513 rows\n", + "Temperature data: 220326 rows\n", + "\n", + "Sample demand data:\n", + " DATETIME TOTALDEMAND REGIONID\n", + "0 1/1/2010 0:00 8038.00 NSW1\n", + "1 1/1/2010 0:30 7809.31 NSW1\n", + "2 1/1/2010 1:00 7483.69 NSW1\n", + "3 1/1/2010 1:30 7117.23 NSW1\n", + "4 1/1/2010 2:00 6812.03 NSW1\n", + "\n", + "Sample temperature data:\n", + " LOCATION DATETIME TEMPERATURE\n", + "0 Bankstown 1/1/2010 0:00 23.1\n", + "1 Bankstown 1/1/2010 0:01 23.1\n", + "2 Bankstown 1/1/2010 0:30 22.9\n", + "3 Bankstown 1/1/2010 0:50 22.7\n", + "4 Bankstown 1/1/2010 1:00 22.6\n", + "\n", + "Preparing datetime formats for matching...\n", + "\n", + "Aggregating temperature data to match demand timestamps...\n", + "Aggregated temperature data: 98075 rows\n", + "\n", + "Matching demand with temperature data...\n", + "Matched data: 196149 rows\n", + "\n", + "Basic statistics:\n", + "Correlation between Temperature and Demand: 0.1490\n", + "\n", + "Create temperature ranges with approximately equal sample sizes:\n", + "Temperature ranges with equal sample sizes:\n", + " TEMP_RANGE_EQUAL mean std count\n", + "0 -1.4-9.6°C 8090.923141 1374.180464 19716\n", + "1 9.6-12.4°C 8272.064712 1559.005406 19534\n", + "2 12.4-14.5°C 8255.912232 1461.555401 19600\n", + "3 14.5-16.2°C 8013.030343 1260.794113 19820\n", + "4 16.2-17.9°C 7710.532598 1071.712451 19672\n", + "5 17.9-19.3°C 7602.960400 966.825203 19453\n", + "6 19.3-20.8°C 7648.302012 903.238727 19698\n", + "7 20.8-22.4°C 7901.227821 902.386812 19570\n", + "8 22.4-24.6°C 8378.741634 1012.111945 19672\n", + "9 24.6-44.7°C 9263.300834 1411.672929 19414\n", + "\n", + "Creating visualizations...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_18064\\3158245480.py:132: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " demand_by_equal_temp = merged_df.groupby('TEMP_RANGE_EQUAL')['TOTALDEMAND'].agg(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analysis complete. Visualizations saved.\n", + "\n", + "Key Findings:\n", + "1. Overall correlation between temperature and demand: 0.1490\n", + "2. Quadratic regression equation: 7.3922x² + -226.2074x + 9551.9102\n", + "3. R-squared value for quadratic model: -4463.0909\n", + "4. Optimal temperature (minimum demand point): 15.30°C\n", + "5. Correlation in cold temperatures (below 15.30°C): 0.0346\n", + "6. Correlation in hot temperatures (above 15.30°C): 0.4436\n", + "7. Correlation in extreme temperatures:\n", + " - Cold (<5°C): 0.0117\n", + " - Hot (>30°C): 0.3878\n", + "8. Cold region quadratic model (T < 15.30°C):\n", + " - Equation: -7.8373x² + 174.4628x + 7291.6949\n", + " - R-squared: 0.0043\n", + "9. Hot region quadratic model (T > 15.30°C):\n", + " - Equation: 9.3206x² + -285.4092x + 9828.3509\n", + " - R-squared: 0.2285\n", + "\n", + "Calculated demand at various temperatures using the quadratic model:\n", + " At 0°C: 9551.91 MW\n", + " At 5°C: 8605.68 MW\n", + " At 10°C: 8029.06 MW\n", + " At 15°C: 7822.05 MW\n", + " At 20°C: 7984.66 MW\n", + " At 25°C: 8516.88 MW\n", + " At 30°C: 9418.71 MW\n", + " At 35°C: 10690.15 MW\n", + "\n", + "This analysis quantifies the U-shaped relationship between temperature and demand,\n", + "where both very cold and very hot temperatures result in higher electricity demand,\n", + "likely due to heating and cooling requirements respectively.\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import datetime\n", + "\n", + "# File paths\n", + "demand_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\totaldemand_nsw.csv\" # Change path\n", + "temperature_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\temperature_nsw.csv\" # change path\n", + "\n", + "# Load data\n", + "\n", + "demand_df = pd.read_csv(demand_path)\n", + "temperature_df = pd.read_csv(temperature_path)\n", + "\n", + "print(f\"Demand data: {len(demand_df)} rows\")\n", + "print(f\"Temperature data: {len(temperature_df)} rows\")\n", + "\n", + "# Display sample data\n", + "print(\"\\nSample demand data:\")\n", + "print(demand_df.head())\n", + "print(\"\\nSample temperature data:\")\n", + "print(temperature_df.head())\n", + "\n", + "# Convert date formats for matching\n", + "print(\"\\nPreparing datetime formats for matching...\")\n", + "\n", + "def convert_demand_datetime(demand_datetime):\n", + " \"\"\"Convert demand datetime to a standard format\"\"\"\n", + " try:\n", + " dt = datetime.strptime(demand_datetime, '%d/%m/%Y %H:%M')\n", + " return dt.strftime('%Y-%m-%d %H:%M')\n", + " except:\n", + " return demand_datetime\n", + "\n", + "def convert_temp_datetime(temp_datetime):\n", + " \"\"\"Convert temperature datetime to a standard format\"\"\"\n", + " try:\n", + " dt = datetime.strptime(temp_datetime, '%d/%m/%Y %H:%M')\n", + " return dt.strftime('%Y-%m-%d %H:%M')\n", + " except:\n", + " try:\n", + " # Try alternative format if the first one fails\n", + " dt = datetime.strptime(temp_datetime, '%d/%m/%Y %H:%M:%S')\n", + " return dt.strftime('%Y-%m-%d %H:%M')\n", + " except:\n", + " return temp_datetime\n", + "\n", + "# Add standardized datetime columns\n", + "demand_df['DATETIME_STD'] = demand_df['DATETIME'].apply(convert_demand_datetime)\n", + "temperature_df['DATETIME_STD'] = temperature_df['DATETIME'].apply(convert_temp_datetime)\n", + "\n", + "\n", + "# We'll create an hourly aggregation to match with demand data\n", + "print(\"\\nAggregating temperature data to match demand timestamps...\")\n", + "\n", + "# Extract hour component for grouping\n", + "temperature_df['HOUR'] = temperature_df['DATETIME_STD'].apply(\n", + " lambda x: x[:13] if len(x) >= 13 else x # Get YYYY-MM-DD HH part\n", + ")\n", + "\n", + "# Aggregate temperature by hour (average if multiple readings per hour)\n", + "hourly_temp = temperature_df.groupby(['HOUR', 'LOCATION'])['TEMPERATURE'].mean().reset_index()\n", + "print(f\"Aggregated temperature data: {len(hourly_temp)} rows\")\n", + "\n", + "# Now match demand with temperature based on datetime\n", + "print(\"\\nMatching demand with temperature data...\")\n", + "\n", + "# Extract hour component from demand timestamps too\n", + "demand_df['HOUR'] = demand_df['DATETIME_STD'].apply(\n", + " lambda x: x[:13] if len(x) >= 13 else x # Get YYYY-MM-DD HH part\n", + ")\n", + "\n", + "# Merge demand with temperature\n", + "merged_df = pd.merge(\n", + " demand_df,\n", + " hourly_temp,\n", + " left_on='HOUR',\n", + " right_on='HOUR',\n", + " how='inner'\n", + ")\n", + "\n", + "print(f\"Matched data: {len(merged_df)} rows\")\n", + "\n", + "# If the merge didn't work well, try a different approach\n", + "if len(merged_df) < len(demand_df) * 0.5: # Less than 50% matched\n", + " print(\"Low match rate. Trying alternative approach...\")\n", + "\n", + " # Create a date-only field for matching by day\n", + " demand_df['DATE'] = demand_df['DATETIME_STD'].apply(lambda x: x[:10] if len(x) >= 10 else None)\n", + " temperature_df['DATE'] = temperature_df['DATETIME_STD'].apply(lambda x: x[:10] if len(x) >= 10 else None)\n", + "\n", + " # Calculate daily average temperature\n", + " daily_temp = temperature_df.groupby(['DATE', 'LOCATION'])['TEMPERATURE'].mean().reset_index()\n", + "\n", + " # Merge by date\n", + " merged_df = pd.merge(\n", + " demand_df,\n", + " daily_temp,\n", + " on='DATE',\n", + " how='inner'\n", + " )\n", + "\n", + " print(f\"Date-based match: {len(merged_df)} rows\")\n", + "\n", + "# Basic statistics\n", + "print(\"\\nBasic statistics:\")\n", + "print(f\"Correlation between Temperature and Demand: {merged_df['TEMPERATURE'].corr(merged_df['TOTALDEMAND']):.4f}\")\n", + "\n", + "# Calculate statistics by temperature ranges with equal sample sizes\n", + "print(\"\\nCreate temperature ranges with approximately equal sample sizes:\")\n", + "# Determine the number of ranges (bins)\n", + "num_ranges = 10 # You can adjust this number\n", + "\n", + "# Use quantile-based binning to create ranges with equal sample sizes\n", + "quantiles = np.linspace(0, 1, num_ranges+1)\n", + "temp_quantiles = [merged_df['TEMPERATURE'].quantile(q) for q in quantiles]\n", + "temp_quantiles[0] = temp_quantiles[0] - 0.1 # Ensure the lowest temperature is included\n", + "\n", + "# Create labels for these ranges\n", + "equal_size_labels = [f'{temp_quantiles[i]:.1f}-{temp_quantiles[i+1]:.1f}°C' for i in range(num_ranges)]\n", + "\n", + "# Cut the temperature data into these ranges\n", + "merged_df['TEMP_RANGE_EQUAL'] = pd.cut(\n", + " merged_df['TEMPERATURE'],\n", + " bins=temp_quantiles,\n", + " labels=equal_size_labels,\n", + " include_lowest=True\n", + ")\n", + "\n", + "# Group by equal-sized temperature ranges\n", + "demand_by_equal_temp = merged_df.groupby('TEMP_RANGE_EQUAL')['TOTALDEMAND'].agg(\n", + " ['mean', 'std', 'count']\n", + ").reset_index()\n", + "print(\"Temperature ranges with equal sample sizes:\")\n", + "print(demand_by_equal_temp)\n", + "\n", + "# Visualizations\n", + "print(\"\\nCreating visualizations...\")\n", + "\n", + "# 1. Scatter plot of Temperature vs Demand with quadratic regression\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], alpha=0.5)\n", + "plt.title('Relationship between Temperature and Electricity Demand')\n", + "plt.xlabel('Temperature (°C)')\n", + "plt.ylabel('Total Demand (MW)')\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add quadratic regression line\n", + "z = np.polyfit(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], 2) # Quadratic fit\n", + "p = np.poly1d(z)\n", + "temp_range = np.linspace(merged_df['TEMPERATURE'].min(), merged_df['TEMPERATURE'].max(), 100)\n", + "plt.plot(temp_range, p(temp_range), \"r--\", linewidth=2, label=f'Quadratic Fit: {z[0]:.4f}x² + {z[1]:.4f}x + {z[2]:.4f}')\n", + "plt.legend()\n", + "\n", + "# Calculate and show R-squared for the quadratic model\n", + "from sklearn.metrics import r2_score\n", + "y_actual = merged_df['TOTALDEMAND']\n", + "x_data = merged_df['TEMPERATURE']\n", + "y_pred = z[0] * x_data**2 + z[1] * x_data + z[2]\n", + "r2 = r2_score(y_actual, y_pred)\n", + "plt.annotate(f'R² = {r2:.4f}', xy=(0.05, 0.95), xycoords='axes fraction',\n", + " fontsize=10, bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\", ec=\"gray\", alpha=0.8))\n", + "\n", + "plt.savefig('temperature_vs_demand_scatter.png')\n", + "\n", + "# 2. Average Demand by Temperature Range (equal sample sizes)\n", + "plt.figure(figsize=(12, 6))\n", + "sns.barplot(x='TEMP_RANGE_EQUAL', y='mean', data=demand_by_equal_temp)\n", + "plt.title('Average Electricity Demand by Temperature Range (Equal Sample Sizes)')\n", + "plt.xlabel('Temperature Range')\n", + "plt.ylabel('Average Demand (MW)')\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.savefig('avg_demand_by_equal_temp_range.png')\n", + "\n", + "# 3. Time series plot of temperature and demand (sample period)\n", + "if len(merged_df) > 0:\n", + " # Sort by datetime\n", + " if 'DATETIME_STD' in merged_df.columns:\n", + " merged_df['DATETIME_OBJ'] = pd.to_datetime(merged_df['DATETIME_STD'])\n", + " merged_df = merged_df.sort_values('DATETIME_OBJ')\n", + "\n", + " # Get a sample period (first 7 days or less)\n", + " sample_period = merged_df.iloc[:min(24*7, len(merged_df))]\n", + "\n", + " # Create the time series plot\n", + " fig, ax1 = plt.subplots(figsize=(14, 7))\n", + "\n", + " # Plot demand\n", + " color = 'tab:blue'\n", + " ax1.set_xlabel('Date')\n", + " ax1.set_ylabel('Demand (MW)', color=color)\n", + " ax1.plot(sample_period['DATETIME_OBJ'], sample_period['TOTALDEMAND'], color=color)\n", + " ax1.tick_params(axis='y', labelcolor=color)\n", + "\n", + " # Create second y-axis for temperature\n", + " ax2 = ax1.twinx()\n", + " color = 'tab:red'\n", + " ax2.set_ylabel('Temperature (°C)', color=color)\n", + " ax2.plot(sample_period['DATETIME_OBJ'], sample_period['TEMPERATURE'], color=color)\n", + " ax2.tick_params(axis='y', labelcolor=color)\n", + "\n", + " plt.title('Temperature and Demand Over Time')\n", + " fig.tight_layout()\n", + " plt.savefig('temperature_demand_time_series.png')\n", + "\n", + "print(\"\\nAnalysis complete. Visualizations saved.\")\n", + "\n", + "# Additional insights\n", + "print(\"\\nKey Findings:\")\n", + "print(f\"1. Overall correlation between temperature and demand: {merged_df['TEMPERATURE'].corr(merged_df['TOTALDEMAND']):.4f}\")\n", + "\n", + "# Quadratic regression coefficients\n", + "z = np.polyfit(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], 2)\n", + "p = np.poly1d(z)\n", + "print(f\"2. Quadratic regression equation: {z[0]:.4f}x² + {z[1]:.4f}x + {z[2]:.4f}\")\n", + "\n", + "# Calculate R-squared for the quadratic model\n", + "from sklearn.metrics import r2_score\n", + "y_actual = merged_df['TOTALDEMAND']\n", + "x_data = merged_df['TEMPERATURE']\n", + "y_pred = z[0] * x_data**2 + z[1] * x_data**2 + z[2]\n", + "r2 = r2_score(y_actual, y_pred)\n", + "print(f\"3. R-squared value for quadratic model: {r2:.4f}\")\n", + "\n", + "# Calculate optimal temperature (vertex of parabola)\n", + "optimal_temp = -z[1] / (2*z[0])\n", + "print(f\"4. Optimal temperature (minimum demand point): {optimal_temp:.2f}°C\")\n", + "\n", + "# Calculate separate correlations for hot and cold temperatures\n", + "cold_df = merged_df[merged_df['TEMPERATURE'] < optimal_temp]\n", + "hot_df = merged_df[merged_df['TEMPERATURE'] > optimal_temp]\n", + "\n", + "cold_corr = cold_df['TEMPERATURE'].corr(cold_df['TOTALDEMAND'])\n", + "hot_corr = hot_df['TEMPERATURE'].corr(hot_df['TOTALDEMAND'])\n", + "\n", + "print(f\"5. Correlation in cold temperatures (below {optimal_temp:.2f}°C): {cold_corr:.4f}\")\n", + "print(f\"6. Correlation in hot temperatures (above {optimal_temp:.2f}°C): {hot_corr:.4f}\")\n", + "\n", + "# Calculate correlation for extreme temperatures (<5°C and >30°C)\n", + "extreme_cold_df = merged_df[merged_df['TEMPERATURE'] < 5]\n", + "extreme_hot_df = merged_df[merged_df['TEMPERATURE'] > 30]\n", + "\n", + "extreme_cold_corr = extreme_cold_df['TEMPERATURE'].corr(extreme_cold_df['TOTALDEMAND']) if len(extreme_cold_df) > 2 else float('nan')\n", + "extreme_hot_corr = extreme_hot_df['TEMPERATURE'].corr(extreme_hot_df['TOTALDEMAND']) if len(extreme_hot_df) > 2 else float('nan')\n", + "\n", + "print(f\"7. Correlation in extreme temperatures:\")\n", + "print(f\" - Cold (<5°C): {extreme_cold_corr:.4f}\" if not np.isnan(extreme_cold_corr) else \" - Cold (<5°C): Not enough data\")\n", + "print(f\" - Hot (>30°C): {extreme_hot_corr:.4f}\" if not np.isnan(extreme_hot_corr) else \" - Hot (>30°C): Not enough data\")\n", + "\n", + "# Calculate quadratic models for cold and hot regions separately\n", + "if len(cold_df) > 10:\n", + " cold_z = np.polyfit(cold_df['TEMPERATURE'], cold_df['TOTALDEMAND'], 2)\n", + " cold_r2 = r2_score(cold_df['TOTALDEMAND'],\n", + " cold_z[0] * cold_df['TEMPERATURE']**2 + cold_z[1] * cold_df['TEMPERATURE'] + cold_z[2])\n", + " print(f\"8. Cold region quadratic model (T < {optimal_temp:.2f}°C):\")\n", + " print(f\" - Equation: {cold_z[0]:.4f}x² + {cold_z[1]:.4f}x + {cold_z[2]:.4f}\")\n", + " print(f\" - R-squared: {cold_r2:.4f}\")\n", + "\n", + "if len(hot_df) > 10:\n", + " hot_z = np.polyfit(hot_df['TEMPERATURE'], hot_df['TOTALDEMAND'], 2)\n", + " hot_r2 = r2_score(hot_df['TOTALDEMAND'],\n", + " hot_z[0] * hot_df['TEMPERATURE']**2 + hot_z[1] * hot_df['TEMPERATURE'] + hot_z[2])\n", + " print(f\"9. Hot region quadratic model (T > {optimal_temp:.2f}°C):\")\n", + " print(f\" - Equation: {hot_z[0]:.4f}x² + {hot_z[1]:.4f}x + {hot_z[2]:.4f}\")\n", + " print(f\" - R-squared: {hot_r2:.4f}\")\n", + "\n", + "# Create a new plot showing original data and quadratic model fit\n", + "plt.figure(figsize=(12, 8))\n", + "\n", + "# Scatter plot of actual data\n", + "plt.scatter(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], alpha=0.2, color='blue', label='Actual Data')\n", + "\n", + "# Sort temperatures for smooth curve plotting\n", + "temp_sorted = np.sort(merged_df['TEMPERATURE'].unique())\n", + "\n", + "# Plot overall quadratic model\n", + "overall_curve = z[0] * temp_sorted**2 + z[1] * temp_sorted + z[2]\n", + "plt.plot(temp_sorted, overall_curve, 'r-', linewidth=3, label=f'Overall: {z[0]:.4f}x² + {z[1]:.4f}x + {z[2]:.4f}')\n", + "\n", + "# Create and plot separate models for cold and hot regions\n", + "if len(cold_df) > 10:\n", + " cold_temps = np.sort(cold_df['TEMPERATURE'].unique())\n", + " cold_z = np.polyfit(cold_df['TEMPERATURE'], cold_df['TOTALDEMAND'], 2)\n", + " cold_curve = cold_z[0] * cold_temps**2 + cold_z[1] * cold_temps + cold_z[2]\n", + " plt.plot(cold_temps, cold_curve, 'g-', linewidth=2, label=f'Cold Region Model (T < {optimal_temp:.2f}°C)')\n", + "\n", + "if len(hot_df) > 10:\n", + " hot_temps = np.sort(hot_df['TEMPERATURE'].unique())\n", + " hot_z = np.polyfit(hot_df['TEMPERATURE'], hot_df['TOTALDEMAND'], 2)\n", + " hot_curve = hot_z[0] * hot_temps**2 + hot_z[1] * hot_temps + hot_z[2]\n", + " plt.plot(hot_temps, hot_curve, 'y-', linewidth=2, label=f'Hot Region Model (T > {optimal_temp:.2f}°C)')\n", + "\n", + "# Add vertical line at optimal temperature\n", + "plt.axvline(x=optimal_temp, color='purple', linestyle='--', alpha=0.7,\n", + " label=f'Optimal Temperature: {optimal_temp:.2f}°C')\n", + "\n", + "plt.title('Temperature vs. Demand: Quadratic Regression Analysis')\n", + "plt.xlabel('Temperature (°C)')\n", + "plt.ylabel('Demand (MW)')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.savefig('temperature_demand_quadratic_models.png')\n", + "\n", + "print(\"\\nCalculated demand at various temperatures using the quadratic model:\")\n", + "for temp in [0, 5, 10, 15, 20, 25, 30, 35]:\n", + " predicted_demand = z[0] * temp**2 + z[1] * temp + z[2]\n", + " print(f\" At {temp}°C: {predicted_demand:.2f} MW\")\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/R squared including forecast and temp equal.ipynb b/src/R squared including forecast and temp equal.ipynb new file mode 100644 index 000000000..a4cbc0b6c --- /dev/null +++ b/src/R squared including forecast and temp equal.ipynb @@ -0,0 +1,670 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-03-30T11:16:15.652720400Z", + "start_time": "2025-03-30T11:15:56.157099200Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading datasets...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:20: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_temperature = pd.read_csv(temperature_path)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded forecast data: 10906019 rows\n", + "Loaded demand data: 196513 rows\n", + "Loaded temperature data: 220326 rows\n", + "\n", + "Sample forecast datetime: 2010-01-01 00:00:00\n", + "Sample demand datetime: 1/01/2010 0:00\n", + "Sample temperature DATETIME: 1/01/2010 0:00\n", + "Filtered forecast data for PERIODID 24: 196505 rows\n", + "Forecast datetime format appears to be: ISO\n", + "Demand datetime format appears to be: Australian\n", + "Parsing dates...\n", + "\n", + "After parsing:\n", + "Sample forecast datetime: 2010-01-01 00:00:00\n", + "Sample demand datetime: 2010-01-01 00:00:00\n", + "Sample temperature datetime: 2010-01-01 00:00:00\n", + "Using temperature column: TEMPERATURE\n", + "Merging forecast with demand data...\n", + "Merged forecast and demand: 196505 rows\n", + "Aggregating temperature data by hour...\n", + "Merging with temperature data...\n", + "Final merged dataset: 196505 rows\n", + "\n", + "Overall Forecast Accuracy Metrics for PERIODID 24:\n", + "Mean Absolute Error (MAE): 169.09 MW\n", + "Mean Absolute Percentage Error (MAPE): 2.05%\n", + "Root Mean Square Error (RMSE): 234.87 MW\n", + "R-squared (R²): 0.9673\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:138: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " df_temperature['hour'] = df_temperature['DATETIME'].dt.floor('H')\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:145: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " merged_df['hour'] = merged_df['DATETIME'].dt.floor('H')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analyzing temperature impact on forecast accuracy...\n", + "Rows with temperature data: 196141 out of 196505 total rows\n", + "\n", + "Created 10 temperature ranges with approximately equal sample sizes:\n", + " -1.2-9.6°C: 19716 samples\n", + " 9.6-12.4°C: 19531 samples\n", + " 12.4-14.5°C: 19598 samples\n", + " 14.5-16.2°C: 19820 samples\n", + " 16.2-17.9°C: 19672 samples\n", + " 17.9-19.3°C: 19453 samples\n", + " 19.3-20.8°C: 19696 samples\n", + " 20.8-22.4°C: 19570 samples\n", + " 22.4-24.6°C: 19672 samples\n", + " 24.6-44.7°C: 19413 samples\n", + "\n", + "Forecast Accuracy by Temperature Range:\n", + " MAE MAPE Count R-squared\n", + "TEMP_RANGE \n", + "-1.2-9.6°C 135.474387 1.666570 19716 0.983183\n", + "9.6-12.4°C 152.734895 1.820509 19531 0.981756\n", + "12.4-14.5°C 166.439634 1.989024 19598 0.974931\n", + "14.5-16.2°C 162.821001 2.005873 19820 0.969206\n", + "16.2-17.9°C 147.631537 1.911072 19672 0.966248\n", + "17.9-19.3°C 143.563883 1.882025 19453 0.959564\n", + "19.3-20.8°C 150.007322 1.966236 19696 0.949130\n", + "20.8-22.4°C 168.671350 2.124474 19570 0.931492\n", + "22.4-24.6°C 191.211639 2.265433 19672 0.932528\n", + "24.6-44.7°C 273.703233 2.911766 19413 0.934154\n", + "\n", + "Fitting nonlinear model with temperature and forecast as predictors...\n", + "\n", + "Nonlinear Model Results (Polynomial Temperature + Forecast):\n", + "R-squared: 0.9687\n", + "Coefficients:\n", + " Temperature: -25.346849\n", + " Temperature²: 0.769912\n", + " Forecast: 0.969277\n", + " Intercept: 438.465014\n", + "\n", + "R-squared Comparison:\n", + " Temperature Only (Linear): 0.0222\n", + " Temperature Only (Polynomial): 0.0971\n", + " Forecast Only: 0.9679\n", + " Combined Model: 0.9687\n", + "\n", + "Creating 3D visualization of Temperature, Forecast, and Actual Demand...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:222: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " full_df_with_temp['TEMP_RANGE'] = pd.cut(\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:246: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " accuracy_by_temp = full_df_with_temp.groupby('TEMP_RANGE').agg({\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:253: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:253: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Creating visualizations...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\3776373426.py:398: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", + " plt.boxplot(box_data, labels=ranges)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analysis complete! Results saved to CSV and visualizations saved as PNG files.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import datetime\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score\n", + "\n", + "# File paths (Hey team please change to your local path 1,2 3)\n", + "forecast_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\forecastdemand_nsw.csv\" #Change path\n", + "demand_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\totaldemand_nsw.csv\" #Change path\n", + "temperature_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\temperature_nsw.csv\" #change path\n", + "target_periodid = 24\n", + "\n", + "# Load datasets\n", + "\n", + "df_forecast = pd.read_csv(forecast_path)\n", + "df_demand = pd.read_csv(demand_path)\n", + "df_temperature = pd.read_csv(temperature_path)\n", + "\n", + "print(f\"Loaded forecast data: {len(df_forecast)} rows\")\n", + "print(f\"Loaded demand data: {len(df_demand)} rows\")\n", + "print(f\"Loaded temperature data: {len(df_temperature)} rows\")\n", + "\n", + "# Display samples to check datetime formats\n", + "print(\"\\nSample forecast datetime:\", df_forecast['DATETIME'].iloc[0] if len(df_forecast) > 0 else \"No data\")\n", + "print(\"Sample demand datetime:\", df_demand['DATETIME'].iloc[0] if len(df_demand) > 0 else \"No data\")\n", + "temp_dt_col = 'date_time' if 'date_time' in df_temperature.columns else 'DATETIME'\n", + "print(f\"Sample temperature {temp_dt_col}:\", df_temperature[temp_dt_col].iloc[0] if len(df_temperature) > 0 else \"No data\")\n", + "\n", + "# Filter forecast data for PERIODID 24\n", + "df_forecast = df_forecast[df_forecast['PERIODID'] == target_periodid]\n", + "print(f\"Filtered forecast data for PERIODID {target_periodid}: {len(df_forecast)} rows\")\n", + "\n", + "# Check if the forecast and demand datetime are already in ISO format (YYYY-MM-DD)\n", + "forecast_date_format = \"ISO\" if '-' in str(df_forecast['DATETIME'].iloc[0]) else \"Australian\"\n", + "demand_date_format = \"ISO\" if '-' in str(df_demand['DATETIME'].iloc[0]) else \"Australian\"\n", + "print(f\"Forecast datetime format appears to be: {forecast_date_format}\")\n", + "print(f\"Demand datetime format appears to be: {demand_date_format}\")\n", + "\n", + "# Parse dates - with appropriate handling for existing formats\n", + "print(\"Parsing dates...\")\n", + "\n", + "# For forecast data\n", + "if forecast_date_format == \"ISO\":\n", + " # Already in ISO format, just parse\n", + " df_forecast['DATETIME'] = pd.to_datetime(df_forecast['DATETIME'], errors='coerce')\n", + "else:\n", + " # Australian format, convert to ISO\n", + " df_forecast['DATETIME'] = pd.to_datetime(df_forecast['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# For demand data\n", + "if demand_date_format == \"ISO\":\n", + " # Already in ISO format, just parse\n", + " df_demand['DATETIME'] = pd.to_datetime(df_demand['DATETIME'], errors='coerce')\n", + "else:\n", + " # Australian format, convert to ISO\n", + " df_demand['DATETIME'] = pd.to_datetime(df_demand['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# For temperature data\n", + "if 'date_time' in df_temperature.columns:\n", + " # First convert from Australian format to datetime\n", + " df_temperature['date_time'] = pd.to_datetime(df_temperature['date_time'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + " # Then create a DATETIME column in the same format as forecast/demand\n", + " df_temperature['DATETIME'] = df_temperature['date_time']\n", + "else:\n", + " # Directly parse the DATETIME column\n", + " df_temperature['DATETIME'] = pd.to_datetime(df_temperature['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# Drop rows with invalid dates\n", + "df_forecast = df_forecast.dropna(subset=['DATETIME'])\n", + "df_demand = df_demand.dropna(subset=['DATETIME'])\n", + "df_temperature = df_temperature.dropna(subset=['DATETIME'])\n", + "\n", + "# Print sample of parsed dates to verify format consistency\n", + "print(\"\\nAfter parsing:\")\n", + "print(\"Sample forecast datetime:\", df_forecast['DATETIME'].iloc[0] if len(df_forecast) > 0 else \"No data\")\n", + "print(\"Sample demand datetime:\", df_demand['DATETIME'].iloc[0] if len(df_demand) > 0 else \"No data\")\n", + "print(\"Sample temperature datetime:\", df_temperature['DATETIME'].iloc[0] if len(df_temperature) > 0 else \"No data\")\n", + "\n", + "# Identify temperature column\n", + "temp_column = 'temperature' if 'temperature' in df_temperature.columns else 'TEMPERATURE'\n", + "print(f\"Using temperature column: {temp_column}\")\n", + "\n", + "# Merge forecast and demand data\n", + "print(\"Merging forecast with demand data...\")\n", + "merged_df = pd.merge(\n", + " df_forecast,\n", + " df_demand[['DATETIME', 'TOTALDEMAND', 'REGIONID']],\n", + " on=['DATETIME', 'REGIONID'],\n", + " how='inner'\n", + ")\n", + "print(f\"Merged forecast and demand: {len(merged_df)} rows\")\n", + "\n", + "# If merge failed, debug by examining values more closely\n", + "if len(merged_df) == 0:\n", + " print(\"\\nDEBUG: Merge failed - examining DATETIME values\")\n", + "\n", + " # Convert all to strings in ISO format for comparison\n", + " df_forecast['DATETIME_STR'] = df_forecast['DATETIME'].dt.strftime('%Y-%m-%d %H:%M:%S')\n", + " df_demand['DATETIME_STR'] = df_demand['DATETIME'].dt.strftime('%Y-%m-%d %H:%M:%S')\n", + "\n", + " # Print some samples for comparison\n", + " print(\"\\nForecast DATETIME samples:\")\n", + " print(df_forecast['DATETIME_STR'].head(5).tolist())\n", + " print(\"\\nDemand DATETIME samples:\")\n", + " print(df_demand['DATETIME_STR'].head(5).tolist())\n", + "\n", + " # Check if there are any exact matches\n", + " forecast_set = set(df_forecast['DATETIME_STR'].tolist())\n", + " demand_set = set(df_demand['DATETIME_STR'].tolist())\n", + " common = forecast_set.intersection(demand_set)\n", + " print(f\"\\nNumber of common datetime values: {len(common)}\")\n", + "\n", + " # Try a more flexible merge on date only\n", + " print(\"\\nTrying a more flexible merge on date only...\")\n", + " df_forecast['DATE'] = df_forecast['DATETIME'].dt.date\n", + " df_demand['DATE'] = df_demand['DATETIME'].dt.date\n", + "\n", + " date_merged = pd.merge(\n", + " df_forecast,\n", + " df_demand[['DATE', 'TOTALDEMAND', 'REGIONID']],\n", + " on=['DATE', 'REGIONID'],\n", + " how='inner'\n", + " )\n", + " print(f\"Date-only merge produced {len(date_merged)} rows\")\n", + "\n", + " if len(date_merged) > 0:\n", + " merged_df = date_merged\n", + " print(\"Using date-only merge for analysis\")\n", + " else:\n", + " print(\"Analysis cannot continue without matching data\")\n", + " exit(1)\n", + "\n", + "# Aggregate temperature by hour to handle multiple readings per hour\n", + "print(\"Aggregating temperature data by hour...\")\n", + "df_temperature['hour'] = df_temperature['DATETIME'].dt.floor('H')\n", + "hourly_temp = df_temperature.groupby('hour')[temp_column].mean().reset_index()\n", + "hourly_temp.rename(columns={temp_column: 'TEMPERATURE'}, inplace=True)\n", + "\n", + "# Merge with temperature data\n", + "print(\"Merging with temperature data...\")\n", + "# Create an hour column in the merged data for joining\n", + "merged_df['hour'] = merged_df['DATETIME'].dt.floor('H')\n", + "full_df = pd.merge(\n", + " merged_df,\n", + " hourly_temp,\n", + " on='hour',\n", + " how='left'\n", + ")\n", + "print(f\"Final merged dataset: {len(full_df)} rows\")\n", + "\n", + "# If no temperature data was merged, try a different approach\n", + "if full_df['TEMPERATURE'].isna().all():\n", + " print(\"\\nDEBUG: Temperature merge failed - trying date-based match\")\n", + "\n", + " # Create date columns\n", + " merged_df['DATE'] = merged_df['DATETIME'].dt.date\n", + " df_temperature['DATE'] = df_temperature['DATETIME'].dt.date\n", + "\n", + " # Aggregate temperature by date\n", + " daily_temp = df_temperature.groupby('DATE')[temp_column].mean().reset_index()\n", + " daily_temp.rename(columns={temp_column: 'TEMPERATURE'}, inplace=True)\n", + "\n", + " # Merge on date\n", + " full_df = pd.merge(\n", + " merged_df,\n", + " daily_temp,\n", + " on='DATE',\n", + " how='left'\n", + " )\n", + " print(f\"Date-based temperature merge: {len(full_df)} rows with non-null temperature: {full_df['TEMPERATURE'].notna().sum()}\")\n", + "\n", + "# Calculate error metrics\n", + "full_df['ABS_ERROR'] = abs(full_df['FORECASTDEMAND'] - full_df['TOTALDEMAND'])\n", + "full_df['PERC_ERROR'] = 100 * full_df['ABS_ERROR'] / full_df['TOTALDEMAND']\n", + "\n", + "# Overall accuracy metrics\n", + "mae = full_df['ABS_ERROR'].mean()\n", + "mape = full_df['PERC_ERROR'].mean()\n", + "rmse = np.sqrt((full_df['FORECASTDEMAND'] - full_df['TOTALDEMAND']).pow(2).mean())\n", + "\n", + "# Calculate R-squared (coefficient of determination)\n", + "ss_total = ((full_df['TOTALDEMAND'] - full_df['TOTALDEMAND'].mean()) ** 2).sum()\n", + "ss_residual = ((full_df['TOTALDEMAND'] - full_df['FORECASTDEMAND']) ** 2).sum()\n", + "r_squared = 1 - (ss_residual / ss_total)\n", + "\n", + "print(\"\\nOverall Forecast Accuracy Metrics for PERIODID 24:\")\n", + "print(f\"Mean Absolute Error (MAE): {mae:.2f} MW\")\n", + "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")\n", + "print(f\"Root Mean Square Error (RMSE): {rmse:.2f} MW\")\n", + "print(f\"R-squared (R²): {r_squared:.4f}\")\n", + "\n", + "# Save the forecast vs actual data regardless of temperature\n", + "full_df[['DATETIME', 'REGIONID', 'FORECASTDEMAND', 'TOTALDEMAND', 'ABS_ERROR', 'PERC_ERROR']].to_csv(\n", + " 'forecast_vs_actual_periodid24.csv', index=False\n", + ")\n", + "\n", + "# Check if we have temperature data before continuing with temperature analysis\n", + "if full_df['TEMPERATURE'].notna().sum() > 0:\n", + " # Analyze impact of temperature on forecast accuracy\n", + " print(\"\\nAnalyzing temperature impact on forecast accuracy...\")\n", + "\n", + " # Remove rows with missing temperature values\n", + " full_df_with_temp = full_df.dropna(subset=['TEMPERATURE'])\n", + " print(f\"Rows with temperature data: {len(full_df_with_temp)} out of {len(full_df)} total rows\")\n", + "\n", + " # Create temperature bins with equal sample sizes\n", + " # You can change this number to adjust how many temperature ranges to create\n", + " num_temp_ranges = 10\n", + "\n", + " # Calculate quantiles for equal sample size bins\n", + " quantiles = np.linspace(0, 1, num_temp_ranges + 1)\n", + " temp_quantile_values = [full_df_with_temp['TEMPERATURE'].quantile(q) for q in quantiles]\n", + "\n", + " # Create labels for these ranges\n", + " temp_labels = [f'{temp_quantile_values[i]:.1f}-{temp_quantile_values[i+1]:.1f}°C'\n", + " for i in range(num_temp_ranges)]\n", + "\n", + " # Create the temperature ranges with equal sample sizes\n", + " full_df_with_temp['TEMP_RANGE'] = pd.cut(\n", + " full_df_with_temp['TEMPERATURE'],\n", + " bins=temp_quantile_values,\n", + " labels=temp_labels,\n", + " include_lowest=True\n", + " )\n", + "\n", + " # Print information about the created temperature ranges\n", + " print(f\"\\nCreated {num_temp_ranges} temperature ranges with approximately equal sample sizes:\")\n", + " temp_range_counts = full_df_with_temp['TEMP_RANGE'].value_counts().sort_index()\n", + " for range_name, count in temp_range_counts.items():\n", + " print(f\" {range_name}: {count} samples\")\n", + "\n", + " # Function to calculate R-squared for a group\n", + " def calculate_r_squared(group):\n", + " if len(group) < 3:\n", + " return np.nan\n", + " ss_total = ((group['TOTALDEMAND'] - group['TOTALDEMAND'].mean()) ** 2).sum()\n", + " if ss_total == 0:\n", + " return np.nan\n", + " ss_residual = ((group['TOTALDEMAND'] - group['FORECASTDEMAND']) ** 2).sum()\n", + " return 1 - (ss_residual / ss_total)\n", + "\n", + " # Group by temperature range and calculate accuracy metrics\n", + " accuracy_by_temp = full_df_with_temp.groupby('TEMP_RANGE').agg({\n", + " 'ABS_ERROR': 'mean',\n", + " 'PERC_ERROR': 'mean',\n", + " 'TEMPERATURE': 'count'\n", + " }).rename(columns={'ABS_ERROR': 'MAE', 'PERC_ERROR': 'MAPE', 'TEMPERATURE': 'Count'})\n", + "\n", + " # Calculate R-squared for each temperature range\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n", + " accuracy_by_temp['R-squared'] = r_squared_by_temp\n", + "\n", + " print(\"\\nForecast Accuracy by Temperature Range:\")\n", + " print(accuracy_by_temp)\n", + "\n", + " # Non-linear (polynomial) model with both temperature and forecast as predictors\n", + " print(\"\\nFitting nonlinear model with temperature and forecast as predictors...\")\n", + "\n", + " # Create polynomial features for temperature (to capture U-shape)\n", + " from sklearn.preprocessing import PolynomialFeatures\n", + " from sklearn.linear_model import LinearRegression\n", + " from sklearn.metrics import r2_score\n", + "\n", + " # Prepare the data\n", + " X = full_df_with_temp[['TEMPERATURE', 'FORECASTDEMAND']]\n", + " y = full_df_with_temp['TOTALDEMAND']\n", + "\n", + " # Create polynomial features for temperature (degree 2 for U-shape)\n", + " poly = PolynomialFeatures(degree=2, include_bias=False)\n", + " X_temp_poly = poly.fit_transform(full_df_with_temp[['TEMPERATURE']])\n", + "\n", + " # Combine polynomial temperature features with forecast\n", + " X_combined = np.column_stack((X_temp_poly, full_df_with_temp['FORECASTDEMAND']))\n", + "\n", + " # Fit the model\n", + " model = LinearRegression()\n", + " model.fit(X_combined, y)\n", + "\n", + " # Make predictions\n", + " y_pred = model.predict(X_combined)\n", + "\n", + " # Calculate R-squared\n", + " r2_combined = r2_score(y, y_pred)\n", + "\n", + " print(f\"\\nNonlinear Model Results (Polynomial Temperature + Forecast):\")\n", + " print(f\"R-squared: {r2_combined:.4f}\")\n", + " print(f\"Coefficients:\")\n", + "\n", + " # Get feature names\n", + " feature_names = [f'Temperature', f'Temperature²', 'Forecast']\n", + " for name, coef in zip(feature_names, model.coef_):\n", + " print(f\" {name}: {coef:.6f}\")\n", + " print(f\" Intercept: {model.intercept_:.6f}\")\n", + "\n", + " # Individual R-squared values for comparison\n", + " # 1. Temperature only (linear)\n", + " model_temp_linear = LinearRegression()\n", + " model_temp_linear.fit(full_df_with_temp[['TEMPERATURE']], y)\n", + " r2_temp_linear = r2_score(y, model_temp_linear.predict(full_df_with_temp[['TEMPERATURE']]))\n", + "\n", + " # 2. Temperature only (polynomial)\n", + " model_temp_poly = LinearRegression()\n", + " model_temp_poly.fit(X_temp_poly, y)\n", + " r2_temp_poly = r2_score(y, model_temp_poly.predict(X_temp_poly))\n", + "\n", + " # 3. Forecast only\n", + " model_forecast = LinearRegression()\n", + " model_forecast.fit(full_df_with_temp[['FORECASTDEMAND']], y)\n", + " r2_forecast = r2_score(y, model_forecast.predict(full_df_with_temp[['FORECASTDEMAND']]))\n", + "\n", + " print(\"\\nR-squared Comparison:\")\n", + " print(f\" Temperature Only (Linear): {r2_temp_linear:.4f}\")\n", + " print(f\" Temperature Only (Polynomial): {r2_temp_poly:.4f}\")\n", + " print(f\" Forecast Only: {r2_forecast:.4f}\")\n", + " print(f\" Combined Model: {r2_combined:.4f}\")\n", + "\n", + " # Visualize the relationship between temperature, forecast and actual demand\n", + " print(\"\\nCreating 3D visualization of Temperature, Forecast, and Actual Demand...\")\n", + "\n", + " # If we have many data points, sample to make the plot clearer\n", + " plot_data = full_df_with_temp\n", + " if len(full_df_with_temp) > 1000:\n", + " plot_data = full_df_with_temp.sample(1000, random_state=42)\n", + "\n", + " # Create a 3D scatter plot\n", + " from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + " fig = plt.figure(figsize=(12, 10))\n", + " ax = fig.add_subplot(111, projection='3d')\n", + "\n", + " scatter = ax.scatter(\n", + " plot_data['TEMPERATURE'],\n", + " plot_data['FORECASTDEMAND'],\n", + " plot_data['TOTALDEMAND'],\n", + " c=plot_data['TOTALDEMAND'],\n", + " cmap='viridis',\n", + " s=50,\n", + " alpha=0.6\n", + " )\n", + "\n", + " ax.set_xlabel('Temperature (°C)')\n", + " ax.set_ylabel('Forecast Demand (MW)')\n", + " ax.set_zlabel('Actual Demand (MW)')\n", + " ax.set_title(f'3D Relationship: Temperature, Forecast and Actual Demand (PERIODID {target_periodid})')\n", + "\n", + " # Add a color bar\n", + " cbar = fig.colorbar(scatter, ax=ax, label='Actual Demand (MW)')\n", + "\n", + " # Save the 3D plot\n", + " plt.tight_layout()\n", + " plt.savefig('3d_relationship_temp_forecast_actual for temp U.png')\n", + " plt.close()\n", + "\n", + " # Save temperature analysis to CSV\n", + " full_df_with_temp.to_csv('forecast_vs_actual_with_temp_periodid24 for temp U.csv', index=False)\n", + " accuracy_by_temp.reset_index().to_csv('accuracy_by_temperature_periodid24 for tempU.csv', index=False)\n", + "\n", + " # Create visualizations\n", + " print(\"\\nCreating visualizations...\")\n", + "\n", + " # 1. Scatter plot of Forecasted vs Actual Demand colored by temperature\n", + " plt.figure(figsize=(10, 8))\n", + " scatter = plt.scatter(\n", + " full_df_with_temp['TOTALDEMAND'],\n", + " full_df_with_temp['FORECASTDEMAND'],\n", + " c=full_df_with_temp['TEMPERATURE'],\n", + " cmap='coolwarm',\n", + " alpha=0.7\n", + " )\n", + " plt.colorbar(scatter, label='Temperature (°C)')\n", + " plt.plot([full_df_with_temp['TOTALDEMAND'].min(), full_df_with_temp['TOTALDEMAND'].max()],\n", + " [full_df_with_temp['TOTALDEMAND'].min(), full_df_with_temp['TOTALDEMAND'].max()],\n", + " 'k--', label='Perfect Forecast')\n", + " plt.title(f'Forecast vs Actual Demand (PERIODID {target_periodid})')\n", + " plt.xlabel('Actual Demand (MW)')\n", + " plt.ylabel('Forecasted Demand (MW)')\n", + " plt.legend()\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('forecast_vs_actual_periodid24.png')\n", + " plt.close()\n", + "\n", + " # 2. Error distribution by temperature range\n", + " plt.figure(figsize=(12, 8))\n", + "\n", + " # Create boxplot manually to ensure correct ordering\n", + " ranges = full_df_with_temp['TEMP_RANGE'].unique().tolist()\n", + " ranges.sort() # Sort the ranges\n", + "\n", + " # Collect data for each range\n", + " box_data = [full_df_with_temp[full_df_with_temp['TEMP_RANGE'] == temp_range]['PERC_ERROR'].values\n", + " for temp_range in ranges]\n", + "\n", + " # Create the boxplot\n", + " plt.boxplot(box_data, labels=ranges)\n", + "\n", + " plt.title(f'Forecast Error Distribution by Temperature Range (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature Range')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.xticks(rotation=45)\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('error_by_temperature_periodid24 fortemp U.png')\n", + " plt.close()\n", + "\n", + " # 3. Correlation between temperature and forecast error\n", + " if len(full_df_with_temp) > 10:\n", + " plt.figure(figsize=(10, 6))\n", + " plt.scatter(full_df_with_temp['TEMPERATURE'], full_df_with_temp['PERC_ERROR'], alpha=0.5)\n", + " plt.title(f'Temperature vs Forecast Error (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature (°C)')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.grid(True, alpha=0.3)\n", + "\n", + " # Add trend line\n", + " if len(full_df_with_temp) > 2: # Need at least 3 points for a trend line\n", + " z = np.polyfit(full_df_with_temp['TEMPERATURE'], full_df_with_temp['PERC_ERROR'], 1)\n", + " p = np.poly1d(z)\n", + " temp_range = np.linspace(full_df_with_temp['TEMPERATURE'].min(), full_df_with_temp['TEMPERATURE'].max(), 100)\n", + " plt.plot(temp_range, p(temp_range), \"r--\", linewidth=2)\n", + "\n", + " corr = full_df_with_temp['TEMPERATURE'].corr(full_df_with_temp['PERC_ERROR'])\n", + " plt.annotate(f'Correlation: {corr:.4f}',\n", + " xy=(0.05, 0.95),\n", + " xycoords='axes fraction',\n", + " bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\", ec=\"gray\", alpha=0.8))\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('temperature_vs_error_periodid24 forTemp U.png')\n", + " plt.close()\n", + "\n", + " # 4. R-squared by temperature range\n", + " if 'R-squared' in accuracy_by_temp.columns and not accuracy_by_temp['R-squared'].isna().all():\n", + " plt.figure(figsize=(12, 6))\n", + "\n", + " # Convert index to list for proper ordering in the plot\n", + " ranges = accuracy_by_temp.index.tolist()\n", + "\n", + " # Plot the bar chart\n", + " plt.bar(range(len(ranges)), accuracy_by_temp['R-squared'])\n", + "\n", + " # Set the tick positions and labels\n", + " plt.xticks(range(len(ranges)), ranges, rotation=45)\n", + "\n", + " plt.title(f'R-squared by Temperature Range (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature Range')\n", + " plt.ylabel('R-squared (R²)')\n", + " plt.ylim(0, 1) # R-squared is typically between 0 and 1\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('r_squared_by_temperature_periodid24 fortemp U.png')\n", + " plt.close()\n", + "else:\n", + " print(\"\\nNo temperature data was successfully matched with forecast/demand data.\")\n", + " print(\"Skipping temperature-related analysis.\")\n", + "\n", + "# Always create the time series visualization for forecast vs actual\n", + "# Get a sample period for better visualization (most recent 14 days)\n", + "full_df_sorted = full_df.sort_values('DATETIME')\n", + "sample_period = full_df_sorted.tail(min(24*14, len(full_df_sorted)))\n", + "\n", + "plt.figure(figsize=(14, 7))\n", + "plt.plot(sample_period['DATETIME'], sample_period['TOTALDEMAND'], 'b-', label='Actual Demand')\n", + "plt.plot(sample_period['DATETIME'], sample_period['FORECASTDEMAND'], 'r--', label='Forecast Demand')\n", + "plt.title(f'Forecast vs Actual Demand Over Time (PERIODID {target_periodid})')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Demand (MW)')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('time_series_forecast_actual_periodid24 fortempU.png')\n", + "plt.close()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/R squared including forecast and temp.ipynb b/src/R squared including forecast and temp.ipynb new file mode 100644 index 000000000..3bf6fb03d --- /dev/null +++ b/src/R squared including forecast and temp.ipynb @@ -0,0 +1,656 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-04-05T02:53:13.683947500Z", + "start_time": "2025-04-05T02:52:55.477025600Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading datasets...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:20: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_temperature = pd.read_csv(temperature_path)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded forecast data: 10906019 rows\n", + "Loaded demand data: 196513 rows\n", + "Loaded temperature data: 220326 rows\n", + "\n", + "Sample forecast datetime: 2010-01-01 00:00:00\n", + "Sample demand datetime: 1/01/2010 0:00\n", + "Sample temperature DATETIME: 1/01/2010 0:00\n", + "Filtered forecast data for PERIODID 24: 196505 rows\n", + "Forecast datetime format appears to be: ISO\n", + "Demand datetime format appears to be: Australian\n", + "Parsing dates...\n", + "\n", + "After parsing:\n", + "Sample forecast datetime: 2010-01-01 00:00:00\n", + "Sample demand datetime: 2010-01-01 00:00:00\n", + "Sample temperature datetime: 2010-01-01 00:00:00\n", + "Using temperature column: TEMPERATURE\n", + "Merging forecast with demand data...\n", + "Merged forecast and demand: 196505 rows\n", + "Aggregating temperature data by hour...\n", + "Merging with temperature data...\n", + "Final merged dataset: 196505 rows\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:138: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " df_temperature['hour'] = df_temperature['DATETIME'].dt.floor('H')\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:145: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " merged_df['hour'] = merged_df['DATETIME'].dt.floor('H')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Overall Forecast Accuracy Metrics for PERIODID 24:\n", + "Mean Absolute Error (MAE): 169.09 MW\n", + "Mean Absolute Percentage Error (MAPE): 2.05%\n", + "Root Mean Square Error (RMSE): 234.87 MW\n", + "R-squared (R²): 0.9673\n", + "\n", + "Analyzing temperature impact on forecast accuracy...\n", + "Rows with temperature data: 196141 out of 196505 total rows\n", + "\n", + "Using specified temperature ranges:\n", + " <= 0: 16 samples\n", + " 0-5: 2710 samples\n", + " 5-10: 19550 samples\n", + " 10-15: 42344 samples\n", + " 15-20: 62603 samples\n", + " 20-25: 51527 samples\n", + " 25-30: 14269 samples\n", + " 30-35: 2568 samples\n", + " 35-40: 484 samples\n", + " > 40: 70 samples\n", + "\n", + "Forecast Accuracy by Temperature Range:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:214: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " full_df_with_temp['TEMP_RANGE'] = pd.cut(\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:238: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " accuracy_by_temp = full_df_with_temp.groupby('TEMP_RANGE').agg({\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:245: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_17264\\1254910683.py:245: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n" + ] + }, + { + "data": { + "text/plain": " MAE MAPE Count R-squared\nTEMP_RANGE \n<= 0 162.318750 2.090544 16 0.960227\n0-5 132.277295 1.653575 2710 0.975757\n5-10 137.099084 1.678702 19550 0.983940\n10-15 160.561842 1.919153 42344 0.977542\n15-20 150.157787 1.936071 62603 0.963830\n20-25 175.763657 2.162140 51527 0.941278\n25-30 261.557903 2.840032 14269 0.930041\n30-35 348.152960 3.455496 2568 0.912743\n35-40 441.745868 3.869580 484 0.810509\n> 40 475.275714 3.866062 70 0.807524", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
MAEMAPECountR-squared
TEMP_RANGE
<= 0162.3187502.090544160.960227
0-5132.2772951.65357527100.975757
5-10137.0990841.678702195500.983940
10-15160.5618421.919153423440.977542
15-20150.1577871.936071626030.963830
20-25175.7636572.162140515270.941278
25-30261.5579032.840032142690.930041
30-35348.1529603.45549625680.912743
35-40441.7458683.8695804840.810509
> 40475.2757143.866062700.807524
\n
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fitting nonlinear model with temperature and forecast as predictors...\n", + "\n", + "Nonlinear Model Results (Polynomial Temperature + Forecast):\n", + "R-squared: 0.9687\n", + "Coefficients:\n", + " Temperature: -25.346849\n", + " Temperature²: 0.769912\n", + " Forecast: 0.969277\n", + " Intercept: 438.465014\n", + "\n", + "R-squared Comparison:\n", + " Temperature Only (Linear): 0.0222\n", + " Temperature Only (Polynomial): 0.0971\n", + " Forecast Only: 0.9679\n", + " Combined Model: 0.9687\n", + "\n", + "Creating 3D visualization of Temperature, Forecast, and Actual Demand...\n" + ] + }, + { + "ename": "PermissionError", + "evalue": "[Errno 13] Permission denied: 'accuracy_by_temperature_periodid24 for temp U o bin .csv'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mPermissionError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[4], line 351\u001B[0m\n\u001B[0;32m 349\u001B[0m \u001B[38;5;66;03m# Save temperature analysis to CSV\u001B[39;00m\n\u001B[0;32m 350\u001B[0m full_df_with_temp\u001B[38;5;241m.\u001B[39mto_csv(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mforecast_vs_actual_with_temp_periodid24 for temp u o bin .csv\u001B[39m\u001B[38;5;124m'\u001B[39m, index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m--> 351\u001B[0m \u001B[43maccuracy_by_temp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreset_index\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43maccuracy_by_temperature_periodid24 for temp U o bin .csv\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 353\u001B[0m \u001B[38;5;66;03m# Create visualizations\u001B[39;00m\n\u001B[0;32m 354\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124mCreating visualizations...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "File \u001B[1;32m~\\.conda\\envs\\pythonProject7\\lib\\site-packages\\pandas\\util\\_decorators.py:333\u001B[0m, in \u001B[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 327\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m num_allow_args:\n\u001B[0;32m 328\u001B[0m warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m 329\u001B[0m msg\u001B[38;5;241m.\u001B[39mformat(arguments\u001B[38;5;241m=\u001B[39m_format_argument_list(allow_args)),\n\u001B[0;32m 330\u001B[0m \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[0;32m 331\u001B[0m stacklevel\u001B[38;5;241m=\u001B[39mfind_stack_level(),\n\u001B[0;32m 332\u001B[0m )\n\u001B[1;32m--> 333\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", + "File \u001B[1;32m~\\.conda\\envs\\pythonProject7\\lib\\site-packages\\pandas\\core\\generic.py:3967\u001B[0m, in \u001B[0;36mNDFrame.to_csv\u001B[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001B[0m\n\u001B[0;32m 3956\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m, ABCDataFrame) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mto_frame()\n\u001B[0;32m 3958\u001B[0m formatter \u001B[38;5;241m=\u001B[39m DataFrameFormatter(\n\u001B[0;32m 3959\u001B[0m frame\u001B[38;5;241m=\u001B[39mdf,\n\u001B[0;32m 3960\u001B[0m header\u001B[38;5;241m=\u001B[39mheader,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 3964\u001B[0m decimal\u001B[38;5;241m=\u001B[39mdecimal,\n\u001B[0;32m 3965\u001B[0m )\n\u001B[1;32m-> 3967\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mDataFrameRenderer\u001B[49m\u001B[43m(\u001B[49m\u001B[43mformatter\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_csv\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 3968\u001B[0m \u001B[43m \u001B[49m\u001B[43mpath_or_buf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3969\u001B[0m \u001B[43m \u001B[49m\u001B[43mlineterminator\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlineterminator\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3970\u001B[0m \u001B[43m \u001B[49m\u001B[43msep\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msep\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3971\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3972\u001B[0m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3973\u001B[0m \u001B[43m \u001B[49m\u001B[43mcompression\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcompression\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3974\u001B[0m \u001B[43m \u001B[49m\u001B[43mquoting\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquoting\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3975\u001B[0m \u001B[43m \u001B[49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3976\u001B[0m \u001B[43m \u001B[49m\u001B[43mindex_label\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mindex_label\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3977\u001B[0m \u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3978\u001B[0m \u001B[43m \u001B[49m\u001B[43mchunksize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchunksize\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3979\u001B[0m \u001B[43m \u001B[49m\u001B[43mquotechar\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mquotechar\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3980\u001B[0m \u001B[43m \u001B[49m\u001B[43mdate_format\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdate_format\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3981\u001B[0m \u001B[43m \u001B[49m\u001B[43mdoublequote\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdoublequote\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3982\u001B[0m \u001B[43m \u001B[49m\u001B[43mescapechar\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mescapechar\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3983\u001B[0m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 3984\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\.conda\\envs\\pythonProject7\\lib\\site-packages\\pandas\\io\\formats\\format.py:1014\u001B[0m, in \u001B[0;36mDataFrameRenderer.to_csv\u001B[1;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001B[0m\n\u001B[0;32m 993\u001B[0m created_buffer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m 995\u001B[0m csv_formatter \u001B[38;5;241m=\u001B[39m CSVFormatter(\n\u001B[0;32m 996\u001B[0m path_or_buf\u001B[38;5;241m=\u001B[39mpath_or_buf,\n\u001B[0;32m 997\u001B[0m lineterminator\u001B[38;5;241m=\u001B[39mlineterminator,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 1012\u001B[0m formatter\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfmt,\n\u001B[0;32m 1013\u001B[0m )\n\u001B[1;32m-> 1014\u001B[0m \u001B[43mcsv_formatter\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msave\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1016\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m created_buffer:\n\u001B[0;32m 1017\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(path_or_buf, StringIO)\n", + "File \u001B[1;32m~\\.conda\\envs\\pythonProject7\\lib\\site-packages\\pandas\\io\\formats\\csvs.py:251\u001B[0m, in \u001B[0;36mCSVFormatter.save\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 247\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 248\u001B[0m \u001B[38;5;124;03mCreate the writer & save.\u001B[39;00m\n\u001B[0;32m 249\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 250\u001B[0m \u001B[38;5;66;03m# apply compression and byte/text conversion\u001B[39;00m\n\u001B[1;32m--> 251\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 252\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 253\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 254\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 255\u001B[0m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 256\u001B[0m \u001B[43m \u001B[49m\u001B[43mcompression\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompression\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 257\u001B[0m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 258\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m handles:\n\u001B[0;32m 259\u001B[0m \u001B[38;5;66;03m# Note: self.encoding is irrelevant here\u001B[39;00m\n\u001B[0;32m 260\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mwriter \u001B[38;5;241m=\u001B[39m csvlib\u001B[38;5;241m.\u001B[39mwriter(\n\u001B[0;32m 261\u001B[0m handles\u001B[38;5;241m.\u001B[39mhandle,\n\u001B[0;32m 262\u001B[0m lineterminator\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlineterminator,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 267\u001B[0m quotechar\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mquotechar,\n\u001B[0;32m 268\u001B[0m )\n\u001B[0;32m 270\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_save()\n", + "File \u001B[1;32m~\\.conda\\envs\\pythonProject7\\lib\\site-packages\\pandas\\io\\common.py:873\u001B[0m, in \u001B[0;36mget_handle\u001B[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[0m\n\u001B[0;32m 868\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(handle, \u001B[38;5;28mstr\u001B[39m):\n\u001B[0;32m 869\u001B[0m \u001B[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001B[39;00m\n\u001B[0;32m 870\u001B[0m \u001B[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001B[39;00m\n\u001B[0;32m 871\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mencoding \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mmode:\n\u001B[0;32m 872\u001B[0m \u001B[38;5;66;03m# Encoding\u001B[39;00m\n\u001B[1;32m--> 873\u001B[0m handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[0;32m 874\u001B[0m \u001B[43m \u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 875\u001B[0m \u001B[43m \u001B[49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 876\u001B[0m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 877\u001B[0m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 878\u001B[0m \u001B[43m \u001B[49m\u001B[43mnewline\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m 879\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 880\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 881\u001B[0m \u001B[38;5;66;03m# Binary mode\u001B[39;00m\n\u001B[0;32m 882\u001B[0m handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mopen\u001B[39m(handle, ioargs\u001B[38;5;241m.\u001B[39mmode)\n", + "\u001B[1;31mPermissionError\u001B[0m: [Errno 13] Permission denied: 'accuracy_by_temperature_periodid24 for temp U o bin .csv'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import datetime\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score\n", + "\n", + "# File paths (Hey team please change to your local path 1,2 3)\n", + "forecast_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\forecastdemand_nsw.csv\" #Change path\n", + "demand_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\totaldemand_nsw.csv\" # Change path\n", + "temperature_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\temperature_nsw.csv\" #Change path\n", + "target_periodid = 24\n", + "\n", + "# Load datasets\n", + "\n", + "df_forecast = pd.read_csv(forecast_path)\n", + "df_demand = pd.read_csv(demand_path)\n", + "df_temperature = pd.read_csv(temperature_path)\n", + "\n", + "print(f\"Loaded forecast data: {len(df_forecast)} rows\")\n", + "print(f\"Loaded demand data: {len(df_demand)} rows\")\n", + "print(f\"Loaded temperature data: {len(df_temperature)} rows\")\n", + "\n", + "# Display samples to check datetime formats\n", + "print(\"\\nSample forecast datetime:\", df_forecast['DATETIME'].iloc[0] if len(df_forecast) > 0 else \"No data\")\n", + "print(\"Sample demand datetime:\", df_demand['DATETIME'].iloc[0] if len(df_demand) > 0 else \"No data\")\n", + "temp_dt_col = 'date_time' if 'date_time' in df_temperature.columns else 'DATETIME'\n", + "print(f\"Sample temperature {temp_dt_col}:\", df_temperature[temp_dt_col].iloc[0] if len(df_temperature) > 0 else \"No data\")\n", + "\n", + "# Filter forecast data for PERIODID 24\n", + "df_forecast = df_forecast[df_forecast['PERIODID'] == target_periodid]\n", + "print(f\"Filtered forecast data for PERIODID {target_periodid}: {len(df_forecast)} rows\")\n", + "\n", + "# Check if the forecast and demand datetime are already in ISO format (YYYY-MM-DD)\n", + "forecast_date_format = \"ISO\" if '-' in str(df_forecast['DATETIME'].iloc[0]) else \"Australian\"\n", + "demand_date_format = \"ISO\" if '-' in str(df_demand['DATETIME'].iloc[0]) else \"Australian\"\n", + "print(f\"Forecast datetime format appears to be: {forecast_date_format}\")\n", + "print(f\"Demand datetime format appears to be: {demand_date_format}\")\n", + "\n", + "# Parse dates - with appropriate handling for existing formats\n", + "print(\"Parsing dates...\")\n", + "\n", + "# For forecast data\n", + "if forecast_date_format == \"ISO\":\n", + " # Already in ISO format, just parse\n", + " df_forecast['DATETIME'] = pd.to_datetime(df_forecast['DATETIME'], errors='coerce')\n", + "else:\n", + " # Australian format, convert to ISO\n", + " df_forecast['DATETIME'] = pd.to_datetime(df_forecast['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# For demand data\n", + "if demand_date_format == \"ISO\":\n", + " # Already in ISO format, just parse\n", + " df_demand['DATETIME'] = pd.to_datetime(df_demand['DATETIME'], errors='coerce')\n", + "else:\n", + " # Australian format, convert to ISO\n", + " df_demand['DATETIME'] = pd.to_datetime(df_demand['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# For temperature data\n", + "if 'date_time' in df_temperature.columns:\n", + " # First convert from Australian format to datetime\n", + " df_temperature['date_time'] = pd.to_datetime(df_temperature['date_time'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + " # Then create a DATETIME column in the same format as forecast/demand\n", + " df_temperature['DATETIME'] = df_temperature['date_time']\n", + "else:\n", + " # Directly parse the DATETIME column\n", + " df_temperature['DATETIME'] = pd.to_datetime(df_temperature['DATETIME'], format=\"%d/%m/%Y %H:%M\", errors='coerce')\n", + "\n", + "# Drop rows with invalid dates\n", + "df_forecast = df_forecast.dropna(subset=['DATETIME'])\n", + "df_demand = df_demand.dropna(subset=['DATETIME'])\n", + "df_temperature = df_temperature.dropna(subset=['DATETIME'])\n", + "\n", + "# Print sample of dates to verify format consistency\n", + "print(\"\\nAfter parsing:\")\n", + "print(\"Sample forecast datetime:\", df_forecast['DATETIME'].iloc[0] if len(df_forecast) > 0 else \"No data\")\n", + "print(\"Sample demand datetime:\", df_demand['DATETIME'].iloc[0] if len(df_demand) > 0 else \"No data\")\n", + "print(\"Sample temperature datetime:\", df_temperature['DATETIME'].iloc[0] if len(df_temperature) > 0 else \"No data\")\n", + "\n", + "# Identify temperature column\n", + "temp_column = 'temperature' if 'temperature' in df_temperature.columns else 'TEMPERATURE'\n", + "print(f\"Using temperature column: {temp_column}\")\n", + "\n", + "# Merge forecast and demand data\n", + "print(\"Merging forecast with demand data...\")\n", + "merged_df = pd.merge(\n", + " df_forecast,\n", + " df_demand[['DATETIME', 'TOTALDEMAND', 'REGIONID']],\n", + " on=['DATETIME', 'REGIONID'],\n", + " how='inner'\n", + ")\n", + "print(f\"Merged forecast and demand: {len(merged_df)} rows\")\n", + "\n", + "# If merge failed, debug by examining values more closely\n", + "if len(merged_df) == 0:\n", + " print(\"\\nDEBUG: Merge failed - examining DATETIME values\")\n", + "\n", + " # Convert all to strings in ISO format for comparison\n", + " df_forecast['DATETIME_STR'] = df_forecast['DATETIME'].dt.strftime('%Y-%m-%d %H:%M:%S')\n", + " df_demand['DATETIME_STR'] = df_demand['DATETIME'].dt.strftime('%Y-%m-%d %H:%M:%S')\n", + "\n", + " # Print some samples for comparison\n", + " print(\"\\nForecast DATETIME samples:\")\n", + " print(df_forecast['DATETIME_STR'].head(5).tolist())\n", + " print(\"\\nDemand DATETIME samples:\")\n", + " print(df_demand['DATETIME_STR'].head(5).tolist())\n", + "\n", + " # Check if there are any exact matches\n", + " forecast_set = set(df_forecast['DATETIME_STR'].tolist())\n", + " demand_set = set(df_demand['DATETIME_STR'].tolist())\n", + " common = forecast_set.intersection(demand_set)\n", + " print(f\"\\nNumber of common datetime values: {len(common)}\")\n", + "\n", + " # Try a more flexible merge on date only\n", + " print(\"\\nTrying a more flexible merge on date only...\")\n", + " df_forecast['DATE'] = df_forecast['DATETIME'].dt.date\n", + " df_demand['DATE'] = df_demand['DATETIME'].dt.date\n", + "\n", + " date_merged = pd.merge(\n", + " df_forecast,\n", + " df_demand[['DATE', 'TOTALDEMAND', 'REGIONID']],\n", + " on=['DATE', 'REGIONID'],\n", + " how='inner'\n", + " )\n", + " print(f\"Date-only merge produced {len(date_merged)} rows\")\n", + "\n", + " if len(date_merged) > 0:\n", + " merged_df = date_merged\n", + " print(\"Using date-only merge for analysis\")\n", + " else:\n", + " print(\"Analysis cannot continue without matching data\")\n", + " exit(1)\n", + "\n", + "# Aggregate temperature by hour to handle multiple readings per hour\n", + "print(\"Aggregating temperature data by hour...\")\n", + "df_temperature['hour'] = df_temperature['DATETIME'].dt.floor('H')\n", + "hourly_temp = df_temperature.groupby('hour')[temp_column].mean().reset_index()\n", + "hourly_temp.rename(columns={temp_column: 'TEMPERATURE'}, inplace=True)\n", + "\n", + "# Merge with temperature data\n", + "print(\"Merging with temperature data...\")\n", + "# Create an hour column in the merged data for joining data\n", + "merged_df['hour'] = merged_df['DATETIME'].dt.floor('H')\n", + "full_df = pd.merge(\n", + " merged_df,\n", + " hourly_temp,\n", + " on='hour',\n", + " how='left'\n", + ")\n", + "print(f\"Final merged dataset: {len(full_df)} rows\")\n", + "\n", + "# If no temperature data was merged, try a different approach\n", + "if full_df['TEMPERATURE'].isna().all():\n", + " print(\"\\nDEBUG: Temperature merge failed - trying date-based match\")\n", + "\n", + " # Create date columns\n", + " merged_df['DATE'] = merged_df['DATETIME'].dt.date\n", + " df_temperature['DATE'] = df_temperature['DATETIME'].dt.date\n", + "\n", + " # Aggregate temperature by date\n", + " daily_temp = df_temperature.groupby('DATE')[temp_column].mean().reset_index()\n", + " daily_temp.rename(columns={temp_column: 'TEMPERATURE'}, inplace=True)\n", + "\n", + " # Merge on date\n", + " full_df = pd.merge(\n", + " merged_df,\n", + " daily_temp,\n", + " on='DATE',\n", + " how='left'\n", + " )\n", + " print(f\"Date-based temperature merge: {len(full_df)} rows with non-null temperature: {full_df['TEMPERATURE'].notna().sum()}\")\n", + "\n", + "# Calculate error metrics\n", + "full_df['ABS_ERROR'] = abs(full_df['FORECASTDEMAND'] - full_df['TOTALDEMAND'])\n", + "full_df['PERC_ERROR'] = 100 * full_df['ABS_ERROR'] / full_df['TOTALDEMAND']\n", + "\n", + "# Overall accuracy metrics\n", + "mae = full_df['ABS_ERROR'].mean()\n", + "mape = full_df['PERC_ERROR'].mean()\n", + "rmse = np.sqrt((full_df['FORECASTDEMAND'] - full_df['TOTALDEMAND']).pow(2).mean())\n", + "\n", + "# Calculate R-squared (coefficient of determination)\n", + "ss_total = ((full_df['TOTALDEMAND'] - full_df['TOTALDEMAND'].mean()) ** 2).sum()\n", + "ss_residual = ((full_df['TOTALDEMAND'] - full_df['FORECASTDEMAND']) ** 2).sum()\n", + "r_squared = 1 - (ss_residual / ss_total)\n", + "\n", + "print(\"\\nOverall Forecast Accuracy Metrics for PERIODID 24:\")\n", + "print(f\"Mean Absolute Error (MAE): {mae:.2f} MW\")\n", + "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")\n", + "print(f\"Root Mean Square Error (RMSE): {rmse:.2f} MW\")\n", + "print(f\"R-squared (R²): {r_squared:.4f}\")\n", + "\n", + "# Save the forecast vs actual data regardless of temperature\n", + "full_df[['DATETIME', 'REGIONID', 'FORECASTDEMAND', 'TOTALDEMAND', 'ABS_ERROR', 'PERC_ERROR']].to_csv(\n", + " 'forecast_vs_actual_periodid24.csv', index=False\n", + ")\n", + "\n", + "# Check if we have temperature data before continuing with temperature analysis\n", + "if full_df['TEMPERATURE'].notna().sum() > 0:\n", + " # Analyze impact of temperature on forecast accuracy\n", + " print(\"\\nAnalyzing temperature impact on forecast accuracy...\")\n", + "\n", + " # Remove rows with missing temperature values\n", + " full_df_with_temp = full_df.dropna(subset=['TEMPERATURE'])\n", + " print(f\"Rows with temperature data: {len(full_df_with_temp)} out of {len(full_df)} total rows\")\n", + "\n", + " # Create temperature bins with specific temperature ranges as requested\n", + " temp_bins = [-10, 0, 5, 10, 15, 20, 25, 30, 35, 40, 50]\n", + " temp_labels = ['<= 0', '0-5', '5-10', '10-15', '15-20', '20-25', '25-30', '30-35', '35-40', '> 40']\n", + "\n", + " # Create the temperature ranges with specified bins\n", + " full_df_with_temp['TEMP_RANGE'] = pd.cut(\n", + " full_df_with_temp['TEMPERATURE'],\n", + " bins=temp_bins,\n", + " labels=temp_labels,\n", + " include_lowest=True\n", + " )\n", + "\n", + " # Print information about the created temperature ranges\n", + " print(f\"\\nUsing specified temperature ranges:\")\n", + " temp_range_counts = full_df_with_temp['TEMP_RANGE'].value_counts().sort_index()\n", + " for range_name, count in temp_range_counts.items():\n", + " print(f\" {range_name}: {count} samples\")\n", + "\n", + " # Function to calculate R-squared for a group\n", + " def calculate_r_squared(group):\n", + " if len(group) < 3:\n", + " return np.nan\n", + " ss_total = ((group['TOTALDEMAND'] - group['TOTALDEMAND'].mean()) ** 2).sum()\n", + " if ss_total == 0:\n", + " return np.nan\n", + " ss_residual = ((group['TOTALDEMAND'] - group['FORECASTDEMAND']) ** 2).sum()\n", + " return 1 - (ss_residual / ss_total)\n", + "\n", + " # Group by temperature range and calculate accuracy metrics\n", + " accuracy_by_temp = full_df_with_temp.groupby('TEMP_RANGE').agg({\n", + " 'ABS_ERROR': 'mean',\n", + " 'PERC_ERROR': 'mean',\n", + " 'TEMPERATURE': 'count'\n", + " }).rename(columns={'ABS_ERROR': 'MAE', 'PERC_ERROR': 'MAPE', 'TEMPERATURE': 'Count'})\n", + "\n", + " # Calculate R-squared for each temperature range\n", + " r_squared_by_temp = full_df_with_temp.groupby('TEMP_RANGE').apply(calculate_r_squared)\n", + " accuracy_by_temp['R-squared'] = r_squared_by_temp\n", + "\n", + " print(\"\\nForecast Accuracy by Temperature Range:\")\n", + " display(accuracy_by_temp)\n", + "\n", + " # Non-linear (polynomial) model with both temperature and forecast as predictors\n", + " print(\"\\nFitting nonlinear model with temperature and forecast as predictors...\")\n", + "\n", + " # Create polynomial features for temperature (to capture U-shape)\n", + " from sklearn.preprocessing import PolynomialFeatures\n", + " from sklearn.linear_model import LinearRegression\n", + " from sklearn.metrics import r2_score\n", + "\n", + " # Prepare the data\n", + " X = full_df_with_temp[['TEMPERATURE', 'FORECASTDEMAND']]\n", + " y = full_df_with_temp['TOTALDEMAND']\n", + "\n", + " # Create polynomial features for temperature (degree 2 for U-shape)\n", + " poly = PolynomialFeatures(degree=2, include_bias=False)\n", + " X_temp_poly = poly.fit_transform(full_df_with_temp[['TEMPERATURE']])\n", + "\n", + " # Combine polynomial temperature features with forecast\n", + " X_combined = np.column_stack((X_temp_poly, full_df_with_temp['FORECASTDEMAND']))\n", + "\n", + " # Fit the model\n", + " model = LinearRegression()\n", + " model.fit(X_combined, y)\n", + "\n", + " # Make predictions\n", + " y_pred = model.predict(X_combined)\n", + "\n", + " # Calculate R-squared\n", + " r2_combined = r2_score(y, y_pred)\n", + "\n", + " print(f\"\\nNonlinear Model Results (Polynomial Temperature + Forecast):\")\n", + " print(f\"R-squared: {r2_combined:.4f}\")\n", + " print(f\"Coefficients:\")\n", + "\n", + " # Get feature names\n", + " feature_names = [f'Temperature', f'Temperature²', 'Forecast']\n", + " for name, coef in zip(feature_names, model.coef_):\n", + " print(f\" {name}: {coef:.6f}\")\n", + " print(f\" Intercept: {model.intercept_:.6f}\")\n", + "\n", + " # Individual R-squared values for comparison\n", + " # 1. Temperature only (linear)\n", + " model_temp_linear = LinearRegression()\n", + " model_temp_linear.fit(full_df_with_temp[['TEMPERATURE']], y)\n", + " r2_temp_linear = r2_score(y, model_temp_linear.predict(full_df_with_temp[['TEMPERATURE']]))\n", + "\n", + " # 2. Temperature only (polynomial)\n", + " model_temp_poly = LinearRegression()\n", + " model_temp_poly.fit(X_temp_poly, y)\n", + " r2_temp_poly = r2_score(y, model_temp_poly.predict(X_temp_poly))\n", + "\n", + " # 3. Forecast only\n", + " model_forecast = LinearRegression()\n", + " model_forecast.fit(full_df_with_temp[['FORECASTDEMAND']], y)\n", + " r2_forecast = r2_score(y, model_forecast.predict(full_df_with_temp[['FORECASTDEMAND']]))\n", + "\n", + " print(\"\\nR-squared Comparison:\")\n", + " print(f\" Temperature Only (Linear): {r2_temp_linear:.4f}\")\n", + " print(f\" Temperature Only (Polynomial): {r2_temp_poly:.4f}\")\n", + " print(f\" Forecast Only: {r2_forecast:.4f}\")\n", + " print(f\" Combined Model: {r2_combined:.4f}\")\n", + "\n", + " # Visualize the relationship between temperature, forecast and actual demand\n", + " print(\"\\nCreating 3D visualization of Temperature, Forecast, and Actual Demand...\")\n", + "\n", + " # If we have many data points, sample to make the plot clearer\n", + " plot_data = full_df_with_temp\n", + " if len(full_df_with_temp) > 1000:\n", + " plot_data = full_df_with_temp.sample(1000, random_state=42)\n", + "\n", + " # Create a 3D scatter plot\n", + " from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + " fig = plt.figure(figsize=(12, 10))\n", + " ax = fig.add_subplot(111, projection='3d')\n", + "\n", + " scatter = ax.scatter(\n", + " plot_data['TEMPERATURE'],\n", + " plot_data['FORECASTDEMAND'],\n", + " plot_data['TOTALDEMAND'],\n", + " c=plot_data['TOTALDEMAND'],\n", + " cmap='viridis',\n", + " s=50,\n", + " alpha=0.6\n", + " )\n", + "\n", + " ax.set_xlabel('Temperature (°C)')\n", + " ax.set_ylabel('Forecast Demand (MW)')\n", + " ax.set_zlabel('Actual Demand (MW)')\n", + " ax.set_title(f'3D Relationship: Temperature, Forecast and Actual Demand (PERIODID {target_periodid})')\n", + "\n", + " # Add a color bar\n", + " cbar = fig.colorbar(scatter, ax=ax, label='Actual Demand (MW)')\n", + "\n", + " # Save the 3D plot\n", + " plt.tight_layout()\n", + " plt.savefig('3d_relationship_temp_forecast_actual for temp U obin.png')\n", + " plt.close()\n", + "\n", + " # Save temperature analysis to CSV\n", + " full_df_with_temp.to_csv('forecast_vs_actual_with_temp_periodid24 for temp u o bin .csv', index=False)\n", + " accuracy_by_temp.reset_index().to_csv('accuracy_by_temperature_periodid24 for temp U o bin .csv', index=False)\n", + "\n", + " # Create visualizations\n", + " print(\"\\nCreating visualizations...\")\n", + "\n", + " # 1. Scatter plot of Forecasted vs Actual Demand colored by temperature\n", + " plt.figure(figsize=(10, 8))\n", + " scatter = plt.scatter(\n", + " full_df_with_temp['TOTALDEMAND'],\n", + " full_df_with_temp['FORECASTDEMAND'],\n", + " c=full_df_with_temp['TEMPERATURE'],\n", + " cmap='coolwarm',\n", + " alpha=0.7\n", + " )\n", + " plt.colorbar(scatter, label='Temperature (°C)')\n", + " plt.plot([full_df_with_temp['TOTALDEMAND'].min(), full_df_with_temp['TOTALDEMAND'].max()],\n", + " [full_df_with_temp['TOTALDEMAND'].min(), full_df_with_temp['TOTALDEMAND'].max()],\n", + " 'k--', label='Perfect Forecast')\n", + " plt.title(f'Forecast vs Actual Demand (PERIODID {target_periodid})')\n", + " plt.xlabel('Actual Demand (MW)')\n", + " plt.ylabel('Forecasted Demand (MW)')\n", + " plt.legend()\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('forecast_vs_actual_periodid24 fortemp U o bin .png')\n", + " plt.close()\n", + "\n", + " # 2. Error distribution by temperature range\n", + " plt.figure(figsize=(12, 8))\n", + "\n", + " # Create boxplot manually to ensure correct ordering\n", + " ranges = full_df_with_temp['TEMP_RANGE'].unique().tolist()\n", + " ranges.sort() # Sort the ranges\n", + "\n", + " # Collect data for each range\n", + " box_data = [full_df_with_temp[full_df_with_temp['TEMP_RANGE'] == temp_range]['PERC_ERROR'].values\n", + " for temp_range in ranges]\n", + "\n", + " # Create the boxplot\n", + " plt.boxplot(box_data, labels=ranges)\n", + "\n", + " plt.title(f'Forecast Error Distribution by Temperature Range (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature Range')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.xticks(rotation=45)\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('error_by_temperature_periodid24 fortemp U o bin .png')\n", + " plt.close()\n", + "\n", + " # 3. Correlation between temperature and forecast error\n", + " if len(full_df_with_temp) > 10:\n", + " plt.figure(figsize=(10, 6))\n", + " plt.scatter(full_df_with_temp['TEMPERATURE'], full_df_with_temp['PERC_ERROR'], alpha=0.5)\n", + " plt.title(f'Temperature vs Forecast Error (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature (°C)')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.grid(True, alpha=0.3)\n", + "\n", + " # Add trend line\n", + " if len(full_df_with_temp) > 2: # Need at least 3 points for a trend line\n", + " z = np.polyfit(full_df_with_temp['TEMPERATURE'], full_df_with_temp['PERC_ERROR'], 1)\n", + " p = np.poly1d(z)\n", + " temp_range = np.linspace(full_df_with_temp['TEMPERATURE'].min(), full_df_with_temp['TEMPERATURE'].max(), 100)\n", + " plt.plot(temp_range, p(temp_range), \"r--\", linewidth=2)\n", + "\n", + " corr = full_df_with_temp['TEMPERATURE'].corr(full_df_with_temp['PERC_ERROR'])\n", + " plt.annotate(f'Correlation: {corr:.4f}',\n", + " xy=(0.05, 0.95),\n", + " xycoords='axes fraction',\n", + " bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\", ec=\"gray\", alpha=0.8))\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('temperature_vs_error_periodid24 fortemp U Obin.png')\n", + " plt.close()\n", + "\n", + " # 4. R-squared by temperature range\n", + " if 'R-squared' in accuracy_by_temp.columns and not accuracy_by_temp['R-squared'].isna().all():\n", + " plt.figure(figsize=(12, 6))\n", + "\n", + " # Convert index to list for proper ordering in the plot\n", + " ranges = accuracy_by_temp.index.tolist()\n", + "\n", + " # Plot the bar chart\n", + " plt.bar(range(len(ranges)), accuracy_by_temp['R-squared'])\n", + "\n", + " # Set the tick positions and labels\n", + " plt.xticks(range(len(ranges)), ranges, rotation=45)\n", + "\n", + " plt.title(f'R-squared by Temperature Range (PERIODID {target_periodid})')\n", + " plt.xlabel('Temperature Range')\n", + " plt.ylabel('R-squared (R²)')\n", + " plt.ylim(0, 1) # R-squared is typically between 0 and 1\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('r_squared_by_temperature_periodid24 fortemp U Obin.png')\n", + " plt.close()\n", + "else:\n", + " print(\"\\nNo temperature data was successfully matched with forecast/demand data.\")\n", + " print(\"Skipping temperature-related analysis.\")\n", + "\n", + "\n", + "# Get a sample period for better visualization (most recent 14 days)\n", + "full_df_sorted = full_df.sort_values('DATETIME')\n", + "sample_period = full_df_sorted.tail(min(24*14, len(full_df_sorted)))\n", + "\n", + "plt.figure(figsize=(14, 7))\n", + "plt.plot(sample_period['DATETIME'], sample_period['TOTALDEMAND'], 'b-', label='Actual Demand')\n", + "plt.plot(sample_period['DATETIME'], sample_period['FORECASTDEMAND'], 'r--', label='Forecast Demand')\n", + "plt.title(f'Forecast vs Actual Demand Over Time (PERIODID {target_periodid})')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Demand (MW)')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('time_series_forecast_actual_periodid24 fortemp U Obin.png')\n", + "plt.close()\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/RF M2-1.ipynb b/src/RF M2-1.ipynb new file mode 100644 index 000000000..5d7f0f104 --- /dev/null +++ b/src/RF M2-1.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "M2-1\n", + "Doesn't include any weather features\n", + "includes Forecast , actual demand lag and mean , hour and day of week" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-04-20T21:55:16.882118100Z", + "start_time": "2025-04-20T21:54:33.009662800Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data timespan: 2010-01-01 00:01:24 to 2021-03-17 12:00:56\n", + "Total records: 195524\n", + "Data frequency: 0 days 00:30:00\n" + ] + }, + { + "data": { + "text/plain": " id period_id forecast_demand date_time_current \\\n7510461 2017100739 24 6824.10 2017-10-07 23:01:35 \n7510519 2017100740 24 6733.41 2017-10-07 23:31:38 \n7510576 2017100741 24 6665.92 2017-10-08 00:01:42 \n7510632 2017100742 24 6638.01 2017-10-08 00:31:40 \n7510687 2017100743 24 6582.00 2017-10-08 01:01:37 \n... ... ... ... ... \n10853504 2021031713 24 7325.13 2021-03-17 10:01:17 \n10853532 2021031714 24 7271.28 2021-03-17 10:31:09 \n10853559 2021031715 24 7172.65 2021-03-17 11:01:00 \n10853585 2021031716 24 7041.74 2021-03-17 11:31:07 \n10853610 2021031717 24 6955.50 2021-03-17 12:00:56 \n\n date_time_future date_time_current_rounded total_demand \\\n7510461 2017-10-08 11:00:00 2017-10-07 23:00:00 6954.92 \n7510519 2017-10-08 11:30:00 2017-10-07 23:30:00 6928.97 \n7510576 2017-10-08 12:00:00 2017-10-08 00:00:00 6963.49 \n7510632 2017-10-08 12:30:00 2017-10-08 00:30:00 6966.67 \n7510687 2017-10-08 13:00:00 2017-10-08 01:00:00 6939.97 \n... ... ... ... \n10853504 2021-03-17 22:00:00 2021-03-17 10:00:00 7419.77 \n10853532 2021-03-17 22:30:00 2021-03-17 10:30:00 7417.91 \n10853559 2021-03-17 23:00:00 2021-03-17 11:00:00 7287.32 \n10853585 2021-03-17 23:30:00 2021-03-17 11:30:00 7172.39 \n10853610 2021-03-18 00:00:00 2021-03-17 12:00:00 7094.51 \n\n temperature_future temperature_current forecast_interval \\\n7510461 21.2 13.3 0 days 12:00:00.000000000 \n7510519 21.3 12.8 0 days 12:00:00.000000000 \n7510576 21.2 12.7 0 days 12:00:00.000000000 \n7510632 21.3 12.6 0 days 12:00:00.000000000 \n7510687 21.0 12.4 0 days 12:00:00.000000000 \n... ... ... ... \n10853504 19.7 21.3 0 days 12:00:00.000000000 \n10853532 19.5 21.6 0 days 12:00:00.000000000 \n10853559 19.1 21.9 0 days 12:00:00.000000000 \n10853585 18.8 21.8 0 days 12:00:00.000000000 \n10853610 18.6 22.6 0 days 12:00:00.000000000 \n\n demand_lag12h demand_lag24h demand_lag168h hour day_of_week \\\n7510461 NaN NaN NaN 23 5 \n7510519 NaN NaN NaN 23 5 \n7510576 NaN NaN NaN 0 6 \n7510632 NaN NaN NaN 0 6 \n7510687 NaN NaN NaN 1 6 \n... ... ... ... ... ... \n10853504 7695.32 7373.83 6211.99 10 2 \n10853532 7595.23 7345.78 6409.04 10 2 \n10853559 7537.24 7218.99 6566.17 11 2 \n10853585 7511.38 7056.88 7056.38 11 2 \n10853610 7471.58 6999.23 7468.13 12 2 \n\n month demand_rolling_mean_24h \n7510461 10 NaN \n7510519 10 NaN \n7510576 10 NaN \n7510632 10 NaN \n7510687 10 NaN \n... ... ... \n10853504 3 7522.112083 \n10853532 3 7512.402917 \n10853559 3 7273.215625 \n10853585 3 7267.357708 \n10853610 3 7010.598542 \n\n[60247 rows x 17 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idperiod_idforecast_demanddate_time_currentdate_time_futuredate_time_current_roundedtotal_demandtemperature_futuretemperature_currentforecast_intervaldemand_lag12hdemand_lag24hdemand_lag168hhourday_of_weekmonthdemand_rolling_mean_24h
75104612017100739246824.102017-10-07 23:01:352017-10-08 11:00:002017-10-07 23:00:006954.9221.213.30 days 12:00:00.000000000NaNNaNNaN23510NaN
75105192017100740246733.412017-10-07 23:31:382017-10-08 11:30:002017-10-07 23:30:006928.9721.312.80 days 12:00:00.000000000NaNNaNNaN23510NaN
75105762017100741246665.922017-10-08 00:01:422017-10-08 12:00:002017-10-08 00:00:006963.4921.212.70 days 12:00:00.000000000NaNNaNNaN0610NaN
75106322017100742246638.012017-10-08 00:31:402017-10-08 12:30:002017-10-08 00:30:006966.6721.312.60 days 12:00:00.000000000NaNNaNNaN0610NaN
75106872017100743246582.002017-10-08 01:01:372017-10-08 13:00:002017-10-08 01:00:006939.9721.012.40 days 12:00:00.000000000NaNNaNNaN1610NaN
......................................................
108535042021031713247325.132021-03-17 10:01:172021-03-17 22:00:002021-03-17 10:00:007419.7719.721.30 days 12:00:00.0000000007695.327373.836211.9910237522.112083
108535322021031714247271.282021-03-17 10:31:092021-03-17 22:30:002021-03-17 10:30:007417.9119.521.60 days 12:00:00.0000000007595.237345.786409.0410237512.402917
108535592021031715247172.652021-03-17 11:01:002021-03-17 23:00:002021-03-17 11:00:007287.3219.121.90 days 12:00:00.0000000007537.247218.996566.1711237273.215625
108535852021031716247041.742021-03-17 11:31:072021-03-17 23:30:002021-03-17 11:30:007172.3918.821.80 days 12:00:00.0000000007511.387056.887056.3811237267.357708
108536102021031717246955.502021-03-17 12:00:562021-03-18 00:00:002021-03-17 12:00:007094.5118.622.60 days 12:00:00.0000000007471.586999.237468.1312237010.598542
\n

60247 rows × 17 columns

\n
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN values in features: 0\n", + "NaN values in target: 0\n", + "Index([ 7574338, 7574396, 7574453, 7574509, 7574564, 7574618, 7574671,\n", + " 7574723, 7574774, 7574824,\n", + " ...\n", + " 10853349, 10853382, 10853414, 10853445, 10853475, 10853504, 10853532,\n", + " 10853559, 10853585, 10853610],\n", + " dtype='int64', length=59095)\n", + "Feature Importances:\n", + " forecast_demand 0.990142\n", + "demand_lag24h 0.003592\n", + "demand_rolling_mean_24h 0.001764\n", + "demand_lag168h 0.001582\n", + "demand_lag12h 0.001457\n", + "day_of_week 0.000791\n", + "hour 0.000672\n", + "dtype: float64\n", + "\n", + "Model Performance:\n", + "- MSE: 54529.85\n", + "- MAPE: 2.14%\n", + "\n", + "Original Forecast Performance:\n", + "- MSE: 55155.44\n", + "- MAPE: 2.17%\n", + "\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABJUAAAIOCAYAAAABRTxkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3QWcVGX3B/DfdG0XsCwdgiCIYgK2WFjYha2v3a9iYCd2i4rYHa+tf2zERkKkG3aB7d3p/H/Oc2e2F3Zhd2d25/flM0zciWfi7sw995zz6CKRSAREREREREREREStoG/NlYmIiIiIiIiIiASDSkRERERERERE1GoMKhERERERERERUasxqERERERERERERK3GoBIREREREREREbUag0pERERERERERNRqDCoREREREREREVGrMahEREREREREREStxqASEREREbWbSCQS7yEQERFRO2FQiYiIqJ2cccYZ2GGHHXDyySc3e52rrrpKXeeGG27Y7sf77bff1H3JcXvepi2tX78et956Kw488EDstNNOGDt2LP7zn/9g1qxZLb69jP+DDz5o1WO29jYtJff7xBNPNLtcHlOu09zhjjvuQFeybNkynHLKKS36DNY9DBkyBLvssotad7799ts2G8+MGTMwZswYjBgxAk8//XSb3S8REVGyMsZ7AERERF2ZXq/H3LlzsXHjRnTv3r3eMrfbje+++w7J6pdffsEll1yiXpfzzjsPAwYMQFlZGT799FOce+65OPPMM3HjjTdu8T7y8vLw9ttvo3fv3i1+3G25TVt78sknkZub2+jynJwcdCVffvkl/v777xZdd8qUKRg2bFhNdlNlZSWmT5+Oiy++GM899xz23Xff7RqL0+nE/fffj/322w/nnHMOCgoKtuv+iIiIiEElIiKidrXjjjti+fLlauP6rLPOqrdMAko2mw1paWlINps2bcLll1+uslGeeuopWCyWmmWHHnqoyii59957MWjQIJxwwgnN3o/ZbMbOO+/cqsfeltu0taFDhzKo0cDAgQMbvS+jR49WQaBXXnllu4NKEqQKh8M46KCDsNtuu23naImIiEiw/I2IiKgd2e12tTEsQaWGPv/8cxxyyCEwGuvv4/H5fCrQIsEVKQkbP348pk2bpjaI63rrrbfU7aWU5/TTT0dhYWGjx5DLrr76auy+++4YOXKkyv75999/Wzz+W265RZULhUKhepfffffd2GOPPRAIBOD1enHbbbdhn332wfDhw9W4X3zxxS3erwSNJFPrrrvuqhdQipEAnAQYnnnmmZqePFJOeO2116pglCw7++yzmyxlk8yY0047TV1HAhIvv/yyur9YiWHD28ixBP/mzZuHk046Sb3m+++/f6PnILf773//q0r0JKNmr732UufLy8vRHhYsWKAytuR1luCblAVKOVnDsjH5HMh45To///yzWvbnn3+qz4S85/LeX3/99SoLrK6VK1fi0ksvVcslyHLhhRdixYoVrXq+//zzj/pM7brrrhg1apR6nSUzT0gZoGRktaQssDkpKSno169fvc92RUWFymrae++91Xt14oknqqy3uuTx5LEnTpyo1g85fcABB6hlkv0my+uuh3I9Gb981uW+JQAVI+M++OCD1X3IayWvhyyX+5PL7rnnHvUeye2vueYauFwutb7K+iCvy2WXXVbvNZP15aGHHlLrtawv8r7JZ3nRokU115HPqryW77//vlrH5XpHH300fvzxx1a9h/K35IEHHlB/g+Q+jjzySPV8iYiI2gqDSkRERO3s8MMPrymBq1uKIxuIEyZMqHddCaBI8OCFF15QGTrPPvusCtI8+uijqvdQzGuvvabOy8ai9IaR4IEEgOqSIIL0pFm4cKFaJhuyEpiSgEvdDc8tkQ3ZkpKSej2X5D6++OILHHHEETCZTGqjWp6LBC4kECP9kWRDVjaImyM9kyRbp2FJYF2HHXYYNmzYUG9jWx7X4XCoYJOUzDUkzyuWEfbwww+rDXrZwP/rr7+2+DzlOV155ZXqvZLry4a+PIeffvpJLfd4PJg0aZK6f3nd5XnK+c8++wyPPPLIVl7Fph8vGAzWO9QNGv766681vYjk9ZXgW1FRkXo/G753EtiQ116CIRLY+OOPP9RrYLVa1edGgii///67Gq8ENGKZYhJAW716tQoITp06Vb3PEiCSoE1Lnq98huU9yMzMVIEXuVxuJ4Gw6upq9fk9/vjj1XWl3HBLGWfN8fv9KrgVK1WUIImM8ZtvvlH9yOS5x8onGwaWZN2RIMrjjz+ugi6xANdFF12kxiNk3ZGgqwQg5XpSjvnVV1+pAGbstRIS1Prhhx/Uc5w8eTLS09PV5VKeJ++LXC73K6Wbxx13nPp833nnneq+Zaxy3zESmJN144ILLlC3l/uTYKEEpOo2NZeAnbzuEkSVILPBYFCf51jAa2vvodyXPB8JOkrQStYZ+XzI6/bRRx+1+r0gIiJqCsvfiIiI2plky0iZW90SuP/7v/9Ddna2ymSoS4Izs2fPVgERCdoIyZ6QAMFjjz2mNuylTEg2hiUAEus5JNkTspEvG5AxkqEjG5dvvvkmevbsqS6T7Am5ndxX3Q3d5sj45LaysSyZIUICTMXFxSrgJCRgIWOMjVeyNiRDS55fcyRQIGPZkj59+qhjCSxJJpGQINbtt9+uSthi91OX9N5JTU1VQTl5zUX//v232CxdyAa49O6JBT7kect79P3332PcuHFqw12CF9KTp1evXuo6e+65p8pukuffWpL50pC8h7HsKAkAyvOXAJcEE2LL5Xbyvsn7F3PqqaeqwGOM3Faye+S1iN1Wgo7y/kgwQ4KKkikmAZuXXnqppreTNMeWQJY8J+k7tbXnK2WdkoEjn0kJwsVeawnYSLaO3D4WNGxJuWEs0CbkWN53+ZxLcFTGLP73v/9h8eLFeOedd9RzEvI5kiDQgw8+WC+QKaVzEkyJiX1WJEAl45HgjARaJNNJAnIxgwcPVo8Xe61i45HAndxnw0wqCShJtqGsHx9++KEK9rz77rvqcygkMDlnzhx1Wl5zeW1uvvlmtR4KCXjJunvfffepoFDs/ZDAnGTRxQJqsk5J9pkEHCV7aWvvoYxJHlvGF3ss+SxL4E9eKwloN8ySJCIiai1+kxAREbUzCQhJqUzdoJJkfEgmjk6nq3dd2WCXDb26QQJx1FFHqUCCLJfm36WlparkqS65v7pBJcnckGygbt261Wysy21lI/zjjz9u0dhlfPLYb7zxhsqGkGCOjL1v3741G/USRJLHlUwsyZySg2RIbC2Is7UN2lhApG72hgQtYgGlpsgGtzy/WEBJSHZGLKi2JXK9GHmMrKwsVaIn5HWU10ACHxJgWrNmjQqqSPlR7LVtDQlmNGzUHQtCyGNK6ZuUNcVeAyG9t+Q9l4yZumRsMRIwkICCZAvJ6xYbmwSGpBG6lMdJoEQytySwUncMEgCq2zh+a89X+l3JaySZdfJ5lYCFBBevu+46bIuGPceEBCYlABMLQMpnWsYs5Xh1X3d5XSSzTAJFsSyiuq9LUyR7UIIyDbMFJXAknxdZ12JBpebuT0rr6n6OpdG6BH9i76XIyMjA0qVLaz5XscChBJ9WrVqlXt/Y6y7jiZHXtm4z+ViATt5jsbX3UAJHsv7K+lj3tZK/RbL+S3bU1l4jIiKirWFQiYiIqANIwEeCBBJ4kR5CsnEs5VYNyUaxlBPVDSaIutkLsfIXuV5T14mRLCUJBsRm1GootnG6NZKRJEEQyXqQwMHXX3+tSmxibrrpJrUxKxuqUvIjBwnQSBBKMieaIhvtkomyJevWrVPH+fn5NZdJ6duWSFZLUxlSLZlVTYJ/dUkArm5ASzJCpKRKXle5P+lRI8EreU9aS7JhmmvULfcnj9vUmOWyho8nQYyYqqoqFQh6/vnn1aGhWP8qeQ5baxS+tecr78Xrr7+uPhtSligZSvIayudFAkFbCv41RTLQYp9V+fxLcEje+7qBVxmLZMk195mWZbGgUt3XpSmx9ailr3NTnz3JVGpoa48r65GUNEqATu5T1pHYbep+3uoGRkXsdYiVSW7tPYyVwMWyyBravHkzg0pERLTdGFQiIiLqAJJpIRuQkq0kG5CyMSgb6Q3JBrGUFElj7LqBJdkAjAWSYsEkyVZquBFZl2RLSGmN9HBpSks3+qWUSjIyJHAggRYJXEj2Ut37kX4ycpDeM5IpIWVL0iNGspqaItkS0k9GAkvNZRHJa9WjR4+a0reWkOCWlBA1JK+VZDltq08++USVJ0kWjjR1liwSccUVV6isorYk75sEEJp6HhI0kcyX5shnTG4rWT+xcsS6YoEKeYyGjbuFBDvlsylZPC15vvKaSi8f+bzOnz9fladJuaVk2DTV82prnzNpvL0lMm7JkpMsnKa0Zka9WPBJXueGnw15nWNlf21p7dq1KotPZqCT8kR5DHm/JDgX69/VUlt7D2W5/K2RmfO2VF5KRES0Pdiom4iIqANI4EU2JKUJcKzJdVMkCCSlKg1ni4uVq0mvH9molmBLw+vULV2K3ZeU18Q21mMH2fB/7733GmVDbYlkn8hGrwSJJPMhtsEtzYylv4sEiIRklkjJkDy/pmaji5EeOJLlIU2K6zZErlt6JeVHMpuVBLJaSmbAknFKQ+cYme2uYe+l1pJSIyk/k0BJLMAivXHk8oaz8m0vCQRIwFE+J3Vn3ZPMGenx1LAPV13ymkoQTrJg6r7nUqomzbRjDdelxEvK5OoGJSTwJs9Pyuta8nzl8yd9liQAI5+lWHaa3C723rfmvWsJ+UxLY2zJRqv7/KSsT/poteYzLeWbsl5Kv7C6ZOY8GX9zGT7bQ5pvy2dTmnRL4C2WfRQLKNXNVNqarb2H8lpJKaXcZ93XSkrxpPH3tpRtEhERNcRMJSIiog4izXJjQRIpD2ouo0l6FMly6bkipTESXJFSpmOPPVY16RbXXnutygSS60k/G8kskQyRuiRbRQJIcnzOOeeoDCeZTlyaHEswp7Vjl8wVuX3dWeik3ElKkWRmLWmiLVO1SyBLGhZLsKk50ghaekTJzFaSCSPNnqXnj5QkSTBFglcSnIrNgNZS0t9Hxigb1vKcJatKHkde84b9q1pDMrXk9ZXXQPr3SOaY9MaRLJdYxktbkvdW+iJJ8EEacQcCAdW0W3rubK1flcw4JreT+5CMMglMSdBPAhDSjFzIZ0JmAJPXST6T8t5JGZtkesmMaTJj2daerwRdJMAk45HHkywpee8k+DV+/Hh1HQkwCQncSBBne7N/5LMiMx9KA255ryW4Ko3tZf2QJtbyPFpKMr5k3BJgkdvJ85Tgo3xeZD2T9a2tyboiPZgku0s+n/J+SjNuCRaKWA+vltjaeyiZShJklfdcDrJ+STaZNHqXMtZYsJCIiGh7MKhERETUQWR2KNnIlg1h2cBrigQ+pCxGNvxkdifJQpBSFgkU1J3JSpoLS6BEyswkcCQ9eu644w51vRhp0C0NtGU2MMkgkQwJyXK6++67a6Z6bynZAJXZxyQjpGETcXlcmbpeAheStSJZJHL/Uiq1JZLlIhvF8jylf49koMjrI9kUEiSQDd/WkpIeCX5I02YJWMlYZINbNra31o9pSyTAIAEHmRFMsqjktZUGyBLwueWWW7BixYpm39Ntsddee6nXRD4H8p5KRo1kpshsbJJ1tCWxWeQk0CevgQQbJJgh9xebhU0+g/I8JLhxww03qPuXYKbMFCZBo5Y+X8kOkiCM9NWSHl2xjCh5b4UEl+TzKY8hnwn5HG5vFpeUislnWsYuASwpn5QAmgRpWuuyyy5T/ZMkUCU9oSTQJJ9v6Xe2td5I20I+nzJ2eW+kXFRea3lPXn31VZW9J1lSEphtia29h0ICkfL+yN8UyWKS91H+jmwtMElERNRSukhr8myJiIiIEpj0k5EgSt2p3yVbSQJ60ltKMqKIiIiIqG0wU4mIiIi6jIULF9Zk90h2jjQvlwwdKQVqOHU8EREREW0fBpWIiIioy4j1qZF+QFJOJyVM0rD43nvvZQ8ZIiIiojbG8jciIiIiIiIiImq1tp3nlYiIiIiIiIiIkgKDSkRERERERERE1GoMKhERERERERERUasxqERERERERERERK3GoBIREREREREREbWasfU3oZjS0mp01rnzdDogOzu1Uz8HovbC9YOoaVw3iJrGdYOoaVw3iDrn+hEbW0swqLQd5I1PtDc/GZ8DUXvh+kHUNK4bRE3jukHUNK4bRF13/WD5GxERERERERERtRqDSkRERERERERE1GoMKhERERERERERUauxpxIRERERERFRnITDYYRCwXgPg+LQDNvr9SIQ8Hd4TyWDwQi9Xt91gkp+vx8TJ07ELbfcgj322ENdNnfuXNx3331YsmQJ8vLycN555+GEE06ouc3s2bNxzz33YN26dRg5ciTuvvtu9OrVq2b5jBkz8OKLL8LpdOKwww5T922z2dQyn8+H22+/HV9//TWsVivOOeccdSAiIiIiIiLqCJFIBFVVZfB4nPEeCsVJWZleBRXjwWZLQVpaFnQS3erMQSUJ8FxzzTVYtmxZzWXFxcU4//zzccopp6jA0sKFCzF58mTk5uZiv/32Q2FhIS655BJcdtllGDduHJ566ilcfPHF+Pjjj9UL8tVXX+HJJ5/E1KlTkZ2drW4rp6dMmaLu/4EHHsA///yDl19+Wd3X9ddfj/z8fBx66KFxfCWIiIiIiIgoWcQCSikpmTCbLdu9cU+dj8GgQygU6fBgpt/vg9NZrs6np2d33qDS8uXLVUBJnlRdM2fORE5ODq6++mp1vm/fvvjtt9/wySefqKDSu+++i+HDh9dkF917770YM2YMfv/9d5Xp9Morr+DMM8/E/vvvr5ZLVtK5556L6667Tj2W3P7555/HsGHD1EECWq+//jqDSkRERERERNTuwuFQTUApJSUt3sOhODEa9QgGOz5TSYKYQgJLqamZ21UKF9dG3bEg0Ntvv13vcsk+kkBRQ1LKJubNm4fRo0fXXC5lbRIckpK5UCiEBQsW1Fu+8847IxAIYPHixeoQDAYxatSomuW77rqrus94pZ0RERERERFR8pDt1rob90QdLfbZ295+XnHNVDr11FObvLygoEAdYkpLS/HZZ5+pcrdYeZz0WapLytw2btyIqqoqVVJXd7nRaERGRoZaLhG4zExJLzTXLJesKLlNRUUFsrKyWjz+zpydGBt7Z34ORO2F6wdR07huEDWN6wZR07hutOS14YtD8RH77MlRw49haz6Wce+ptDXSDV2CSRL4Oemkk9RlHo+nXlBIyHlp+C3Xj51varmUvzW1TMjy1sjOTkVn1xWeA1F74fpB1DSuG0RN47pB1DSuG43Jdqs0aZaeOlICRcnLGKf3PxzWRZNuHGoCs22V0EEll8ulGnCvXr0ab7zxRs3sbRaLpVEASM6npaWpZbHzDZfL7SXNsKllorUvZGlpdYdP/ddWJPIof9w783Mgai9cP4iaxnWDqGlcN4iaxnWjeTKNvLRfkSbN8eip01Y+//wT3HPP7bjhhpsxYcIxLbrNhg3rsXbtGuy115jtfvy7775NHd90023Nji3GYDAgKysb++67Py644GLY7Q4kek+loqJCnHDCUXj33Y/Ro0d+mz62fPbkM1he7oLJFGhy3e3UQSXpn3Teeedh7dq1apY2adYd061bN5SUlNS7vpwfOnSoKnOTwJKcHzBggFomPZSktE1mj5NMpfLycnWZlMXFyukkoCRBqdaQP4yd/Y9jV3gORO2F6wdR07huEDWN6wZR07huNNZVXo+ZM79Cz54F+PLLz1scVLrvvjux8867tElQaWvy8rrh+edfVqelz/KqVSvw2GMPYeXKFXj00ae3q0F1VxHZzvUzIV9BiZZdeumlWL9+PV599VUMGjSo3vKRI0fir7/+qjkv5XD//vuvulw+FDvttFO95dLAWwJIQ4YMUYEnOS2Xxch15Tb8QBERERERERFtXXl5Gf766w+cffb5mDfvbxQWbmjR7RrO/t6eZBs/OztHHbp374G99hqLBx54RI33xx+/67BxdGUJGUV577338Ntvv+Guu+5S2UOSSSQHyTYSxx13HObMmYNp06Zh2bJlmDx5smrsLTPJxRqAv/jii5g5cybmz5+P2267DSeeeKIqf5PDMcccoy6TZXKd6dOnY9KkSXF+1kRERERERESdw7ffzkRKSgrGjz8MOTm5+PLLz+olfjzwwN04/PAD1eH+++9Wk2NJudrcuXPw0kvP49JLL1DlXWPHjlbHMS+++JxaFvPJJx/h1FOPw3777YkjjjgQDz10f83seduid+++KlPqxx+/r7nshx++w+mnn4ADDxyD88+fhL//rk1SkbG88caruPLKi3HAAdry9evXqed08MHjcPLJx9a7/qxZP+Dss0/FAQfsjUMP3Q+33noj3G53zXO7/fab8eCD92L8+H1x2GEH4vXXtUwqIRVVjzzygLrdsccejtmzZyHRJWRQ6auvvlLZShdeeCHGjh1bc4jN/iYBpCeeeALvv/8+jj/+eBVseuqpp2q6lx9xxBHqtlOmTME555yDESNG4Lrrrqu5fwlCDRs2DGeeeSZuv/12db/jx4+P2/MlIiIiIiIikiwe6S3ckYdtzRz65puvVeaPZAONGbOPCirF7ktK3ObPn4f77nsIjzzyFBYsmIvnn38GV1xxLYYPH4GTTz4d99wzdauPIcGaRx+digsvvARvvvkBrr12Mj777H8qcLM9+vbth9WrV6rTy5YtVcGuSZPOxcsvv4Xx4w/HtddergJHMTNmvICjjpqIF198NdqqZ5Kagf6FF15Fv34D8OijD9b0i7r55utx7LEn4PXX38Mdd9yHv/76HR9//EHNfX333Uw1Wdj06a/htNMm4ZlnnlA9pmJBp59//gn33fcw7rzzPrz33ltIdAnTU2nJkiU1pyXLaGv23XdfdWjOBRdcoA5NkWyl+++/Xx2IiIiIiIiI4k0CMhMmjMcff/zWoY+7++574pNPvqpJ0miJTZs2YsGCeTjppNPUeWl+/dFH72H+/LkqyPL999+oYNKIETur5ddddyOWLVuiMpukHY1sk6elpaug1pbYbHbccMMt2HffA9R5aVb91luvY9WqlTWXbQuHI6Ume+itt17FkUceg/HjD1XnTzjhZMyd+xc+/PA9XHbZVeqyvfceiwMOOEidHjduPxVQO/fcC9VrdtRRx+LGG69VyyQ55sorr1OXxca76667q/HGpKen45JLrlSNw08//Uy88soMLF68CL169VZZWZdeeqXKpBKXX341rrvuSiSyhAkqERERERERESWz1gR24kmCKpJts8cee6nzo0btitTUNHzxxac4+uiJqjxtyJChNdcfOXKUOrSW3IdMxCUZPNJke8WK5SqDSAJh28PtdtXM/iazza9cObNeNpE09d59d+25ifz8njWnZTzSnyn2XlksFnV9IYEhk8mMl19+UTUDl2woCSgdcsjhNbfv0aOnCijF2O12hELa5GIVFeUYNGiHOs9/GBIdg0pEREREREREcSZBCskYimXQdBQJarQ2mCWzvkmPpEMOqa0ekkCSlHZNmHB0i++nqcet2y/pt99+weTJ1+LQQw/HnnvujbPPvgAPPXQfttfy5cvQv/+Amsc77bQzceihR9S7jgSLYuoGgZobd6yU7uKLz8PYsfuobKOTTz4N77zzJuqKzUJfV90SxLqnTSYTEh2DSkRERA38sbYc1b4QRvdKR5o18b/MiYiIqGuQYIXDoWXQJCrp/7N06RJceeW12GWX0TWXS0aONKVet26tCsLIpFojR2rlbz/99L1qzj19+uv1AjJGo/Y7q24gre4scp988iGOOOIoXHPN9TWNrKVv0a677rbN45fxSZneKaecoc737t0HRUUbUFDQq+Y6Tz/9GHr16qPK4lrjq68+x847j8Ktt95Vc9n69WvRp0+/rd42IyMDWVnZWLx4IQYOHKQuW7p0MRIdg0pEREQN3PXVUhRW+fDSqTtjeA8GlYiIiIjqZilJPyRpXC0lcDH9+w/ESy+9gP/7vy9V1s9jj01VjbWlkfdzzz2NvfYao64n/ZSkhK28vAxZWVnIy+uGN954BeeccwHmzfsbv/wyq6YETB7nn3/mqbI3CUa99toMlJaWwO/3t2is0uNIri8CgSCWLVuMJ598VAWlxowZpy4/8cRTcckl56lSM+md9PPPP+Ltt9/AY4890+rXJj09XY3133//QUpKKv73vw+waNG/9crnmiPPb+LEE/DCC8+hW7ceSE1NxRNPPIxEx6ASERFRA2ajNjmqLxiO91CIiIiIEq6f0vjxh9ULKMUce+xxeOyxh/DOO//D9OnTcNVVl6gSrgMOOBjnn3+Rus6ECcfg3nvvwJo1q1Tm0uTJt+CRR6bijDNOVMGeSZPOwS+//Kyue845F+Kee27DhReepZprS2DqmGOOV02/W2Lz5k04+mitAbfZLL2Ququxn3rqpJrrDB++E2655Q41XslQ6tmzALfeendNs+zWOP74k6NZXJeo10eyls4++3wViGsJee5er1dlfEm2l9z24YcTe4IxXWRb5w8klJRUo7O+epJxmJOT2qmfA1F74fqR3LyBEMY9rv2QmXrUjthvUE68h5QwuG4QNY3rBlHTuG40LxDwo7S0CNnZPVRjZ0pORqMewTjtxNzSZzC27raEtiuWiIiIFG+g9os9zF/ARERERETNYlCJiIioDm+wdsaRQIhBJSIiIiKi5jCoRERE1Eymki/EnkpERERERM1hUImIiKiZTCU/G3UTERERETWLQSUiIqJmMpV27ZUR17EQERERESUyBpWIiIiayFQalOtAv2x7vIdDRERERJSwGFQiIiKqQwcd8lLMyHZwel8iIiIioi0xbnEpERFRktmjbyaeO2kk1ld4sKLEhQE5jngPiYiIiIgoITFTiYiIqIFPF27CZe//g/fnFcV7KERERERECYuZSkRERA1YjNo+F87+RkRERNRYSUkJXnzxOcye/SOqq53Iz++Jww8/EieeeAqMxubDDJdeegFGjdoV55574VYf4/jjj8Q551yg7nd7zJnzJy6//D+YNevPJpePHTu6ycvHjz8MU6bcic7ir7/+QHZ2Dvr27dehj8ugEhERUR0fL9iIp2etVqd9IQaViIiIiOratGkjLrroXPTu3Qd33HEfcnPzsGjRQjzzzBOYM+cPPPDAo9Drmy6KuueeqTAaTS16nOeffwV2uw0d4e67H8Dw4SPqXWaxWNGZXHHFRXj88WcZVCIiIoqnDVXemtM+ZioRERER1fPoo1NVZtJDDz0Bg8GgLpPzw4aNwBlnnIgPP3wPxx13YpO3TUtLb/HjZGZmoqOkpqapLB9qPfZUIiIiqsMbCNWcZvkbERERUa2yslLMmvUjTjttUk1AKaZ79+44/PAJ+OSTj9T5zz//BBdddA4mT74WhxyyL77++gtV/iZlczFvv/06jjnmMIwfv68KVl122YXqdrHyt9hpud3LL7+Iq6++FAccMAYnnzwRv/32S839rFq1Ui07+OB9cMABe+Pii8/D6tWr2uQ5+3w+PP3045g48QgcdNBYXH/9VSpbSxQVFaryuRkzXsChh+6Phx++X13+ww/f4fTTT8CBB47B+edPwt9//1Vzf8FgEM899xSOPvoQHHjgPrj55utRWVmhlhUXb8bNN/9X3df++++Fc845DfPnz6257bvvvoXjjpugnuO5556BefPm1rxWQsr86r6+HYFBJSIiojrqZif5grUBJiIiIqKO4AmEmj00zKLe0nXr7ijb0nVbY8mSxYhEIhgyZFiTy0eM2BnLly+F3+9X5xcsmI9+/frjuedmYPfd96p3XQkyvfjiNFx++TV49tnpKkAzd+6cZh/7lVem46CDDsGrr76NQYMG4/7770I4HFYHCfT06JGPGTPewDPPTEcoFMIzzzyOtvDgg/fixx+/w803345nn30JwWAIkydfox43Zv78eXjxxVdxwgmnYNmypbj77tswadK5ePnltzB+/OG49trLsX79OnXdF154Fl988SkmT74VL7wwA+XlZZg69R617I47bkEoFMZzz72E6dNfV6WFDz10n1q2dOliPP30Y7jmmhvw+uvvYeTInTFlyvVqHFIqGCvjO+WUM9CRWP5GRERUR90fYL5gJK5jISIiouSzz+M/N7tsTL8sPDpxeM358U//Am8zmdW7FKTjuZNG1pw/6vnfUeEJNLreH9fs0+KxVVdXqePU1NRmy8hEVZV2PZ1OhzPPPKfJ/kQffPCuaux9wAEHqfM33XQ7Jk48vNnH3muvsTVNu88881ycddYpKnPK4UjBMccch2OPPQE2m9aD6bDDJuCNN7RAS0tce+0VMBhqc27S0zPw3nufqOfx1Vef48EHH8cuu2gNvW+99U6VtfTHH7+pvlJCnkfPngXq9J133oIjjzwG48cfqs6fcMLJmDv3L1UWeOmlV+KTTz7EJZdciT333BtGox7XXjsZ3377fypYN27cfthvvwOQl9dN3XbixBNx3XVXqNNFRUXq9ZSMMAmgnX/+xdh773EqqBQrFZTX3263oyMxqERERFRH7IdZrwwrThvdM97DISIiIkoYsaCRBHNigY+6SkqK1XFamna9zMysZhter1ixDKefflbNeblNLEjTlF69etecdjgcNaVkEkg65pjj8eWXn2Hx4n+xdu1qLFmyBFlZWS1+XjfccDN23LE2WBdrNL5u3VoVtKm7TPpCyTjXrFlVM14J8sSsXr0aK1fOxMcff1BzWSAQUJlaFRUVqKysxA47DK1ZJplcsdnwjj32eMyc+RX++Wc+1qyR57G4JiNqjz32Qv/+AzFp0skYPHgHjB27L4466tgtzrbXERhUIiIiqiOWBn72Hr1x4ODceA+HiIiIksyPl49pdplep6t3/uuL65eU1VX/msDH5+++3WMbMmRH1UtpyZJFTQaVJKgzYMAgmM1mdT523BStJ1P9rHDJ1mlOU8ETub7b7VZ9iyS7aOzYfVSJnASW3nzztRY/r5ycXBQU9Gp0eXPjlxI1OTR1PSm9O+20M3HooUegLovFssUAkASPrrrqElRXV+PAAw/GmDH7qGDUTTddp5ZbrVZMmzZDlQj+/POPqt/URx+9r8rupEwuXthTiYiIqA6L0YBUixF2c/3mk0REREQdwWYyNHuwGPUtvq7VVP+3THPXaw0ps5ISrRkzXlTBk7qkefWnn36Mo446pkX3JRk6kokT43I5sX79erSWNMGWDKnHH38Wp546Cbvttocay5YCVC0lJW0S/Fq4cEHNZdJUe/36tc1mVfXu3QdFRRtUkCp2kKylX3+drcoGMzIyVN+pmGXLluDYYw9XzcYlYPToo09j0qRzsPfeY1FaWqKuI89FspdeffUlVYZ32WVX44033off76vXyDseGFQiIiKq44GjdsTnF+6BHIcZf6+vjPdwiIiIiBLKlVdeq3oNSfNpmX1s48aNarYzmXls1KhdVW+jljjuuJPw7rtv4ocfvlUztd17753weNyqb1BrpKenw+Px4KefvlfNvmX2uffff0dl+Wwv6U905JHH4pFHHsCcOX9i+fJluOOOKSpLS4JXTTnxxFMxc+bXaqa2DRvW45133sDbb79RU753/PEnq2bdcn8rV67AY489hGHDdlIBJym7++abr7BxYxG++24mpk/XZnKTxueS6fTSS8+r5yfP85tvvlbPWzLDhJQBrlq1Ak6nEx2J5W9EREQNbKr24by35iHFYsB3lzafgk5ERESUbKRUbNq0lzBjxgu4/fabVJ+g/PyeOPro41TD6lg/oq2RMjWZEW3q1HtV0ET6A3Xv3qPVPYKGDx+Bs846Dw89dL+6nwEDBuLqq6/HfffdieLizdhe0lz7yScfxc03X68CVaNH766yiZorjRs+fCfccssdmD59mpqtTbKdbr31buy88y5qufSRkhK3KVNuUD2hpNn2lVdep3pKycxu8ro+99xT6NWrD6644lrcddetKptJnufkyVPUcglydevWXT1O3779aoJVTz31uApkyYx6HUUXaYucsCRVUlKNzvrqSfA3Jye1Uz8HovbC9YM2Vnlx5PO/w2TQYfaV4+I9nITBdYOoaVw3iJrGdaN5gYAfpaVFyM7uAZOp+b5DXZmUrUkwSoIjQgIsEyYchHvuebBmprWuzmjUI9jM7H3x/AzG1t2WYKYSERFRHVd/+I/KVBKBUAThSKRRU0wiIiIi2j5SrrZgwXxcd91k2O0OVQonx1IKRp0HeyoRERHVMWd9JZYWu2rO++O094iIiIioKzvvvP+optYy49lZZ52CNWtW46GHnlC9g6jzYKYSERFRlFSEewP1ZzLxBcONZk8hIiIiou0jWUnSE4g6N2YqERERRQXDEYQa9Hzwh5ipRERERETUFAaViIiIoryB2gCSNOmOZSoREREREVFjLH8jIiKK8ga10jeJJ100pq86nWrhVyURERERUVP4S5mIiKhBppL0UDpjt17xHg4RERERUUJj+RsREVGd/kl2kwF2MxtzExERERFtDTOViIiIogbkOPDD5WPULHBryz2o9gbQK9OGNKsp3kMjIiIiIko4zFQiIiJqQKfTYcrni3HWG3Mxd0NVvIdDRERElDDGjh2tDhs3bmy07KOP3lPLXnzxuW267zlz/lS3b4nPP/8Exx9/ZJPLiooKa8YZOxxwwBhcdNG5+OWXWWhLY8eOVuMWMh4Z19aUl5fh229nNnkfnQ0zlYiIiOqKRJD55v54s3IzTtDdCH9waLxHRERERJRQjEYjfv75Bxx33En1Lv/xx+/VzrlE8fzzLyMvr5s67fV68e67b2Hy5Gvx+uvvoWfPgnZ4vFdgt9u2er1nnnlCZcaPHz9enf/f/75EWlo6OiNmKhEREUX9sbYcV78/B8by5UiLVKG/rkj1WSIiIiKiWiNH7oJZs36sd5nL5cQ//yzAoEE7IFFkZGQiOztHHSSIdOmlV8JstuDnn+uPva1kZmbCYrFu9XoSUKpLxmcydc52CwwqERERRRVV+bBgTW0qdxg6eIMMKhERERHVNW7cPpg7d44KJMXMnj0LI0fuDLvdXu+6Ug522mnHq/Kzc889Q90uRm5/66034uCD98HJJ0/E4sX/1rvtpk0bcf31V+HAA8eo0rLp06chFApt87gNBm0yFqPRVFN29sILz+KIIw5UjyPmzftbjVPGO2nSSfj++2/q3cdLLz2PCRMOVrf59NOP6i2rW/4WDAbx3HNP4eijD8Ehh+yLm2++HpWVFao08IsvPlWHY445olH5m8/nw9NPP46JE4/AQQeNVeOS16FuWd8PP3yLE088GgccsDf++98rUVVVWfOY999/lxrbwQePU7ctLt6M9sSgEhERUZQ3EIYD3przZgThZ1CJiIiIOlLA3fwh6G3FdT0tu+426N9/IHJy8vDrr7/UK30bN26/eteTAMsjjzyA008/CzNmvI7Ro3fHddddURPomDr1XqxduxpPPjkNV111Hd566/V62Tw33fRfZGZm4aWXXseNN96K//u/L/Hqqy9t05jdbjemTXsawWAAe+yxV83lkrX0zDMv4j//uQylpSUqSHP44RPwyitv4bTTzsTdd9+uAk3if//7AO+88yYmT56CRx99Gp9++nGzjyfBKgkcTZ58K5599iXVR2nq1HtwyikSsDpYHV566bVGt3vwwXvx44/f4eabb1e3CwZDmDz5GoTDtb9JX3nlJdx229144olpWLToX7z5pnY/77//Nv7+ew4efvgpvPDCq+o5P/74w2hP7KlEREQU5QuGYNP5as5bdX4GlYiIiKhD5U4b3OwyX58DUDXhlZrzOdNHQtcweBTlz98Tlce+V3M++5U9ofeWNbpe8SXrtzlbSQIyBx54MPx+P/7441dcffV/8fXXX9Rc57333sLxx5+Mww6boM5fdNFlKlPp/fffUYGm776biccffxY77DBELT/rrPPw8MP3q9N//fUHNm4swrRpM6DX69G7d19ccsmVuOee29X1WuKMM05UPZ4kQCU9lXJz81RAqG4/paOPnqjuWzz//DMq8BXrFVVQ0AtLly7BO++8gZEjR+GTTz7CSSedijFjxqnl119/s3qMhuTxPvnkQzXePffcW1127bWT8e23/6cyuSwWS025XLDOb82qqip89dXnePDBx7HLLlrD8ltvvVNlLf3xx2/o3buPuuzccy/EjjsOV6fHjz+0JsOrqKhI3XePHj1Uj6abbroNlZVaFlN7YVCJiIiomUwlK/zwMahERERE1MjYsVpJl5Rc/fXX7yp7SbKK6lq9ejXOPvv8epcNH74T1qxZhXXr1qhStkGDaoNoQ4fuWHNariNlXVI6FiPZOlIeJmVkLTF16mMqkCSBJZvNhqys7EbX6d49v95j/vzzT6p0LEaeX69evaPPZ2W9gFa/fv3V/TZUUVGhgjk77DC03nUlGLQl69atVc8xFjASEhySYJKMLRZUkmBXjN3uUGMURx11LGbO/ApHHXUIRo3aFfvss7/KumpPDCoRERFFeYMh2OtkKu3bx45gr845EwcRERF1TsUXLG1+oa5+B5uSc+Zt4br1Z2ErnfQr2tKIETur4/nz5+LHH3/APvvUL30TZrO50WWhUFgdmmpaHet1pF0vpDKI7rvvoUb34XCktGiM3bv3QI8etUGjptQdozzm+PGHYdKkcxrNdlerfpNtg6FxWKX+9VuuqderqdesYVPv2GvYv/8AvPfeJ6q/1ezZP+G5555UJYNPPfV8u83Kx55KREREdTKV7HUylfYqsGHXXhlxHRMRERElGZO9+YPR2orrNsigae5620gCJ3vtNUaVwM2e/aPKimlIMmsWLvyn3mULFy5Ql8tB7kN6AsUsW7ak5nSvXn1Ug2qZwU0yc+RQVLRBNbpurwCJPOb69etqHk8OP/30Q01JX79+A+qNt6ioEE5ndaP7SU1NRUZGBpYvX1rvuR177OHw+bzNjl/K8qSZuLxGMZKVtX792pospS2RHk7yfhxwwEGqJ9ODDz6hgn7Sz6m9MKhEREQUFQxH8E+4H77pdQWq97kbnpH107WJiIiIqNa4cfvik0/+h8zMbOTn92y0/KSTTlPNo7/88jOsXbsGzzzzBFasWIYjjzxGZRsdeugRePTRqSrwJLOfyexuMbvvvie6d++OO+64BStWLFfNsh944B5YrdaaWdza2sSJJ2Dx4kWqobeUon399ZeYNu0plfEkjj/+JLz77ltqRriVK5fjvvvuVP2emiK9pKRZtzyvlStX4LHHHsKwYTvBYrGq5yABqc2b68/MJv2WjjzyWNXcXG63fPky3HHHFOTldcNuu+2x1fHLbHryOH/++TsKCzfg//7vC3Xb9PT220nK8jciIqKoyQcPQuSggQhHgGJ/CMXVPliNXuSnN9grSERERETYffe9VD8fCS41RZp4l5WVquCKHA8cOBgPP/wk+vTRGmPLjG+PPDIVV111icrukUDMU089qpZJ4Oi++x5WQacLLjgTNpsd++9/EC699Ip2ez4SPLr//odV8OvNN19VM9xdeumVqiROHHLI4aioKFdjloyj008/q142Ul2yrLq6GlOm3KBeo733Hocrr7wuej9H4MYbr8EZZ5yMTz/9v3q3k8d78slHVb+qQCCgGofLTHPNlcbVNXHiiSpQdeedU1BdXaV6Okn5YHsF4YQuUreAkVqlpKQanfXVk2y7nJzUTv0ciNoL1w8Sb8/ZgAe/W4GDBufi3iNrmywmM64bRE3jukHUNK4bzQsE/CgtLUJ2dg+YTFsPFlDXZDTq683+liifwdi62xLMVCIiIqrDULoI1sXv4ag187FKPxKbg8fEe0hERERERAmJPZWIiIiiHv1+JX75/EXY5z6H3uW/4CDDX/DXmWmDiIiIiIhqMahEREQU9fvacmwuK685b4Uf/jilJBMRERERJToGlYiIiKJ8wTBs8NWct+n88DKoRERERETUJAaViIiIoryBEBw6b/1MJZa/ERERUTvhvFnU2T97CRNU8vv9mDBhAn777bd6l69ZswYjRoxodP3Zs2er648cORKTJk3CunXr6i2fMWMGxo0bh1GjRuHGG2+Ex+OpWebz+dRlo0ePxtixYzF9+vR2fGZERNRZeBtkKllY/kZERETtIDbFu99f+7uDqCPFPnsGw/bN35YQs79JkOeaa67BsmXL6l1eVFSECy+8UC2vq7CwEJdccgkuu+wyFTh66qmncPHFF+Pjjz+GTqfDV199hSeffBJTp05FdnY2Jk+erE5PmTJF3f6BBx7AP//8g5dfflnd1/XXX4/8/HwceuihHfq8iYgoATOV9LXfOXnWMI4d0SOuYyIiIqKuR683wGZLgdOp9XI0my1qW5aSSzisQygU6fAMJQkoyWdPPoN6vb5zB5WWL1+uAkoNU69mzpyJW265Bbm5uY1u8+6772L48OE455xz1Pl7770XY8aMwe+//4499tgDr7zyCs4880zsv//+avntt9+Oc889F9ddd516HLn9888/j2HDhqmDBLNef/11BpWIiJJYKByBPxSBzVBb/pZhCuKM3XrFdVxERETUNaWlZanjWGCJko9er0c4HJ+seAkoxT6DnTqoFAsEXXXVVdh5551rLv/+++9xxRVXoF+/fqq8ra558+ap0rUYm82mgkNz585Vly9YsACXXnppzXK530AggMWLF6ugUjAYVGVxMbvuuiueffZZ9WZub5SOiIg6b5Nu8VjwODy8rx367AEIZg6M97CIiIioi5LMpPT0bKSmZiIUCsZ7ONTBdDogM9OB8nIXOrq1lpS8tVXsI+5BpVNPPbXJy++66y513LDHkiguLkZeXl69y6TMbePGjaiqqlLlcnWXG41GZGRkqOXywmVmZsJsNtcsz8nJUbepqKhAVlbLI3WdOTsxNvbO/ByI2gvXj+TksBjw29Xj4AuOQdCkRzgCFDt98JW70TvTxpR0rhtEzeK6QdQ0rhstYzDoYTDUbp9SctDpAKvVCrM50OFBpa1pzTob96DStpCm23WDQkLOS7Nvr1crW2huuWQqNbVMyPLWyM5ORWfXFZ4DUXvh+pHcKt0BTHj4J3V62d2HwWRgJmsM1w2ipnHdIGoa1w2irrt+dMqgksViaRQAkvNpaWlqWex8w+VSJhcKhZpcJiRK2BqlpdUJF1FsTeRRPryd+TkQdYb1o6qqEh9++D6OOOIolRVJic+86mtE9Ebo1/+O641r8GjwOGzYWIkUS6f8ymxT/O4gahrXDaKmcd0g6pzrR2xsLdEpfyF369YNJSUl9S6T80OHDlVlbhJYkvMDBgxQy6SHkpS2SdNvyVQqLy9Xl0lZXKycTgJKEpRqDXnjE+3Nb62u8ByIEnn9eOWVl3H77Tdj7dq1uPnm29pqaNQO1pZ78NxPKzBtnTYJhLjICDwfPEL1W3IwK70GvzuImsZ1g6hpXDeIuu760Slz+UeOHIm//vqrXjncv//+qy6Xnkk77bRTveXSwFsCSEOGDFGBJzktl8XIdeU2bNJNRG2tpKS43jElLumf9Muy9Y0ut8Jf08SbiIiIiIhqdcooynHHHYc5c+Zg2rRpWLZsGSZPnoyCggI1i1ys+feLL76ImTNnYv78+bjttttw4oknqvI3ORxzzDHqMlkm15k+fXqjGeaIiNqC1+upd0yJyxsMwwafOh3RGRC2pKvTVh2DSkREREREXab8TQJITzzxBO655x489dRTGDVqlDqOzcxzxBFHYMOGDZgyZYrqlzR+/Hhcd911NbeXIJQElc4880ykpKTgsssuU9chImprMrOk8Hi0SQQocfkCITh02vsUMdkRMUifvUrY4IefQSUiIiIiosQOKi1ZsqTRZZJ91NTl++67rzo054ILLlCHpki20v33368ORETtKTYjpcfjjvdQqAWZSvZYppLJDqigEmBh+RsRERERUeIHlYiIumpQKXZMicsbCMGGWKaSA9Cb1OlDB6Ui066dJiIiIiKiWgwqERG1I58vFlRiT6XOkKnk0EUzlYx2QG9Qp08angV/hi3OoyMiIiIiSjwMKhERtSNmKnUe3kAYy8I98U7ulTh01CAEs4YAkRDCab3iPTQiIiIiooTEoBIRUYf0VGKmUqKbtHsvnDgqX532WbSvR6cvCI8/BAdCsJu1zCUiIiIiItLoo8dERNQOmKnUeRj1OqRYjOoQc8vni3H4c79h5pLiuI6NiIiIiCgRMVOJiKgDeioxU6lz0FesgqF6A0JpBTBWrMRE5zfw6PrCFxoY76ERERERESUcBpWIiDokU4lBpUT37txC9F80DYeWvQrP8DOBsB8Tq97ECv2J8AUPi/fwiIiIiIgSDoNKREQdEFTy+/0IhUIwGNiXJ1H9vqYcps2l6psxYrIDIe29sul8cAXD8R4eEREREVHCYU8lIqJ2VLeXEvsqJTZvMAwHtPeoqLQSy1evU6et8MMXDMV5dERERJRsnvppFa784B8Ew5F4D4WoWcxUIiLqgJ5KsaCSw+GI63ioeb5ASGUliTff/wh+ZxlG7G+NBpX4Y46IiIg61ozftR1cCwqrMKogPd7DIWoSM5WIiNpJOByGz6cFKQT7KnWeTKWNpVXwBLXLbTpmKhEREVHH2yEvRR27/fwdQomLmUpERO2kbkBJcAa4xOYNhGGD9p5VuAOIWLXL+6fr4cxPi+/giBJIMBiE0cifkERE7S3bYVLHpS5/vIdC1CxmKhERdUDpm2BQKbF5gyE4dNp75vRH4Alol++YY8LhO3aL7+CIEsT06c9j4MAC/Prr7HgPhYioS/MHw1hfof0uKXUzqESJi0ElIqJ20rAxN8vfEj9T6cXg4Zjf5wIs2BzC58uC+CT3SrjGTIn30IgSxqxZP8LtduP333+L91CIiLq0zU4f1pZrvx2ZqUSJjLnLREQdFlTi7G+J7N2zR8Mb3AWFKxdhZfmD6rKVoR4YbusN+IJIsfArk8jprFbHbrcr3kMhIurSvP4QHjI9g910i/FhyeUABsZ7SERNYqYSEVE7YaZS55JuM6FbqgXu6EazWFChx/5Pzsa1/1sY17ERJQqn06mOWc5LRNS+fJ4qHGf4Cb31xUh1rYn3cIiaxaASEVE7YU+lzsdU+CvMxQtg0gPdHDocEPgOpxq+UX0NiAhwubSgkpTAERFR+wl4andyjcqJ61CItoi5/ERE7cTjYVCps3D5g3j8++V4eNnxOBxApk2Hnmk6nGr4HIX6LJwZPCreQyRKqEwllr8REbWvoFf7eyt66zaDu7coUTFTiYiogzKV2FMpcVV7g/hiwdqa8646s79Z4WemElEUM5WIiDpG0FubqeQvXxfXsRBtCYNKRETthD2VOg9vMAw7fDXn3QHAE4yo0zYJKoUYVCKq31OJQSUiovbkd5XXnA67SuD2h+I6HqLmsPyNiKgdM5WO39GIMb0M+HJ5kJlKCcwXCMOu094fX8SISDSwJGw6P3wB/pAj8vv96iCYqURE1L7yTbU7Iw1BJxYVOzGyZ3pcx0TUFAaViIjaifRQum5vC3bvacCRg014mj2VEpY3GKrJVPKGDerYE9AylUQkVJvFRJSsnHVmRmRQiYiofWXrav/mrovkotTJ3yKUmFj+RkTUTnw+nwooiQFZejbqTmDeQBgOaJlK3pD21egJ1i4/ZEBKvIZGlHClb4Llb0RE7asiaMGHiwJ4aNNonOC/DSWxFGqiBMNMJSKiDmvUzaBSovIEQrDptD2AroBOHUtvbjkY9cB1+xRw1hVKei5X7YxvzFQiImpffwf64LLvuiF9z13gSAdKXVr5MVGiYaYSEVE78Xg4+1tnatS9NpKH1xxn453VGTWXX/F7T1Qc8y7C1sy4jo8o8crfagNMRETU9j5bHUT+OU/C1n+0Ol/qYqYSJSZmKhERtWemkr32PDOVEtd+A7Ox83lHQqc7Cve9/G3N5d+t1cOXvyf8wTDMkQj0Oi2LiSjZy9+YqURE1L68AQkimXGT7iUcbl6OT4rPAjA43sMiaoRBJSKidqIyk+zNZy5R4rCaDOhu0vpfVVdX1esbM+bRWQiGI/jsgj2Ql2qJ4yiJEieoJD3jQqEQDAZtvSEiorZ1YegNHGX5CSarNgOtxb0h3kMiahLL34iI2gl7KnUuemcRjJvnwxGurLlsv27VONv4NfJRAp80WCJKYi5XbVBJsFk3EVH7MUe8MOlCCIa1LOnhtop4D4moSQwqERG1E8lMOmbpBHXaH9Zx9rcENmtlKf7+4llkvns4rhpVO2Xv5aP8uEn/Egbr18EXYlCJklvDoJLLxaASEVF7sUBrzL28Sisu6hHeFOcRETWN5W9ERO2YqTQ/YyyO9O2J6monvN534z0kasafayvRq3AzDjIC1b7a4JHLJynnBlgRYKYSJb265W+CmUpERO3HDi3jfXmlHkMyAKOnON5DImoSM5WIiNqxp1I4sw8WRPpjdcoIlr8lMG8wBFv0x5uzzoy97kBEHdvgU826iZJZw6ASm3UTEbUfm077QbKsXCt/MwUq4fQF4zwqosYYVCIiasdMpUtXXYWrje/A4drARt0JzBsMw6HTyt5cgQhsNlu9oJJV54cvqDXKJEpWTmc17DuMQf55z8CU2w9utyveQyIi6rJSg+XquNiv/SZJ1bmxsLB2MhGiRMHyNyKidiI9lK7eYxP0uo+QaduEW0Pcq5+ofIG6mUoRZGVlY+PGInhkNl+VqSRBJS3ARJTMmUq5x0xWp3MmXM1MJSKidmQPVkljJQTt3bEuEESJoRvKq6sBZMV7aET1MFOJiKidhP0e6LWMZZxh/6WmNp4SM1PJDl9N+VtaWhpsNjvc0UDSjjlGZNlNcR4lUXy5XLWZSXqLg5MPEBG1oz83G/HNyiBsPYZg1LyTcKz/Dmz2cvOdEg8/lURE7UQXqr/BpQsxqJSovIEQ7LHyN38EqalpsNvtNZlKhw1Kw075afEdJFEClL/VxfI3IqL2EQiFccmfvXHI/1KQOWA3hFxaKVypu07jR6IEwaASEVF7CdQPIpkQRCjEvjyJmqn0XmgffG08GP8Wh5Gamqr6Kk37y48/Bt8I3w7HxXuIRAlR/uYrXKJOB8rWs/yNiKidbKzywXTkrcg/50kMGrRDTVCpuJo7KCnxMKhERNRO9A0yk+xWI8tFEtS9E4Zi4unX4GfsgUUlWlBJMpUWbA5jjXkogun9EI6wpxIlN5fLifLvX4Lvr/dR/efH8HgYVCIiag8uv5YqHQ540adPX9zUbxFmWS7HrpvfjffQiBphUImIqJ3oQlo5VYxdH4LXyz1Miah7mhUDcxzwVleo86mp6SqoJN5aEcYeD/+EV35fF+dREsU/U+nkrCX4Z9cPMdw7h5lKRETtxL1pFZZazsDfmTcgPS0NWZYwCnQlSPFujPfQiBphUImIqJ0YEW3IE2U36eD1MlMpURlK/oXNvR4GHaLlb3YMzNJjH//32Fu/AP5QON5DJIp7ptKMY2zItobx8KE29lQiImonzsoSmHUhmHVBWG02uELaZCGDTcXxHhpRIwwqERG1kzkbgYN9D6A4kq7Oy+RhLH9LTM/OWoWMtw/DbdkfoVuKLjr7mw379TXg8tBLOMvwNXxBBpUouUmm0g+hEer0jNV5cLmYqURE1B7cVWXacVgLJoVN2mQhucFNcR0XUVMYVCIiageRSASVfmBZpABn+CfjyCVHYuamNGYqJag3/1gFPbQm6k41+5v0VHLUzP5mhY9BJUpqfr8fgYgODl20hHf4BAbJiYjaid9dqY5dYbN2QUp3dWQNaGX6RInEGO8BEBF11Q0wnVH7IbA40hvo0xuVxj/g8bCnUqIJhsIwh2s3jl1+KX/TMpXcAa05t1XnZ/kbJTWnsxo6kw0OaOuKM2JFyL053sMiIuqSQl4tqOSOmCH57qas3up8is6NTZ4A0m1aBhNRImCmEhFRO/D5vBhuL8U588/F4fpf1WU6k4WZSgnIGwzDDq2pui+kQyhSG1TyBLXrWOFnphIh2Uvf9GYrhuq1hvVXp/4fG3UTEbWTjJAWVPIFtN8e9rz+6jjV4Mf8tQzoU2JhphIRUTuQjKTR3YEpu3tQrZuOfH8pfsyX2d8YVEo03kAI9mhJTyyIJD2VtPI3LVPJBj/8DCpREnO5XCpTCdACSXkGJxt1ExG1k4yQ1lMpHPCr46z8AVi+IQNVphxUqZlqe8Z5hES1mKlERNROmUoy25tIjThxs+l1HNbbC6+X5W+JnKnkivZQ0mZ/qy1/SzcGMTDXEc9hEsW9/E0ylWIi0MHNnkpERO2i1KvDd6uC2BDMUue798jHHnOOxET/Hdjg5+8RSiwMKhERtQOfzwdHmjZTR4wEmVgukni8gTDsOi2oVO3Tgkja7G/2msylbEsY5+7ZJ57DJIp7+ZvObMO5/mvU+UqkwO2LRmGJiKhN/VTdCwd/6MDH3j3U+e7duyPkKlenN1YwS5QSC4NKRETtQMrcUlKltWL9oBIzlRKPNxjCxkgmXtEfi+lztPcnJUXK3+xYUxHG1FXDUH3AQ/EeJlFcSVApULoOkXUL1Hk7vHD7o1FXIiJqU3+HC1Bw8UsoyRiizqekpMIY1LJDi8qdcR4dUX0MKhERtQMJHtnN2p9Yf8Sgju0mLdhEiWVgjgP3nHEEuh96E6bO9teUv0lQqdoPfF2YCn/fA+M9TKK4crmcCFUVw1KpNeq2+srgdVXHe1hERF2SN6QdO6y1s7xdO3ANZlkux6GVb8ZvYERNYFCJiKidgkq2aE+lar2WsWRybWSmUgKymgwYlJuCPLOWdWEymWC1WlVPJVFuzMJ+T/yM01+dE+eREsU3qDQwS49Hd16iztsjLrgrS+M9LCKiLmmy7X38ZbkQE8x/1lyWYdWjQFeCrFBxXMdG1BCDSkTtSO8sgr66MN7DoLg16tZOe41abyW7IcxMpQSl85QhsGkxMqxalpJOp1OZSmJcyhocHPwBAR/7YVFyl791y8uBRa/NgljsirBHHBFRO0nTu5Gtq4bNUjtZe8igNejui01xHBlRYwwqEbWXUADZL++G7Fd2B4LMTkk2Ho8X968cjGN9t+OX9AnqMptRu5wSy+JN1Vj45ZPY7Zez8OB4K1JTtSCgNOoWD+64EI+an4Y5yFIfSu6gUu7gndXptca+GPF2JgIwIBhkXyUiorZm02kTIRgtKTWXGVJy1HFOkEElSiwMKhG1E72nNjVV72GJQDJmKq0PZeDvyCC8490dk73n4tpFQ+HxcM9+ollQVI0Fa7UfaC5/pE5QSSt/84a0MkZ9iAFBSl5OZzVSrFp/uGK/CT3PexbWPiP5N42IqB3YdVqPR6M9o+Yyc1ZvdZyi4+xvlFgYVCJqJ8GKDTWny4qL4joW6njSO0lvtKjTf5Ua8SYOxLLsfdlTKQF5AyHY4VOnnX4gLS1armjX0sw9wWhQiRmHlOSZSqlmbV1wRazqWG+ysgSOiKgdOKJBJaujNqhkyxugjtONfpQ72U6BEkdCBJX8fj8mTJiA3377reaydevW4ayzzsLOO++Mww8/HLNmzap3m9mzZ6vbjBw5EpMmTVLXr2vGjBkYN24cRo0ahRtvvBEeT+2K5/P51GWjR4/G2LFjMX369A54lpRsnIVaM1OxqXBNXMdC8clUOtrzIW6omoqLhmt/anUmC3sqJSBvMKymRxeugGQqpdbLVHIHIurYEPIiEtFOEyUbl8uFVLN2eh/DAnxsvgn90rXLiYiobdkC5eo4IzO75rLMnoMQjgAGHTBv+ao4jo4owYJKEuC5+uqrsWzZsprL5Ef7JZdcgpycHLz//vs4+uijcemll6KwUGt4LMeyfOLEiXjvvfeQlZWFiy++uObH/ldffYUnn3wSd9xxB15++WXMmzcPU6dOrbn/Bx54AP/8849aduutt6rrfvnll3F49tSVrVqzQh3/EBqBf6DtWaDkIb2TLt3Jh//k/Y1djStwiuEbnJ27mJlKCcgbCMOuq81UipW/xRp1u/1aY2IL/AjKrzmiZC1/M9V+/kfoVyHTrq+3046IiLZfIBCAPaT1cczJzau5vFuPAsx15+Kv8CBUOdnnkRJHXINKy5cvx4knnoi1a9fWu/zXX39VmUcSFBowYAAuvPBClbEkASbx7rvvYvjw4TjnnHMwaNAg3HvvvdiwYQN+//13tfyVV17BmWeeif333x8jRozA7bffrm4rP3wkTVtuf9NNN2HYsGE4+OCDcd555+H111+Py2tAXdcnvtEY63sUNwfPRkklywOSMVPJFp39zaoL4l7Ti7it+w/cAEtAvmCoNlPJX5upFAsqeaKZSiPyTAgxqERJXP4WMlhQGtHWD5FqNcDtZqYSEVFbqq6uwpyiMP4sDMGWmV9zebdu3XHQnP1xnP92LHLWNvAmSuqgkgSB9thjD7z99tv1LpfMoh133LHmB73YddddMXfu3JrlUroWIyUKEiCS5aFQCAsWLKi3XAJSEvFdvHixOshMJVIWV/e+5T7DYW1vNFFbWF8VwPpIHtZFuqHMyaBSspEsTIdd6zuyOaj9LbPrg/Cx/C0xM5WiPZWk/C0tLV2dtlpt9TKVLtu7B6wmrVExUbJxuZx4zrUfdvU9h8W6geoyh1mCSvx+IyJqS+WVVTjyEzv2e88OfYbWnFuYTCZYI1qvpQ1lVXEcIVF9RsTRqaee2uTlxcXFyMurTfUT2dnZ2Lhx41aXV1VVqY25usuNRiMyMjLUcr1ej8zMTJjN0cYAklaYk6NuU1FRoUrpWkqn9avslGJj78zPIdGVugMYb/gDhxj+RHnlAOh0B8V7SNSB64f0TrJnS6pSAKvcWnBJhP1urncJxhsM4cvwbtjkNWFxyc8YmJqq3iOz2aR+wN39kx/PPHIXUvNGJv17x++O5M5Uqv7jI5x0+Q3Ql9shyX0pZp2a/Y2fB64bRM3hutF6qzZXoODiGYg08ZsxzaxTudXF1V6+pl2ALoHXj9aMKa5BpeZIeUjdoI+Q89LQe2vLY/1KmlsufZeaWiZi999S2dm1KeCdVVd4DonqNP2XON/8lTo9MxRBTg5f62RaP3S6MKz6oDpty+oOrNcuN4S9/CwkmBuPHIbi6ttw+/VXYO7Gn3B+j9ya98jhcOCL5RXwDjocffrtEO+hJgx+dyQfKXNzrfse5499Etk/ZwGFgKVyNYxGfr/VxXWDqGlcN1ouEAmpY10o0Ojv6wV91uEky2X4IbArcnKOitMIqa1ld/L1IyGDShaLRWUN1SUBH6vVWrO8YQBIzss00LIsdr7hcimTk/K4ppaJ2P23VGlpNTrrREASeZQPb2d+DonuWNPsmtPmQDVKSthQL5nWj/LyKthytaBSfmYaQjojDJEgwn4PPwsJxiGHFBOqNmqRP73eXPMeaSVwFbjgvZUoD6zGsyeOwOC85O1jwO+O5FVdXY1HD7Wgz7eXwO7SZjQ1la3Axo2l/JvGdYOoWVw3Ws+57l/8abkPm4ypKCk5st6yVKsRPXWlyAnxb29XoEvg9SM2tk4bVOrWrZtq4l1XSUlJTUmbLJfzDZcPHTpUlblJYEnOS5NvIT2UJEiVm5urMpXKy8vVZVIWFyunk4CSBKVaQ974RHvzW6srPIeEFAogV1/7hz4vXMzXOcnWj6DPDaNO68WTYk9F2GCFIeiELujhZyEB6avWw+grg14HpKSk1bxHqmdfrh6jA79jrq8HPIHhfP/43ZF0ZOebHEb3yUFK6d8I2XJQFTQhHJEJUFz8LNTBdYOoaVw3Ws7vqkSOrgpBna7Ra2awa61aeoaKEA5HoEvEuilKuvUjro26mzNy5EgsXLiw3tTbf/31l7o8tlzOx0g53L///qsul55JO+20U73l0sBbAkhDhgxRgSc5HWv6HbtvuY3clqgthJyb6p23Rzg7TrJxe3w4pvwKnOafDIMtFRGDlglpCLWuzJba38cLNiL9tX3xzaGrUZCmq7eDwW534JLdzXgID+NIwy/wBzmhAyUfp7MaOqMZmXk91PmN4x7COWuPw7OL0zmjJRFRGwt4tCbcbtRv2SLsWQXqODtcyoASJYyEjKLsvvvu6NGjByZPnoxly5Zh2rRpmD9/Po4//ni1/LjjjsOcOXPU5bJcrldQUKBmkos1AH/xxRcxc+ZMdbvbbrsNJ554otrjLIdjjjlGXSbL5DrTp0/HpEmT4vysqSsxezbXO2+LzixFycPj8+EvDMXP4Z2gN5jwZu7VmPj3Hli0uTZYTonh1d9XwxiJzv7ml0yl2lRf+c5wB7RdR1YE4GNQiZK0SbfOZIUDWgBpeZUOf+YfhdyJN6tMJSIiajsRv1MdeyJaW5e67Hn91XGmyde5U1uoS0nIoJLBYMDTTz+tytImTpyIjz/+GE899RTy8/PVcgkgPfHEE3j//fdVoElK22R5LFp7xBFH4MILL8SUKVNwzjnnYMSIEbjuuutq7l+CUMOGDcOZZ56J22+/HZdddhnGjx8ft+dLXY+vZJU6Lo7OtJxi1HrrUPKQTEudUfsxYDXqcevyvpgz9AqU+42q/JYShy5QOyW60x+pl6lks9nhCWinrfDBF2JQiZKPy+WC3myDQ6cFxU02bR3Rm6xwu2vXHyIi2n4y65vwNpGplF4wVB1bDEBZef12METxkjA9lZYsWVLvfJ8+ffDaa681e/19991XHZpzwQUXqENTZM/z/fffrw5E7cGzaaU6Xliqx372MGz6IJxBL2BsXTN46rxsESdOXHgV9hp/HLqnjYWEvGV/ks5khtfrqZcNQ/GlD2nZFxIv8oWA1NTa98bhsMPtjGUq+eELajOyECVd+ZvKVNKykrqV/4k3TR/gj4wC/LkpeRvXExG1B4e/FLAD4SY21fPy+6AiZEGGwYc5ixbjoDG5cRkjUcJnKhF1dqvWazPjLEcv7OZ9Cjt4ZyCoa7y3gbquLL0bD+5ZgcM8H8Ji1GM/00KcZPgOO+SY4PGwBC6RGILaHkFnNCMpNbVuppINnmhimU3nZ08lStryN6PFqtYBYUYAexn+xTBzETOViIjamN2zUR1bjYZGy7Kzs/G3Mxt/hgdjc2l5HEZHlMCZSkRdyYu+8bjVtz+qN/+AytxMdVmVL4gsOwNLyUIf1gJHYb1WAne24QvsY5iDst52lalEiSEcicAomUpGwBXQgkgmk6le+Vusp1KuNQyftXYZUTIFlRw2C8oiKUjReaFL0WbjtesDcDGoREQJSF+1FqbC3+AbfCyg71ybvKWuEOYUhRDo263RMmn3ctayA6Ebfhj2delxalxGSFQfM5WI2sFGN7A+kgu/wY6QV2u2VxVLd6CkYIhOyFEd0UoeA9Hgkt1sqDezJcWXNN62Q3s/nL5wo7JEu722p9Lo7hbsPygnHsMkiiuXywmXzoFdfNNwfNZHMDq0Ka0dOh/czLwkogSU9dpYpH1zFaz/voXO5v016Rg9I4J5ORObXJ4a3b+1qYp/fykxMKhE1A4q/FpmQ4+MFJyj+wwPmZ5BeMMf8R4WdSCjWdsrttGnHQd0tUElj4d79hOFNxBCGdLwWvBAvL0wUK9JdyxT6df1IbxeuTvcI8+L2ziJ4h1UCpSuQ/fNf+Do4d1hsjrU5Xb44PazzxgRJR5dRCtXNxX+gs6mpO+B6H3VO5jvzWhyebZdiyqVc4c1JQgGlYjawdWmd/Bf41sY1t2KfU0LcZzhJwQ3LYz3sKgDmaKZSv5oMCmo1zKW7BY9eyolkBSLEdefeCiWF5yGKd/56jXpFlIOt6wsjK9KeyHQZ/+4jZMo3uVvwbL16O9dgaN26g69WWvObQuUw+PWsnGJiBLJD6Zx6nipcQg6m2B0Ez3d3vQEP8flrcNsy6W4QTe9g0dG1DQGlYjaWCTox+nmH3Cx8WP0zc1ARVDbmxCo5rSfycSsD9fLUBrVt7s6trhL2FMpgZgMeuxSkIFuEa3ZZWpqer3ldruWkbHS3BcTpv2G535eHZdxEsU7qDQ6X48pfX5Dyo83I2KKZiqF3fBUlcV7eEREjRR5tCbXK4o2o7O5J/cLfG++CnuEmq5yyEy1I19Xhm66ckQiWnUEUTwxqETUxjwV2owN/ogB3Xr0RvnGQnW+p74yziOjjhIMBmGLpioF9Vpz9ox0LVhhMwTZUynB6PxOBKs2wmxAk5lKKWZgJ9Ma9HfNQXmswRJREnE6q9ErLwNDLJuAjfMQMdkQ1hkRCEc4+xsRJSQ3ohni0b6JnYUEiXoYq9FXvwlplqY31R2Z+eq4e7AQYcaUKAF0rlb4RJ2Ar3KDOi4Op6EgPx8LvFq9s9HHvbnJQjKRPinKRKn/fKRn5mGY/Egw2tQyKYNnplLi2FjlRfWPT+Iyz5NIm2DFl7aGPZVs6J+pxzPd3sPmSAbuDB4Qt7ESxTNTKW+HXQD8jg0eI9IdPfDDAV/h4MPGI8XCnh5ElFj01YU4x/ilOp1h9KMzkR2PDoP2d9WSqs0g3VBq9/5AJZCjq4JPH+23QBRHzFQiamM5YS14tK6oGHl53eDV29V5nVcrr6Guz+v1odCfgh/CI7HBrtXy/2neC/8tGo/nVuXD42FQKVGsKnPjjxVaNqEz0DhTyeFw1Mz+ZoVPzRZHlGxcLhdStUpehIx2mdMad/9SgYJLX0UoZ2C8h0dEVI/eXVvyFoy2Iegsqqur4dBrPzxsKdpMmw1lFGi/LdPMESDI35QUfwwqEbUxz+YV6rjICWRlZcFt66FdzmamSUMykXQm7UeM1ajV9H9YmIp3Ms/CEseuDColEG8gXJMa7/JHkJraOFPJHdByy63wwx9iUImSs/xNykBFyKj1U3JEZ7gM640IBFgWmmz+/PN3LF26JN7DIGqS3qvt4F2Mvvix1yXoTKqrK2uCSiZ7/R1dMTk9B9bs8CpcvbQjh0fUJAaViNqYv3SNOq4IWmEwGOC091TndSEGEpKFz+fFboG/cNTCG3D5UK3fiNWk/bnVmyzsqZRAvMGQmhZduAJNBZXsiM3Ya9aFuPFMSVv+lqLNOVHTpPta72N4zXQ3+mcAHg/7KiWTkpISHH30YTjhhKPjPRSiJlVVFKvjjKxuOG+vPuhsmUr2ULU67XDU/00Sk5Kais0Rbdkf//zboeMjagqDSkRtbP3GInVcmaaVBCyPFGA371M42/ffOI+MOorH48Vpgzx4fNe1GO76VV2WHSnHBP0vODivnD2VEohHMpV0WpDP6QfS0ur/gLPb7fBEM5VEJMD3jpKPy+VEqjGknTGnqKOhgYUYa1iIHIeezbqTzKpVK1SAvaioEH5/5+pXQ8mhokSbNOe39W6sWLEMnUlVVRUc0H5rpKZmNHu9BZ5c/BEejE1Vrg4cHVHTGFQiamP3+E/BGO9j+NQ/Wp23WOwoRiYqw9HaAUqKTCWbUWucGGvQ3du/Ak+an8CNfRcyqJRAvIE6mUqq/K3h7G92RHvtK72azkQn6vKZSnqDQc1qCrOWqeQ3aH/bHBYD3G5u1CQTCSbFVFRUxHUsRE0Ju7Xyt6PtC5D13VXoTKqrqrC0NIx1LhMi5uZ/dNy0cTxO8N+GX90FHTo+oqYwqETUxjb7DNiAXBitWsZDlkOb0tTPyRaThpq5I1Xbm7/RowWX9Obo7G+GkMpkosQgjbdt0aCSZCo1DirZIHlKsWyl/+7TKy7jJIp3ptKU6okY7HsV64ZcoC4L6rW/aalmA1wuZiolk8JCbZZbUVHBSUgo8eiiPZWE3VsbBO0MKqurseuMMM6cMwoRW9ONukWWTevZWepmWT7FH4NKRG3MGdJWq25p2g/u7BQLbjG+ivvNLyLi5R69ZMlUSs3QpoFdUKx92euifUhshhAzlRIsU2lWeDg+KB+I5WVhpKam11tut2vv2/UzfagadyciFqYqUXJmKjn//hzHDklHfoY2o2kwmoXpsOg5+UCSKSxkphIlNk9I26EnLNEdR53Fhkovel/1LtbsctEWr9ctXftbXM2YEiUABpWI2lA46Mc9lhn4r/Et9MmKBpXSU3CC4QccZ5wFX2XtFKfUdXm9PlgNWmaLzqR9DgwWLWPNqpdMJW6AJYojhnXH0KNuwH++COO3DaEmM5XEE7/7UTbgREQs9YNORF2d9MyRQ/WcT3HxmN7okab9LbPYtGxci3M9y9+SuvytNiOE4mPNhkKce+eTmLeIs4DFvJF5Cfb1PaxOx7KRO4sKl/Yb0YBoH7tmjM/cgF8sl+IRy3MdNDKi5jGoRNSGnGWFKnh0nuEzFHTLVZdlZ2SgIqLtTXBXl8R5hNQRJBPJbohOPR/dm79T727q2BbxcPa3BNI704Yx/bJQsXqhOt+wUXcsqGTM6I4z3lyASa/Nics4ieLF6dRmIXp2ghUFs66GoXSROp+doQVYzaXL2Kg7qcvfmKkUbze8+SPm20fg2g/mxnsoCaOk2gN3RAuAq76JkdoJNxJdum8DvjVfjRctj2zxetmZGeihK0MPQzlC4c7z/KhrYlCJqA1Vl2g/tDaH09Cje4+ajdTi0kp1OlvPH97JwOfzqYwkoTdrP2oy07QNMLsxAq+Xn4NE4q8qBkJa/njDTCWDwQCr1Yph2RH0rJ6LqvJNcRolUfxK36DT46CBNtjW/B/gc6rLIyYHgmHApJcZL/k3LdmCSjqTBdAbUVnJoFK8rQtoPRxd/i1ntiSTkmovXNB+f+l1ESDYeXbm6f1O9NdvRIF+yzuis7r3Vce5wY0MKlHcMahE1IZ0Lm2Dc1PAjry8PHU6NTUNpVVaKqs5oAWXqGuT8rYbNx2Ei/xXwJW2g3ZhtAxOr9Mh5GP5W6KYvaoMeW+Mgf+WNPTP1CElpXHPJMlWevYAL94y34XhocVxGSdRvLhcLugtDqSlaf3FQtH+cNX73IPD/9wHD/1lYqZSEgmFQthUWo7/XnkezrzgXJSXs1F3vIWri9WxZ8HX8R5KYohE8GpkMp6sqjPrm18LhncKQe3vqTcaFGtOZq8d1XGOyQsj6kxTSxQHDCoRtaGeOm2vwppVq5GX170mU6nMo+1B0LNRd9JkKv3iH4AvwnsA9mx1WanfiFdtZ2PS7wPh4uxvCeOtv9bDGIq+H6YU6PWNvxZtNjvcAa2c0RzxIcg9gpRk5W86sxWOaF8SvUXLipi1uhJLR/wHeSfeyZ5KSaS4eDP698zCffZXMaPbmyhnplLc+YxaoLds3RL2bJQdvAEX8jzLcEBeJZwRK4oj6Qj4O8/rYghpf2t9ui0HlbIKdlAZSrKzsqpoeQeNjqhpDCoRtSFfySp1XOw1ICVF++Gdnp6OSqMWWNi4qSiu46OO66mkSgOkMbfUhshnwhXCLeUH49t+V8DpZlApUYSDHi01Xr4QrU3P7Ga32+GJBpWsOj/8UvNDlETlb0aLFXadtqETMWnfbTaTNp21zmxhplKSlb4Vrq/tqVRVVRXX8SQ7ySQcnlKJH8xX4q+Jm7FoxWokO51Xax7vDkSwo/Np7F79IFxWrSVFZ2CIRINK+i0HlUwWq9reEKuX/9shYyNqDoNKRG0oVLFeHVdHtL1GsfI3Z2ofdbqkZGPcxkYdx+fz4oj1T+Ai3+sYkmVUl1miwSUJNknQiRKDzl+bYWG0NR1UUplKvmhQCQwqUfIFlVJtWpBcRMza91t++W94zvQwrsr4kdkRSaSwsBBOXxj+iLYxG+lMZUVd0D/LV+Nl+yPoo9+MoWle/LxI27mZzHzRcsDSgBnrHj4els9vRYbNhM7C6tOCYkaz1jZhS0r02qRAS9asafdxEW0Jg0pEbaikTPsiq04fVK/R73MbBmI379N4035GHEdHHcXrceOFcaW4Pv0z5FqjwQijHnvpF2KCZR4sYe7VTxS6oLYx7ArokJKqNVNvqqeSJxipCSp5g2yGSsnD5XIizaEFlYIwyOwD6nRKoASHGP7EaPMalr8lkaIiadJtrZnVVh9iQDGeFq5aD3OdfjrVm1Yi2VWVb1bHlebutZMNdCLGCi3brFt60zu66loS6Ibfwztgs4u/Syi+tF3oRNQmLvZfDn3Ii8zw7HqXe4IG+JGBYjf/6CeDSJ3sl4hBS1+2mgy4z/g8+pg34zCbtlFG8WcIaO+VK6hrNPNbvfK36G/27rYw2FGJki2o5LCaEIzo4dHbAJ1OXW60aBlLDkOQ5W9Jlql022m7IU8/T503RUt1KD5Cm5YgXVe7/pldWsZ8MvNXa/1Ny0JW3H+QBXv3qYJp/c8IFIxBZ1Dm9GFleRjmAVpQbEuedh+GFaaR6BUpwWUdMjqipjFTiagNlQfNKEQOHI76G6dWvZatUunRpi2nri3ir/2B540YazKVPND29ht0LJ9KFIboXnZXQCtVbYrWqFsLJZ0yIhs90rbc54CoK5G9/Ct9GRjoexX/yXmt5nKjVeut5ND54HIzWyWZMpV2Sq/9jrPq/XEdT7ILFC2sdz4nUopkF3Jpr0Gp34Rdd+yDsQVA4foV6AzC4TCmzvJgwONOVIy6cqvXz4v+Hqnyc3cXxReDSkRtJBgKwwetZrsgu/7GaV+bG1OMr+AMb+0Pcur6QSVvxISqaC8ei1EPb/TzYYo2hqb4Kwna8L/Q3vh8pU7N1Nhc+dunS4P4Rrcv/L336/AxEsU7qBQoXYe+nhXYZ4f8msvN0R5kdnjh9HKHSTJlKuVbaiebsAcqEYnwOy1eQmX1eygVmDgbn8cfREXEgRKfCR5ThrqsurIcnYGUEmcfcRV6//djfLN265O65Gdpv1vc0R5nRPHCoBJRG6koKcRD5mdwtf4t9MrTZnuLybWEcY7xSxwa+Slu46MOFNR+CHhhrpn9TafTwRfNVLKYdQgEuBGWCM494kC8XL0PLvvMjZSUpsvfHA4Hvl0VwkzvSAR67tXhYySKJ6ezGv5NK7CLYT0mjqwNKhmi5W+2YDU8Hpa/JVNQqbtJe79XVRmwaPFSlj/Gkd6glaPG9DeVwReo7bGUjH7KOhE7+57HDStGwhnUgi1BTyU6A5lNUXqW6XR6OKxbb5UwJm0TfrVcgtdTHuuQ8RE1h0ElojbiLlmF4wyzMNHwE7p1y6u/0KTN4JAKFxBh6VNXFwlpASMpd7MYa/ce9c7NVMe2sIczwCWIcQOykVK2DJGgb4uZSuKnQF+c+spfmLu+c/w4JWqrTKUD+hlwXuo3sM15uubyiEkLKtkjLnirO0cWAG1/aY6Uv3UzVKnzh35gwcyVIVRWMjsmHiRDLDcvS51e79gJf4f6499IX8xfsQ7JrCLaaiLsqYYrqG3qhrzaZzbRVVdX4+GC7/Cx+Sb0r/p1q9fv3q07uuvKkW8ohzfAvq0UPwwqEbURb3mROt4UsCMvr1u9ZWajtrfBoItA56+Oy/ioA0WCNeVv5jp7ETPTtTRsmz4Ij2frac3UAUIBuJ3aBlFzjbqlp1KmFSjQb0K4ZCkqWepDScTlcmGHHinYUbcCusI/GwWVbEYwUylJlJSUID3FBqtO+xvojPboLi9nUDFe70eRsaea/cs16Hgc+8euuPzTapRvSu5m3bGgUshdBWfs69rXOWaAq6qqxGBrGUboV8Gh23q/stzeQ7TjcCn8PjbNp/hhUImojZg9m9RxkdfcKKiUlpoGV7SJns7LPXpd3bIqEy71X4YHQqeqsrcaRq2hos2kY6ZSAnD7Q1g98wm8N+IbvHyMFWlp6c3O/nb0EBPest+Pm42vwRdktiElV/lb7uBR6vTKqtqfjRF7Dj7d+TVY7vPDxSB5UpAspd55WvC9NJIK+wn3ot/Eq5mpFCdLVq7Ea/qjcKL/VphHn4l+vlVwzvsSJRu0KemT1V3Bh/Bi+fkYUDYbnpCWLa6LzvTaGTKVHHptx6Teok2GsCXp+YPUscUQgWvzynYfH1FzGFQiaiP9jNpsE2uWLkFOTm69Zenp6SjzaEElvY8/vrq6jR4jPg3vhR91o+td/mfGYbhq4VDM3JgKr5cbYfFW7PThx0Vr1WlvEM32VJLyt9jsb1YE4A8xqETJVf6WatI+/+FodpKi02Pqvyb0vuZ9+DL6xG+A1KH9lGSDd2MwBdm6aizLugJvDPyamUpx8u+qDepYF/AixWJE3779YNQDhauXIpmll/yJA3u4oHMVI6i3qqxxmUynswTxHQYtQ8lkbfo3SV06kw0VPm3nZcW6Re0+PqLmMKhE1EYCZdrGaWXYBpNJm+UrRqYql5koRFV5cVzGRx3HF/3xYtbXb6D5wPIe+HDALVhmHMxMpQTgDYZh12np4k5/ZAs9leyIZtPDqvMzU4mSisvlREosqGSuE1SS9SE6EYE/zJ+TyaCwcAN+WrQJ5/69K/7c6wV1WYbRx0ylOFleWAwjgrBFtN8TZ/TeAPdNaRgRqi1TTTqRSM3OW2fYgnedO2Owcxqe0p+OztKoO1b2ZrRuPVNJlAa0SWA2JXmGGsUXfwUQtZVqraeSS994w1Q2VqttPdTp8vLNHT406lj5gXUYO+cmXNZLCzTGWGQXouxZMprhdjOoFG/S1NIGLajkCjTfU0nK3zzBWKaSj0ElSrpMpRST9pnXmepv5FwWnIGnTY8i38rMy2RQVFSojvPz82FO1Wa5zTR4makUJ6HKDVhiOROfp9yhzvtTekLivFmO5N28kzI3fTjaU8meix7lC7Hu4eOwp1n7jd4Zyt/s0SChPaXpkvyGSkza9sWGUq1igigekvevDlEbczm1H1Xu9P5NBpUuKZyA3bxPY1nm/nEYHXWkvTNK8dreq3Ck79N6l/dECfbRz8POOUFmKiVIppID3jqZSunNZiq5Y5lK8MPPoBIlW6aSMTqrUIMeH7sH/8Thht+Rawf8/q03laXOn6mkM9vRrXtP2KJBpXSdGxWVDCrFg8VfqiaAMUazolMLhqnjfqZSBMPajpBk468urp0oJTUbadGdRRIc7wyqq8rhMGg9ldLStMldtmZ1pAd+Cw/BJp+xnUdH1Dx++ojayNG+O5AOJ/INPzZaJl8MG1x62JGBSv7u7vIkHV0x2+tdvldgNk4yP4t3dshjT6UE4A2EYYuWv0kj/eYzlWzwRHsq2fUBmKMZZ0TJQDbGHIZQk41j/Xo7EAJSLQa43S6YzdpMp9R1M5VeumAEhug+xdKF3SAtgmUmOJmxijpexoZZQAEQcWiTwxTsuBewBOin24Tf1hdhaO98JBt3VYk6LoukIjPVgcEZIXxyig3dAx8BuByJzueqxKpAGLnpdkRM9X9DNucj41H4xd8b6e71uLrdR0jUNP4yJmqjMhoXrChEDrIzMpvMVAp7tb0kVdIRmLo0i1XbsKoOa3XuMRFDdPY3A5iplAB8wVCdTKXmG3Xb7Q54oqttriWEU3ct6MhhEsWNZB/J4eyqCzDE+xLK+x5Vb3nQqG30OCx6eDz8m5YMmUq7pJRiD/1ipFgt8Ee0mbWCHgaVOlogEECaTyvpcuRpGfL27kPgixhg0QWw4t85SEaeKi1TqTxsQ1ZmFgwpmZgw2IReBm2G5kRXXOlG/8eduC98EWC0teg2+ZlasN8TXR+J4oFBJaI2UOLS0o/Cfi/yc7WU8IZBpd1MK3Gr8WUM3PB+HEZIHSUUCsGRmaNOL66s36g7YowGlUwRZiolSKbS3+FB+KqqH9ZWab2Tmpv9bZMzjEfnmODe5ZIOHydRPGciUscLvsWhw3sjO71+4DVo0DZ6Uix6lalEXVckElGZSt2N2vtsyeyJamiN2/VeNuruaOvXr0PvaAtPc542rTz0BqwNZqmTzsJ/kYw8HreaGKc0YEVmZiYCVm1Hr2TUdQYlPgN6X/c//C+8c4tv06eb9p779fV3ZBJ1JAaViNqAu/BfPGx6GhfiPXTvrqUh15Weno5Bps042/gV+pXPissYqWP4fD7YjFowKRzd4KoRTWW2GyLcq58AdumVjuCIs3DMB8Df5Q7odPWDgHV7KpV7gdt/CsGzy8UdPk6ieIn1IfH++T5uOmQHdEutv9FismlBJqtnM9xud1zGSB2jrKwMIb8XuQbtM2HPKkBZ+s54dZ4f5Zu1Bt7UcVatXoMBvbur04GU2uzZ9SEtwGCqrj9RSLJYnD4OO/uexwlrJiIjIxOW6N8oezQrOdFVe3zQ6Q3N/h5pyqi0CvxquQRfZ9yLcCQ5e2lR/DGoRNQGQiVLMdEwC4eY5iIvr3FQyeFIQZk7Os18gGniXZmUtdlN0TOm+kGlkX20z4bFX86gUgIoyLBhpzQ/vGvmNdukW8QymCIFo3Dum3Px7M+ctpeSg8ulZaW8cJQVqTOvhN5ZfwalblnRDdjixXC5GFTq6qVv+dlaZpIvYkRmVjcsGXw5Jn3kxfwNfO872r+r16N3hlbuFEmrDSot0w/C/0J7Y6GncdZ8MiiPzqoR9lRrQSW7ls5l0oWBUOI3Ne1rLsH/zDfjXuO0Ft+moGcfdNeVo4euDEUlzBqk+GBQiagNhKq1Wu2NXnOTQSXZ41CxaYM63SOaOk5dOVNJOx1pUA+fn51RM4MYeyolhupog9nm+inFMpVEvwwgWDQf60qrOmx8RHEvf9PpcewgwLrkvUYbZZFoT6UUs06VnVDXVVS0Ab3ztL+TxciE1WxU5UWispIbsh1tWWEJfgsPxZ+hgYik9665fGPuATj9Ux1+W1Q/AJwsKj1aUCnkrlKfT5ujzg4jf+LPACd9HkfqV2Kgbn2Lb5ParZ92rPOgfNOadhwdUfMYVCJqA3afFlQqcumQl5fX5HX8fi1TyRLkBmlXJhlIL28aiOsD52NN+u71lsWCTHaTjj2VEsCSTU5MWHwJ3DelYcfuzfcikJ5K4tf95uBzy42w+ks7cJRE8S1/M6ZkwGHSSioi5vqzvzn3vhljvxyMO34KsvytiyssLERBlpapVG7Q+gZ+viaIfhdNg3nIPgiHtd841DHWlzlxXfA/uCx0HcIOrQxO7DKoN5xzv8DGf2YjGV0YfBVv+a7A3uWfqEylzIx0eCNa+njIm/g7dY3QgmI+mVmzhSLm1JrJRPwbl7bX0Ii2iEElojYw0KhtZK5bsQzp6Vo2SkMhs5aCawxUS8fLDh0fdWym0m/unng7tD8qU6PNM6MKdd0w3XMQ7vynG8vfEsD/FhTBFNYyy4zRvgtbCip5QtpXpi7A965DBb1I+X4yzCu/ivdIkjKolGqvzbhsOMX1T+u8WL/vFHQ79X426k6CTCV9yIvN4TSEU3uqy8aXzsDKvGtx046FqK7mDrOOtNmlBR+6p2kTgMT07dsPeh1gdm2A35P4mTltLbPiH+yZXoxU3yaVqZSV5lCzM/siJng7QVDJrAupY3/DnpxbotOhLKDNOuzatKK9hka0RQwqEbWBcJWWZuzUpTbbXC9oy1XHBoSgC7gSYiaXRx99EJ9//mm8h9KlSFmbzqRlvViM9ad3nVNmwh26c/BZ7tnMVEoAwYAXpugPOKOt6WCwkHVa+ip5gtq6rQvyvetIlhWfwbbwVaR/cS4D8h3M5XIizaFtrATlJ6Oh/gas2ahX5XHyN4+B8q6fqfT67A14PHwu8k97SV2mt2o7yzItEZSXl8d5hMlFkt/l92SfnOgUcFGSLb/mmkwsv8yBpX9/i2Sj95ap4xJ3RGUqpaemYbTzEQx2v4BqR18ksmAwCJtRy/gLGlqeqSQqI9qOscpSNs2n+GBQiagNGNyb1bHfojUtbYo5NVPtKRFht/alF0/Lli3FPffcgRtuuCbeQ+lymUqjij/DviufwNjajHTFKhtgEpRQG2AsFYm3sL/2PTA7tN4gzVFBpVA0qBRiUKkjbSzXprUXhoqVcR1LMgaVUu1akNwDm9ojXlf3yrmYanwWF2X8wkylJAgqiR498ms+Bzqb9nczyxJiX6UOVFVVifN6LMVSyyScHXyz0U6QjYZ8dXrd8vlINv7qEnVcHrKrTKWUlBSsfvRUbHz8pEZZXYnYw84e0r7vbI76wcKtKTb1UMdVvsRvRk5dE4NKRG2Q8aPza2nfvpTaZokNZTjsOMx/L3bzPo0Ko5a1FE+lpdoXb0UF9y62Jdlb/9CuRXh5x18wRLeq3jKrMYJddEuxb8o6+NmoO+4iAS2o5Avp4EhrPlMp1qw71rNAH+J715FKnbXBP9P6WXEdSzKWv6XbtJ0hHl3jcox0fxFOMP6Ifa3L2VMpGcrfrCnI666Vvgm9XQsqZZr8qKhgUKmjrFmzBr0dfhh0EdhTG+8QWR/ULjNVJVnT5kgERr/2OSzXZahMpdTUVCAchN/vVzv9EllVVRUsVWvV6UE9G+yV3IoiUy/8Fh6CQn/rMpyI2gqDSkTbyeUPYXffU9jL+wT89tofWw2lp6ViuScNxchApT/+JRyx/gdShiUpt9T+s79ZDcAHltvwdrcZ0AWSr9dBoomVobqCeu2H5xZIX6XoTMVINXB96UgrHbtgRVjbCxte/WO8h5N0QaWU6B80n75xUMlg0Rp32/UBBpW6+M4zyVT67tL+6DPvdvzx9+/qcqNDm7Y+0yhBJe6g6ihr165Bbslf6nRGt/6NlpfptUbqWcGNSCYRvxNmaN/P5S6fyjB2OFJww1gzPjrJhvDyb5DIqqurEQgBZV4gYqkza10L/JY5ESf5p+AD987tNj6iLWFQiWg7FTv9CMCI9V5LszO/ibS0NIS8WiChyhtMiC+vuiUO1HY9lWxWbc++MxiNLkVZzBYEI9qf3Uid0iuKD300U8kV1G01qCQ/Tt1uLUPp6jFaaQF1jEJ9Pq4LXIiqiB3fr/UjFI5/UD5ZSDnGz5W5GOqdjntzH2603GjVgkoOvR9ulvR2WVLaJuWNo20bsLt+CRw2rYzIkqKV/GcavMxU6kBr1qxGb7tW5mTKapwhH0gpUMcFumIkE3el9nxltjeTwahKAY1GI8aO7Iejh5hQuGYREpns7L31ex92ezcX7t2vbtVte+dq2dZ+ffMz2RK1JwaViLbTZqeWThtyliIvr1uz10tNTccE/S+41fgyzBtmJUSabd290dQ2JPPLYdW+1JdV1p9i2WoywgNtmS7IEqp4qwqZ8G1oZ/y00YrU1LStlr+9+U8A81IPRjBzQIeNkQCnP4S/IwOxs28arvCej3+KOMtUR5HvhkBZIQr01RjWv0+j5eborIkOeOH0JHZpCW07yVJKt2gZaSIlSwta2NO0jJgMOFFREf9ekclizZqV6JOhbcKFUrX3oq7U/CHquK+hGOFw/d8hXZnTWYnySApKIynITK/9TvdasmuWJ3pQKefIaxE+5j58vKB1WWYDe2rbHxFLSlK955Q4GFQi2k769b/hYdPTOMM4E926NV8DnZ6ejv3Mi3C28SvYS+YhsTKV2GC1LcvfrNEf3kZz/dp2q0kPL7QsJs4gFn+Hjh2Hy+YPxaSPfC0IKtnw3F8B/Gg+CKGcHTtsjARkV8zD/vq56AatvOaH5aXxHlLSkO8G37p/cGBaKU4c1ThDz2R1qGNb2Ak3Z3/r0v2UCrK18seKiAPZGdrfy4zs7ljg64l3FnhQxfK3DrO5ohJ2k9YsPZzaeL3sN3S0yop26Hwo35Q8fZU2WvpjlG8a9tg0GZmZtRPneMJa1njEm9g7JGRnr96SgojJ1nBOhK3aMd2D3ywX44+MG7Bxc3JlqFFiYFCJaDuZy/7FRMMsjDUv32r5W5lPm2Je56lImJ5KsRIHahtejxs2nVbeaLTUDypl2kwwGJmplCj2G5SDwKLvVJahrJ9bYrc7oLM48M6mLFzw9jzVY4Q6xj4lb2K6+UGcnqWVLsxbtpyvfweR74YJg404OvI5LEvea3wFc7T8LeKG35XYG2y0fZlKvXK1rLTNyILDHC3tNtnxpu54TPrIi/JKvv8dxaDXMlHKdBnS2KzR8t59B+IV9154IngM/l2vTcqSDMqjjQ9DHqdq0h0j5XBKtI9iopKdvVMLvsXrprtR4PqnVbdNy8hDN10FclGJ1RuK2m2MRM1hUIloO+lcm9RxkVu/lfK3NGxcvUyd7ufwJ1RQiZlKbSfsr30tTZYGjbpNBpjNWi8KfZilIokglrHXkkbd6eYIdCEn1qxfh0CIQY2OYgpq5bk75Zrwk+UKvOW9CKs2xz8wnyzlb7v1smKo708YirTGwHVFTFqmkmRNePg90mUVFm5Ar2xtJ0m5QSt5i0lP13q5lJczU6kjSGlTadCMj0J7Y5FjjyavYzabcfvPBvz3jX8Q8EVnmEgCFZ5oUMldhczMOkGlsKleH8VEzlQaZd2IMYaFcOhamc1uz0YwooNeFwEqtRnkiDoSg0pE2yk9oKWZFlaGkJubt8Xyt+JirWzDHqpKiC+vSSNNGNvbwJ5Kbcjl8WGydxLuCJwBs0Xb4KorNiOckUGluNvw7ZNYfq4b0460t6in0u1jI/jRchXONn4JX5A9CzrKwDTttR40eCTSDAHYdT4sn89Z4DqCTOKQM3CEOv1vaePPfMSaibd73Y/Ue6tQ7WZQqasqKipEzwyzOu001Q8qfefvg34XTUMpGn/fUdvbtGkjluoH4MrApZgz/LZmr9fHWA3vqjko3rAayeII/xf42DAZx5bNqJep5I+2HTCEPAmfGZpiCNSbBKHFdHpU+qOb9ZUb2mF0RJ04qFRaWorLL78co0ePxsEHH4wPPvigZtm6detw1llnYeedd8bhhx+OWbPqNz6ePXs2JkyYgJEjR2LSpEnq+nXNmDED48aNw6hRo3DjjTfCw14AtI0GmLS9c5s3rIPVqmWhNCUtLR1lHi27QeeL/172zOAmvHyMDT+d7eDsb23I5QviTRyK6aHDYDFHU67r+DPrKEye2wMLkicjPSEFQ2F8PX8VHMYIjEbDVoNKMvubJ6BtVNvggy/EoFJHMQS0bDJrSgaKs3fXLlzzU3wHlSRkh0OKSfusR6KlbvXo9HimsABZl38Ip6Nxw2DqOplKfq8HxciEJbv+bGN3hR/GyrxrsXsKS246auY3Q1quOt0zo36JfV39+vZFrzQdAmt/R7LIdC3HCNMa5Ec21ctUCujMnWJnXlVVpZpJUxgtKa3/ex3SnmegUqugIOpICRtUkn4Jl1xyCTZu3IhXXnlFBX7uu+8+fP311zXLcnJy8P777+Poo4/GpZdeqmq+hRzL8okTJ+K9995DVlYWLr744poeDF999RWefPJJ3HHHHXj55Zcxb948TJ06Nc7PmDorvUubocFj1FLAmyM9Wyr16eq0qyr+jWbLq7U0YE8gwkylNuT2+uo15m7oslWj8eaQh7DM3fofDNR2vMEwbDrtvar2hrZa/ma32+AOaN8hVvjhC4Y6ZJwE6P1aUCliSUXK4P3V6cPti+M8quQgOxxSjdEAalNBJRVX0kNnMMHHktAunal0z+fr8MPwx7HDsbfXWxbSazvTbNEJKqj9g0q56TYYEEL3tOanj9+ztxVrr0rFSeavkSx0Hm0GwhJ3uF6m0jf+nTHI+wpu8p2NRC/Hd+i0oJIpOrNma7h0WpDRWcWZGKnjJWxQ6Z9//sHff/+Nhx56CDvuuCP2339/nHfeeXjxxRfx66+/qswjCQoNGDAAF154ocpYkgCTePfddzF8+HCcc845GDRoEO69915s2LABv/+uReslSHXmmWeq+xwxYgRuv/12dVtmK9G2MPu0P94he/Olb0IyIaptPbXruuLfe2BjhRZUsqleGGzU3VYMfid2mnMPjtP9DKtRa8xeV+yyiN6EQIA/wuMZVJJp0IUvBFgszf84j5W/Rds1wKbzwx/kBnRHCXm1v0+VIStM/fdVpzMq/4HOz2B4h2QqGbUAqq6ZoNL5kfdwv3EaupkTu18JbbvYTtv8fO03TF0+k7azLMWgTVBB7WvFmrV4M/1JLLGciX5VfzR7vVCPkeq4u6EakUBybN9UlmkZOmXhlHqZSv10xVg+9XgMd85FInNWV6oZ+4TNseXs6aaUGrW+rhV+rovU8RI2qCRBI8kw6tWrV81lO+ywgwo2/fXXXyrQJOUIMbvuuivmztX+WEjmkZTM1W2wOmzYMLU8FAphwYIF9ZZLQEo27hYv5p5Pap3yaifM4egP6bQeW7yufA6XloVxkO8BnGm4D/G2sbq2WXjAyb0abSUvsgmf7P0PbrK8DoO+8ZywfYylGKVbhl7peng83AiLF28gpMrYREC35YCSkO+bWKaShZlKHSYS9MIU0f5WeQwpCKf1RiitN3ThIEyFv8V7eF2a3+9XB0c0WKBvpsfHfuHZOMn4PXLMDJJ3RTKphxz09nRk53VvtDxs0bK0081B7ijpACuKStFTVwKjLgxLRuP3I2bQoB1RGbGrxs3OTcuRDFyVWo/TKmt+vUyljNQUIBysmZgjUQXclTWnM6IN8Ftjra4nfg0PxWb/1n/TECVNUElK22Tlr5s9JKVwwWAQxcXFjaZuz87OVsvFlpZLc2Kfz1dvudFoREZGRs3tiVpqswfYwfcydiu/C/asLfeT0Ol00EOP5ZECrPHFv/TpuBG1Ywh54t/jqavQhbTsl5CucT8lcU74A3xouRWnD/HD42nl7B7UtplK0dlVQobme6HVy1SK7vxL0fkRDDNTqSP4ghFc7f8PpgTOhM2u7bn19xyjjufN/rRmth9qn6axoqZxbDM9Pvx6bfIBqyFc02aAulaWkkEHrLgqC1WvHIO5K9fXW65zZKvjTCtQWVm7UUzto3r1PGTotKb44dTmf3cOHDAAKwJaU/XS1fORDFLD2iQ4mys99TKVChxBvHqsFWdk/IpEFnRXo9wTQUhnBFrwu6Shj01H4mT/LfjcNaRdxke0JUYkKGmwLYGfO++8EzfffLMKFL300ktqmew5k+ky65LzcrmQQFRzy71ebSNiS7dvKV3jJIROIzb2zvwcEkGJy48gjFhfGcbhfbpv9fWUaZeFKxCB/NPH8Q1I89T+MAx7q/hZaKP1IxLSNsC8OitsTdw+KD8UgoDDpIfP5+HrHieSaRTLVAobbVtfd+v0VNq7tx1V+a1PTe8KOvq7wxU24IPwPpCHu9RmVo/rH3g4vlpeibc3DcYhq8owYZiW8k9tKzaBwwnV1yDNbsaT+Xs3+b4HDTbtb5pZh0DAv9VS0q6qq/6uKiragO4pOvQ1lqJnpAJz0zPqPUedTdt4zzKHUFlZjtzc+rPDUduKlK1SxwFjCmBJVX8bm5tx+G9vGnYxA9Xr5kOnOw1det2IRJAWqYK8IMUVbhVUqnk8iw2njzCjLFSGcAKvn6tLnMh6oBqffvIV9mgi031rslKsgAfwhPVd7u9QV6ZL4O+O1owpYYNK8qPk0UcfxZVXXqlK2yTTSHoqSX8kyfhoGACS87GZt+S2TS2XRsmxHztNLZfypNbIzm59E7VE0xWeQzx5Vmq9kULOMtXfKydny69ndqoVxxo+U6nLtvCOSO3WD/EgwdXUiOzp0vr7GCK+rY49GW3L+qGzaH9HNofs2LWp19Rkg8QybGY9bDYDX/c4sVT6sSjSE2m+MriNxq2+D92752BxSRjvrc3E8Ycfm/TvW0d9d1RFtMBGisWIvNxoIC/nKKwsG4JfvlmGtLUVOGvfgR0ylmQTS952rl6A8adMQr++3ZGT2njveYUlVf1NS7HI3zQ9srK4bnQl1dVlKMjUfjtvRgZ26JuDNGttJm5GXg9gBZAOWVf9Sf+3sb1/uzmCMtGLHbqM3lt9rdcHtL+ZVufqhHhf2nPd8Hmq4YQVGREXNpdVY+DA3khL0x7PktUd2ADY9UFYE+B1aI7blIaCy9/EgwuB7ya0fpx9emQCxQEEdKaEeL8pub47EjaoJKSJ9rfffquylCTi/PPPP6vj3r17q9N1lZSU1JS0devWTZ1vuHzo0KGqzE0CS3JeggBCSuoqKiqQm6tN0dlSpaXVEhjvlCTyKB/ezvwcEoFx8Wd41PQxvrLqYLONQknJluu1HVYrTtZ/g4GGjVi+/Az4DPHZoyfrVIY5VBNUqnJ5tjr2ZLI964cupGW/+GFu8jWNTW0rWWtFRfJ3i697PKTpI/jNeiCue6sQ+/S2b/XzL61C5m4M47bfU7HfAycDSbq+dPR3x6Y1q3CAfg5Cprx679Hu+dqPr++XFGN9UQWspsZN8Wn7rFunRZVM8z/CrdPvAHwBlPgalxvqzQ51bPWXY926TQiHmy797eq66u+qpUtXoFeulD76sRlZMFd7UOKsLd3uXTAIP260Y9aydei/xwYMHJicfxs7wrJlSzF4xx0lpwVVlh4Ib+V7qDisZZFleDfE9TdeR6wbm6t9ONw3DbpwAF7v8fD5UPuc9VoPXqsugJLN5YA+MTd/Xb4gbLZUOH2RbXq/hvn+wR+WR7A0R74vz2qXMVJyfXfoomNriRavVZMnT27xACSbaHtJkOeiiy7C008/XRPs+f7777H77rur0rhp06apiH0sO0mad0tGk5Dlcj5GyuH+/fdfXHrppdDr9dhpp53U8j322EMtlwbe0ldpyJDW1aDKG59ob35rdYXnEE8ZFQtwsGE2Nlv6Ii+v+1ZfS5kBriJcrmI5Pmd53F77qqpKZGfKHiwvrg+cj99Ll/Fz0EbrhxHa9Nt+naXJ2w7pmQcsBiySIeP28nWPk7wUC3r718O18DukDjtlq++D1RqdqrfPOFzx/j84Zdee2KNPbc+GZNNR3x2mjXMw3fwg/g0PRiRyas3lg7OMODxlOfzuSvy6egj2HciSm7ZWXe1UpTUP7euD/ccpcO15A2CqnSAlJj8nGygBTJv/hdvtSfq/aV3td5X0VCrIkvfdj0pDNgqgq/f8AnkjcfO/Q/DTT9/j6ePj97smGaxZsxoDektz7tXYrMtD9lZe6ypbbzwTPBIrAzn4b6Rrrxtlbi3gHXQ7o/2Uaj+n9tSs2iv63YhYEq98Xfr97pYXwB2me7BBPwCRyN6tvo+M9HTk6qpQZnAgHI6oyh7qPCKd/Ltjmxp1S5Dmww8/xPLly1XJmJSVrV+/Hh9//LEK2rQFyShyu92YOnWqmgnu3Xffxfvvv69K4CSw1KNHDxXoWrZsmQowzZ8/H8cff7y67XHHHYc5c+aoy2W5XK+goKAmiHTqqafixRdfxMyZM9XtbrvtNpx44omtLn8jMns2q+PCqjDy8rbe10PWlc0btdvkGeM3xas0wc9K1/YuV0YccHH60TZj1IXqZSQ11Keb1tTUGvZw9rc4czq1pp6pqaktmv1NmtVmZmVgxaplKKpkk/WOEPJqe2t9eu3vVYxl/c94OjgFtxhfxXfLpRyE2prT6YTdrMdx/Vywz5+uNtKaEokGmlLMOrjdWgNh6joKCzegZ4aWfeY0N53RH2uKXFnJST/a0+rVq/GvfjD+F9obwR61s1g3p6BnP1z95hLMnrsWXV1s0oaQpwqZmVmNgi2BiJbNGvJqJdWJRiaS6pmmxzjDP9gxsmKb7qNbfh91nB4sqemJR9RRjNuSfSR9jiTrRw51vfDCC/jll1/abHCPPPIIbr31Vhx55JEqKPTYY4+pkjghGUw33XQTJk6ciD59+uCpp55Cfn6+WibXfeKJJ3DPPfeoy0eNGqWOYxHbI444Ahs2bMCUKVNUL6Xx48fjuuuua7NxU/LIDmtlloWVPmRl1f8Sa65xYsmmCqCXGbZQlfTTi1tQqcCkBT/KnD54y4riNJKuZ3Z5Fu4KnIagIx8jm1geic7oYTMBJdGJA9qDJxDC/MIq7FqQDqMhYSf6jJuNVV5c73gT/702HS9FtOyyrTXq3iFHj78HPIWySAreCH7TIeNMdoPTtL9TffO7SS/oGoH8PRDWGdFHvxkrVyxCMDwYxm1obErNk42STLWzxIVQRAcYm56NyLXb1Tjw1o8xd8lqvO1moLyrkUylnn217xCvtf7MyuqyQAjLBx6HfhcdhpLyNXEYYfJYtWYNvkw9GJ8HDsXnO2o7yrdk6IA+8K78ExtC9VuCdEWjfH9gZupUvLZoEz7PqL+TNzszHW5YIP/7PdWwpiPhVFdXIdWqbZYHZPKDbZCdp80GmGn0Y0NpKVJSOnePHupctqmoVMrQJLDU0IEHHqiCOW2lf//+ePXVV5tcJoGk1157rdnb7rvvvurQnAsuuEAdiLZHH4PWqLuqvKJFWXpS/la2Wstt1Hsr4rpHZFUgE0NQgRdSp+G2nOToR1LtDSLFYmjXlOC51RlYGDoCu9ZpZFpXoXUg3iocgp9WrcM4b/uFFW/6dBF+WlmGc/fsjf+M6dtuj9NZzVxagov0XlgdEVjCW/+FabPZa2Z/syIAf2jrgSjafuaQFqSwOrQ2wDERcwqC3XaGeeOfGGdYiI1Vh6Agg9nGbR1USrdLxqULbp2t2WlgZhUBRePvR+rwZSrDnLre7G+VzlSU6HOQntf4u8Ri0GF2yrVIT3Xj6uKT4jLGZLGyqAS6dAP0CCPb0XQ2dF19+vRFjl2HgYYNQPFiILfrTjWf7lmLnoFFGGIK4JfM+s8zMy0NrogV6To3PK4KNB0ej39QSSY7EAFD4zLjlghbtYxBi1GHqtJCoA9/+1HH2abd1/369VOlaHVFIhG8/vrr2GGHHdpqbEQJz+TV9v4ELC1r8i7lb5UGLaOprEwrg4vXl9fZm07GS8FDkGYKId+mNZfuyv5cW4EDnpqNp2atbtfHCcoefQlCmJsO1L1TmIV7sqbgM8dRqi9ce5GAknhrzoZ2e4zOzCczhuq0dHlL3X4LWyh/i2bXwwo/fAEtg4baly6glb9FzI17YAQKxqjjq/sXMqDUTuVvqXZt1i+vBJWaIbGmiNkBgy2VQaUuxuVyaT1O316HytN+wM4Hn93oOjq9Hn5oO1ECHjbpbk9F5dXoiWJkm0PQt2DnWPfuPXDnkQWYeYYNm398Dl2Zzqvt5C31RJCRUb/fYUpKCg6uuhmDvS9jvaE3EpFUEKRatPc0uI1BJRht8Ie0+yjdtL4th0fUPplKUnb2n//8B19//XVNEGnhwoVqA0lK4IiSQsgHa1jbdx5J3Xo/JZGenoH1GTL99TyUlxVD2i3GK6ikt6bACW1DwWYxqcBwV27q9+B3y9Xxy7+vw6Xj+rXb4+RX/IXMv4tx8O5Nl9RajVosX2+yqP507a1fEgQMt0XIX/vatySoJJlKnmA0y1AXQSjAnkodoXBzMeQv5gaPERkNlgUKxgJ/Pgbz+tlad8su/PcrbkGlaDmGR2eLfls0ludarHpbrUy3wO3WAn3UNRQVFapjhyNFZVo3pwoO5KIShiB7arUX+Y1mizjxs/UKuGGDK7J0q3/zJIO+KGNnAD8gWKr9BuqqSoo3QjrvSXl6rMdXjMz6vXz6fxHwOJF50t9IRFJBYI9ov0vMNpltcRvodKjQpSIPVSgqjt+Oa0pO25SpNHr0aBVQmjRpEnJyctRBGmh/8cUXGDp0aNuPkigBrVizBuGIDr6wHvZsrY55a+RH2XvrsnCgbypey7oM8dwjorc6VDqwyMjJ7fJ7mL2BcIf86Luy/1p8u9fvOCTyU5PXsesDGKhbj2Gp1e0aVNrb9yueNT2C/uW/t9tjdGYRnxYQDkUazAzTDLPZDG+o9iszHGRQqSNUVGoZd2tcjfeBBbrvgojRCr2nGChZzAkH2pjTWY1Uaf6mGqU3v+c807MW5xq/wOH2hV3+eyQZm3SL/AFDEZA/ls1w6rSNYGOEfxfbS3l5GXqkaRnQlaacFgfRS6Ll3Vl+LUDYVW0u1nqDVtl7N8pUEikmIBL0q2B5IpKdvZYqraH6sD5aj+BtMS/QB7+Gh6p+qUQJn6kkpCnxaaed1rajIepE1oaycLrvZdg3zcVBec4Wl78VV/kQjvREr2D92Yw6ktW1Dn/mvIzueq2vU6o5otLcHY74jam9eYPhDpkS1mbSfugZbalo6id4t+AGzLT8F5sH2vGw99x2G8sb6Y+r4/6mdQBuaLfH6awiAW3j1xUyIDWtYQ5MY5LFZ7Y6EAzrYNRHoAvEb/bGZPKD7RB8UlaAIdm7N15osCDQYw+Y1/2AF995A4Gdz2X/sDYkG19pNi2Q6t9CUMlg1QIKDn2gQ7IvqWODSgOz9Pjm6FIsffpI2E97B/2zG/9OcOtTgDBgATdk28uaNauRu3G2NF6AI6dvi19pr0XLpO+OYlSEAoCh6X6PnZ01oP2eLXEGsEMTQaX/7GrGDg4rLBtmAUOGJmRQKUOv7eiKzai5La73nImySD5GR7p2Zhp1kaCSzJz26KOPYsGCBQgGg2rvfF3ffMNZcajrK3b6EYQRGyv8yNuhW4uDSuHoFNmV3vjtVdd5ytA9s7ZReKopovZK5+U1ntmlq0izGlHq8uPAwTnt9hherwcOu2R/heDT29BUG02DSSsiseqD8Lraf69uT0MpmDvQBL8WCHYFDUhNbdkMKTabDe6AB2kWHS7es7tsQ1E7+1O/E34JFWBK5uAml7t2vwZf552HaT/r0H95KYNKbUh2NMws7ok9vFdh357ZaG6OXFM0qCRZmC43y5+6Wvlbr0wjeulL4A1bANW4vTGfMR3wAymGaOM5anNr165B71TtW8eU2bvFQaWs3J5whyyw63wwVK1FKHMAuiJ7qEodF1f7sGeD8jex1w45mJDtxsyNiRlskQqCGz/z4ueME/DIxf/Z5vtxmHSQ/N7KWBNIokQOKv33v/9FeXm5ylSS5mdEyajEpX2lB52lyMtr2YwaaWnpSAlV4Urje8ivlEa/TyEe9P7KeudTjGFUu7r2xsA7Z42G0xds12nHJVMpNVsCjIWYVxzCbk1cx2jR9kDZJKjUjrO/bQimo6exEmsj3dB+YbTOyxPW48/wYGwuL0b3tOZ7hTTsqzRjbiWOPvJIpGzHnkRqOZdfa4ieYmn650qw+y4Ykh6AbvYvWF7iwvoKD5t2txHZ0eAuq0aGzYpevfs3ez2TTQvKykar083yp66ksLAQvXLkd34Qm5GJQdEeWw3pbBlaUCmsbdhT+2QqDUjXfr+EU1vWckEM61+AVUu7Y5huDbybl8DURYNKkXBYtaTYXOFtsvzNbcmW0BzcHlfCBpWyD7scf+WPw1dLSnHI0G3byZsqk8SEgGofJxOhThBUmj9/Pj788EMMHCjtM4mS08DCj/Co6Re8Y3cjN3dii4NKOr8TVxo/AAJAcejRuKQi6/1atlQYOrgjkrBuUtNHd3XNbZi2FSn9sBq0PYm6aEZSQ0aLVjpg0oXh97RfDtErq3IweVAllga7M6jUhJ2G7YIDX+4Dz6pSLLy2ZZlKDocdV3zpQ8/zJmEfR8uyE2n7DHLPgUUXRKqh+d8b6TYTdumVgT/WVuD75aU4fXTLN7hoy+VvnhV/4JQ+ARyxS89mr2eOBZXCTrhZ/talFBVtwOgs+S6rRoUhp9nJPIYO3x2fvvQO5i1eiqM9HpXVSW1r9Zo1GN+vF4BN8Nh7tPh2g/v3x/J/sjHMugbuoiVI3+FwdDVSMXOI/35EwiGsW3dWo0bdwhvNHddHS98TsVG3IaU3PHobguHm+5dtzemWH3Bw6Bt87NgRwMVtOkaiNm/U3bdvX5SVac0ziZJVQfU8HGOYjUGm4haXjUn5W1mlU+1NETpf/YyhjmKAVno3C7tiuG86Jq49qcsHlcwrPofz2/uwaM538LTTdPCSqWQ3aPetNzf9o7p3bu2PnXC0BKs9lLv86tihY4+Lpuyca0LVb+8jsHnVFmc1qks2lCw9h+KlJWFMm7263cdIwBTvA3jXcgeyg83PZGMq/A234xmcavgG3y8r6dDxdWXynXD8jkbsXfUhTGu/b/Z6enO0p1LEA7+bU8p3tUylggxtx5fLktv8FUeejmPe8ePFvwOorKwtrae2s2rDJvSJlr+1JlOpV6/eeKNkEG4InIffdSPRVTNaQ+EIwtAj5HE2mankj2ifY30oMQPfTmcV7s//Hs+YHkF338ptvh+HRY9cnfRnSszgGXVd2xRUOv/883HzzTfjzTffxOzZs/HHH3/UOxAlA5tf23jZUO5DXl7LshZkBiljyIcKv76mt1E8WEza468PptYEQBJ1Roy2sLrMjZXfvYB+i55E6s+3Y0Nl+5Ro+HxeWPXRoFIz5VHdMtJqGniHfe2Thi17udx6B34LD8Gc8KB2eYzOTppiCrvdAYNBm1GnJeVvjvQMbKhyY97a4nYeISESgSOi/TD+v+9+wFdffYFwuHEnK0PpYuy4+RNM0P+C+YVVqncabT/5TjhogBmDy76GaVPz03BHolmZFqMOvk4UVPr999/w5JOPNfmZotpMpZ6p2k4wn7X5nWeSwZSRoU14IO0xqO2tL6vGh+Gx+Dy8N/RZzZejNmS1WjHr1/l46Ll3kGLrmpOxlLu1/kFhv0fN8NZUppJfZ1HHxrA3YTOV9nWsxmGGP5CyHZ0w09OlzA9I82gzNxIlfE8lcfvttzf5xbJo0aLtHxlRguumWuEBpc5Qq3qLpdptKHO5kGXRq95G8fg5azdomUp+QwpyVv2Ef//8Ea7dT0dXtaHCiwO8/wA6YJR+Of6vnZqkezxePF+5F3pkWrBDSjN7EnU6BGCGWRpQtFMadoUngLt2WocMnQs3hs/E2e3yKJ2bd8G7WH9tBr5a0/LyU7vdjo9HzMUY639wt0dm1Guqaxa1maAHRp32F/Leu2+HyxvE4ME74NJLr8TEiSeoIL0IFIxVx6MNy2AO+PHTilIcM6Ll5SHUfKZSTj+ZJWmZ6hHXXHg6YknHjJQrcfMdd2LYXomZBdCU6667EosWLcTo0btjzz33ivdwEo7X60VpaSny7VogImRvfufZwo3VSDn+HljKNjBTqR3IpEil3jAeCZ6AwdkWvO5oXb+dvukmFC5cjw1rVwO774Guprt/NWZ3ewgf/7EKl+j1qtVEQ0G9FlQyJ2hQSXoqyQyawhid/GBbdO+WD2wEUvylbTg6onYKKi1evHhbbkbUpfTQaUElT6B1tc+qBM6jZQXpvfH58bXZZ8bScE8YHJmY1u0duMdswqzqI9BVFTt9sNWZK6XaG2i3TKVXvWNhDvbB02n5TV7HHwzjZ+sB+OXXX2v2rrW1KqcHA6H9cHLpUxAIhWEybFNiapf155pS7OMIIyOt5T/eJFMptr4bQon5w7Qrqdv7TQJKYunSJbj88otw33134cILL8EZZ5yJlIz+CDm6w+zaiFuGlWOHgsYbFLRtmUopRi3zMmLaQoaDTo8ZzpHQnfkqyld9gc7Sg2XNmlXq9ObNG+M9nIRUWKhlOmxyRVCWlYvc/OazY+xVy/Bvt5tRnWvBV+WJN117V3gvdClaBkqvrJb1AKyrb99+wLpfkLvuM+j8RyASLVntKuy+Tcio/At7poVUxpxe3/j3TsggM/MC0kU0UTOVHHptbEZry0rym2LL1PrfpZtDKhhpNLZvL1GimG3eypAP6qZNm1S9tRw2bNiAVatW4fPPP9/WuyTqPEI+mENaYCjcyoa9ElSqgPajoKqiOC4/ph9cPRjj/VMxK+s47JJWirG9jQi5u27KenG1FyZdbR8lTzSo19ZkNjedUcuesBibLqmq8gVxdsUkPLfDkyiqap+MqYrKsprnKz0GYjNoUS1LRAsKBXUtz1SSnkqxOKAhnJg/TLsSXTSo5Ie2h3ncuP1wyy13qHJj2ci69dYbMWrUMNx7352oztlVXWdi+nL0zeLMfNvL7/erQ0q0R5zOsuWN0ACMMDgy4A1ue4PZjlReXga3W8sUZY/QphUVFarjK761IPSfv7H7Hvs1e127I13N/id9XCoruu5viXjO/NYtOw09UYyeaa0PEvToPQDvnJqF483fwlC2BF2NPtpKosQdabKfkphr2Bm7eJ/FVVVnIBG5nFVw6Pz1Jj/YFpYsLaiUnWJCRQWzBinBg0ozZ87EuHHjsN9+++HAAw9Uh4MOOgiHH3447rvvvrYfJVGC0bu0prHyA9qa2XRGSnMkLdeZpu3xKy3dhI4mP6R10RnIUu0OeCPaD5Sgu+t++fiq6u+JDjjbZyPC5/Vi4PLXMbLoY3R3NP3Dz2qM9tPS6eH1t09Qqbqitlnxj+bLEA61z+MkW1BJyt9imUp6Ziq1u4oKbT2tjmh7mHv27InLLrsSf/31Dx5++AkMGDBQldo88siDuPbp/6nrhFd8G9cxdxVOpxbQSzFoUVSDZcsbOafrv8TNxleRbUjM6bobkh2hMRUMgmwxUyk/v/mZ/2LsaVoWjczUWFXOfnNtbe3aNTizz2b8bL0Cp2x+qNW379OnD1abtBk0A8XL0NVs2lykjstC9ib7KYkd0nWY98RFSF/xDRJRyKP1eRRWx7YHlfwW7flnWXXYVMyJKyjBg0oPPfQQDj74YHz22Wcq6+Ktt97Cs88+q37wXXnllW0/SqIEM2fJcoQiOmwKprS4SXfdoNK9RbvhwP9n7yqg27jS7p3RiMnMjh3mpOE0ZYYtc7e8Zdjilra47XYLf7vdMjMzMydpkyZpOLGT2I6ZJYtxNPOfN29kSAySLNmyq3uOj2WLRjAP7ne/e/3/h42ZR2M4DIpZuV9bo1LCCdkvwdc1oY02iA5acQ0j5EkMqRTwOvHLPmvxydi3kavh+ySVsmHDOKYRnJiY9jevo+v1aVgBjH/kmOcOBQRRhEaWwIdk886I299kJYYypVRKOOx2+j22inSMMptpW5tarcaZZ56D5ctX48UXX8fcufPw9Tbq5aPtKMN9Tzw5jEc9OhAObugklTT9b3KOEn/CBdxXyFGNDE+lhob6zssppVL/SqWMognw8/27P+r1JgREqs71OFMb2UQolUr09FwUTZEnv4UxeVwpKgMZ0mVfy3aMNjQ00++qQ1/cp1Ipw6iH4LHD6xqe1OWBOghEOQ1YBIv0CBNpe4PGmIkyoRhrhMlobBn6wnUKf17ERCrV1dXhggsuwLhx4zBjxgy0tbVhv/32wx133IGXXnop/keZQtLjpd9rcezzq1DbMTIWlIPFVnYiJvlfxaHbTkRubl5U9yVE7E6HApViIdr5yDe08TQD/GbGl/hGdQOmcE1wySqAUGD0xo8q3bSKFYboS0xlOtQtzU3kaCLSruAULJ5UPYIf1f/AwuzEVPX9u6jOvPZU5XhXXyvSqkHAyMlVEbe/BejmikuRSglHu6oIdwTPwav+A6W/dzVfJal9Rx11DL766kc89spnqHZrUIlCbPabwIdSiV6DgdtNxyY9S9sxlNr+298CLD2PVN3ajJMZDQ11nZdTSqW+lUpHTFLh8Yk/YfkTZ6PD03eqIvGwsYF+R4Ke0VugGi7U1lYju42ma2cXjI/JU2m7i671hPbRRyop5DVduwd9kkrZmhAeP1KDy0qrkIzjrVYhSAUvkSQHMzRxMRYwSh2OdN2G04O3osH259iTpTCCSSWyKfZ66Rd17NixncbdhGSqr++q/qTw58GTy6vRaPfh2o8248+ANlcAISjQZvfGpFQSfLQi4fQPfVuSw2HHJK4Fk9l6zCvNght0oQF+9G6SfxL2wIH+B+FXUMXDguzEPE9YvkxUbALbt++BX/aI4RK0AQt6elbiHFbarpkChS8oQIfoSSWdTg+3m5KvZ8+h7R4pJA5tily8EjoMn3j3kP4OR5b3ljq79977Ystez0lecdvEMcMyto7G9rdjPHdIqloxc3JEpJJGIYy49jfirxQPlJeX4euvv8ZoAfFLHZutQxHTjkzWBbO2/1Zhh6wohOyFlkKclUomWSWbPibq+xOipcpBPz+NI/lIlcFCFQiTSqE+299EBYfLF6hwVIknKcfbyg4R6ns8aD937aAfjwtRArjNnhj/0BRSiBupRFRJ//rXv1BRUYFFixbhk08+wZYtW/DOO+8gJye6mMsURhdqOrxwJ8gnJtnSxAh4lyXq7zwhZSewjbiaex8zmt7HUMPlsMFIvaSRnZkDr0wqMaPYI+adC/bGi1ecBPdhT8BxyOMomrpXQp4n4KfvoQcaSQ3T5+3kaFslmxhTW4VCjWfrSzv/3lqfIpW6w8eH0ChmoixUhKCatgREAp1OizWNPH625kLImZnQY0wBcMvEkOCnm4DeYqK7w5yVD0FWCzrktLgUBtf+ZmltRU7JDBjkWPm+EOKoObpGEZJaOUaSUqmjIz5KpXPPPQNHHHEEKip2jJr2t6IMShbaFJlgB1BPeBj6HVEGk6+9aKSDJBWWpNEtW8g4sMdVb8R7S4B+PhnBJkAYGYrCSCGGglIxr90Z7FOpFNLSuV7H8oAoJF3yG5dZhPyLnsOlHw9+/FCBzn/tjpHhcZfCn5hUuuWWWyTTt82bN0sG3bNnz8ZJJ52E119/HTfeeGP8jzKFpEeuJoRihvbuflM++ltt9m59HY8oH8feupoYlEomlHIWXM19iLm2oa9q+u1dPdai2gQPo4NbVEMIja5Fxq7QqziExh4M/6TjIBijM1ePFMEAVXD6oIRaNuTu9XYMZfVUrJiQDZio0OHiFzaiOkA34cFRbMIeC3QqBZ5vno49f5iJWh1VwUTqqfTuFh73lI2Ff9LxCT3GFADOsRPzmXJkBqhfhtncu1IpDOK5FFaBOnyJ8Sv7s5FKmeWf4NETZw6oUlHI4Q+6kAt+v/9Pp1Qi4zgxUyZYt+4PjJb2tyIzVdy6VQPLe9n08fhsWxDtzT3bzVMY/LmoMpihU1JSL9b1S0hlgl9UQoUgWOfo6iq5XnEDJvhfw3vbmD6VSkYzJZVYRgT45CqiSl6nagNYUw6aHYMfPx/Qv4416ksw2b8uLseXQgoJI5UMBgPuvfdeHHfccRL7/eCDD2L16tX4/fffceCB1PsghT8Xns94A8vU1+Ag9g9Y3H333Y8WTPatx7GK31DAdERNKhmNJlhddMOjDQ299wDvpKSfO6SEL8TgJuY6TPe/hA+aRq/KULfmMehW/Rd+Sw02NzmwoSExlVRBXqj4RJU0NvaFwgy6OVYHnQgGgwnzQ3GF6IYg5Eu1I3SHSaOEofY32Fe8I5G80aS/ETQU7o9bvyiDaxS1WBH5Pc8n1+uZ2vgB3lffhTN1K3oYdfeFDMaO79PvxVeqm9BmS8n+BwO32wWzGrh+llUaOwdCSR6dPxTNm+HxuEeUUXc8lErk/QqP5Vu3bsFIRyAQQFtbK/INdB4LaAdeH+wc/zcc87YX31ckP6k4kkDIyvEl+dLlDjadMLgxPc6YTBOuD16Ms73XQ9CNrvWezctLBtcBt6NPpZI5LQuCyOyWtJYsSqV9ikQ8ofwfzhU/HvTjGRQBZDEOqPnkep0pjG70bfoxAL7//ntUVVVJE8+uuOKKKwZ7XCmMMMy0UsXNC6qH0LbnNRjNIBXJtJAVYIAGqwdZWdEZ9JBqe1PNTulyJuvBUGtIAnIySwdrlhJdiGoDPhGjtVtkdW0HFq9+HhmCBVVMMd5aXgtGn4XZF50T/yeTfan8UIWdqnpFaV4WYAE0ohc+nxcqldyPGCfYHA7kF+Thd1c+alXj0RYYekP4ZAcxrCcwGo1RKZUI+JyJWFpehyv2GQuDOuZpNGlQXb0T9164P9TphXj03d+QLFAEKTHU4RMjan9T60woVLUiKCrwrZUspqNvE0mhiyTJ1rM4uagFwvpn4Vl4bf93UFGTZoOKgcfjQUZG8nqOEfI0nGwWNuom83p/hYCBYLFYOi9v2TLyvSWbZbVRoYEqmEP6gQNJwpt5uz05lLGkBV2pYAb1uSYLqWRX5eBJ/hhMyDVjToyPM6M4C7fe93/Ye49pgPIqjBaQUIawh57gdfSpVMpKT4MHahjgg93aigxTdCE7iS7qjE9n8BfFKqyOg3qdVdAVqMnbM/k4hRQSiZhWw6TF7csvv8TUqVOlaN/uGOmDdwqDh6J9K0JZ0zBa4eMF5DG0sukMMOC46E4jooxoaydyeyO4oIP2trM0incoECQVGg3gFHVIV3M4ZKwBa/73PyjbRocPxK6oaXPgcJkEzHDtwLOqJ/FtYDGA+JNK9T4tHuWPg0fQ4Px+bseo5FYRJaTQg4E2y9FiTHAzGi/04C0+G5cFL8QezOhvSY0GpDXq+cU7oVhgRpXKE1X62xkzlXhddz6WhmbCz++NkQ5BEHDTdZfhtoUKTDLVwefzQaPpjxIdOkxOE0FY99adZREplUQN3UwomRDstlSs+WBbbtLzSHS5DdZg/61vBN45l+DA61/EjqYA9peDXJIVLS3N0vdelTUGXHYpPGVLpU0dURHHiu4tdKNBqURa3wgKNLRwrIig5WqNhUH+eY8hULlq0CTdYNHk8OG0l//AIZOzcethkzDS/ZQaVGPxAH8grhg3NmZSacLYUgTba1G7U0Wqo/Sfo2DPxvBerC78H34vq8HxAVefSqU0o76TVHLY2hG5m+LQFLlMato8FJRDDwYDg94AuAG9rzkOR5dCCgkklb777js8/vjjkmF3Cin4giGEOD0UPJW86zY8jz9m3oUCs2ZUVPF3hU70wgRaQefV0VdjycI1XHlnIIIJODo3Q0MBu4/HdlUhasQcZLEMTjFuwKwJX+MX9chfXPQGr61R6qHnGSWQOUH6n15wStGtAxmPRosanx5f8cfBBF+/pFKzaTa+LjNhWbML+yRgA6YSaRuei6eLFHdgdPtlRYs/6uw40hSCgRHRYug/Kn3X9jcvT89dLeNHYBTE1r/00gvYoZmA/Qs2SX+vqatAycQZSAZoBDqntFvsEZFK4DTwCwzUrIgxbJdyJIXYSCUTYb0lA2YtBtrmLG9To+nw/4Ft3J707W/hlOL885+Ufrf4XLBarYMilaxWSw/SiiiXMjOTV60VCalEbAFrfAYUmc0oKBo74H0m235CZfH/4SdTwaBJusHi5d/r4AmG8Mnm5lFAKlWDM9P3P88Yu+q4tJQ+Rk6wFuaPToZ3zsUIjD0EIx1KvxWZllU4OIOqlvpSKrEsC6egRo4CcNuTa34gqcyGMKmkoIrowYDVZ0mkklpIvqS7FEYvYvJUys3N7fOkTeHPh+1tbhzhvh2PKc6BoM3EZ23ZOOO1tfhiS5ch9GiCwkbjWFtcArTp0ctnycaIhwKOID39BE98kmcixQpHjhS7fV3wMulvY6gDR05UYmaWkBB/n2GHg24gXKpsqAxZ0uU0xgVPAogWomKLJNXtnqqx+L+xT+N7xRJJGRJvaETahufyk+MREUgyr5zhhi/IQwf6HmnNOVG1v3nkU0SDQL8JfyMBFds24bE/OsDOPVky6yew1m1FsoDx0xZFh1+UNgR6Un3t9w4MnAE6rmYFR+f8M1SQSAHZnNvHDLzJCQkiGGM2OFO21P6WzGhsJHNCV0FBlT1WaoEbDAgp1R1lZYNXK/344/d4//13MBxobGwEGd7u2rkAoYvXYtHkkgHvo9MboWUCSFfxsNmGtwVuam7XWDES0ggHan+bki6gAO3IN8Suai8oKIRh0p44+dAFUDWthH7FvbulwBEFX3l5GV5++QXce+9daGtLfpUz66PnbruHzsfp6X1rkP7quBILfE+iEkSFmVxKJb2Kjkm8nKQ5GDCGnM525BRSGCrEJCO5++67ceedd+Kss85CQUGBtNjrjgULFsTr+FIYASC9zNvFYjznTMNRF1+P1koP0FCBDzc24ZQ5BaOuJZLtqJB+b7cIUZt0E0jGwKIIB5cBE9rhc7ZDmzEOQwWnbJ6kVdCFll9Jq4kmvVby0ehLOjxSoXDRnnKfJh9qPX1tZsYNq5+Pu5LO5GtCzsYXsWDP/gMLNEo6ZrJKteSpFG9oGEqYXGb8BVcyP+PD0HyynIr784xUBH0emgBDNkJmSjRG2v7mDcpKJQQ6ScSRiGabA+xXV+C1KUZc7Z8KS0AJvdoPd1PytME6HR1Si4JLYYbZLOy21ugNbkGJLIQQcKRIpUErlTR0A+tXDNyOkeOrwtXc+6g3Ax7PGCS7Uklh6Np4CkHvbqTQYJRKBFu3bsbee+8b8+ORBL3zzjtDKjrst9+ByM6OzrtxsGhqktvfCiL3JeN09D1N5wJotHWguHj4vgeHTc3BPd/RsYwolvSqkauar66txYeL3sJ0tgY1rucBHB7T4ygUCmSNnYaXMg/FeeI1SOvYDm7LW1jhm4jff1+J33//DatWrexhXM9xSlx//c1IZrS2NYGs7CxB1YApoR1L30LFjjKYXnkNyWbUPVZet3GayH0e+0ILKxdQR7BaMoWRh5hG2fXr16O8vBw337z7QEMIhLIy6n+Qwp8Dbtkgr7VuJ/5z38/45x334rGlVaiyeLChwYE9iuLrFzPc+GZtOY4XFagMZcZEKkmScFHABa5LEVSZ8IBuIoZy6eWSFTo6jpJ9q1oEqWaTnpUlbSRGG6mk8dLNJW/Ih1JNFxtpcKHGxyM/zur8gzRb8feZ9djOET+Xs/u8nZ7lkQsrNHoxMUolUB8MW0CBXE0QZn+qr747/O6uKro+LfLNml6vh0cmlTRMIKr2t+dX1KDdHcCNB00YdqK9rMWJ2revwpnKSjhFDe5erIR7JV2QB6y1SBbwckKPW5kBkykyFaVHJH5QPjiciUl4/LOAJEhmyqRSgB24cp7urcbV3IdYaRiPrZ6DkMxoaKhDyN2By9iPsESxFadoc+KuVBqsrxIhpYjfHn1sy5CTSkSpRNRchtwSKdBDTXrhBoBSJurSFV5sHWalklapgFbJwhsUYHUHRyypRFRWDRY7Chk61+izSjAY3VVpqAE7HG48ln4sblO+Ac8XN+LER509glpIm7cUKNPUiJaW5CfnaxsaMJlYO6jypOIDIc/6glERhOC2wet2JZ0yVO0kc68K88YXYLClRn12Kcq2j8FOIRfZHk9ncm0KKSRd+9uzzz6L66+/Hhs2bJDIpe4/KULpzwfR0YArFR/iFN1qvPjic9hZth43F27CIewafLCRJoiMJrwqHoGp/pdw89oc5OREH8tKJjzSxrHZnY5KsRD2oCLixcXNN/8DDz10PwaDS7LX4hvVDTieoylPjJpWRQxcqDOKfjTBGKCLItZUCFFDSSUD44MrAV5GnChvfLn+jY7neFfgd80VeHbmxoS0iugYSipZgvQ4lEL8iauRDJ+LbiA9IQX0RnN0SiU++vY3crtnfqvBBxuaUNMxvCbGZBxZ9enjOJP7Tvr7I8VxWLLP4XBzdEPod7QiWfAccyIeDJ6Mmo5Av9Xn7mhg87FDKMRGXzLZsI48kE1Op3GsggYL9AdOTduNdGww6T2VJBNqUcANqvewt2ILziixxk2pRAJswqTQYLB+/boen8VQgyiVrtrHhMsCj+L9J64BLwxMZWiMsjqC9cLWzbh8qEFIsBqrB2ly+6bVs3tK9UhBa2srGHsD0hj5nDIXD+rxxo0phvWH5/B66BDUC5koNDK4cf80HH74X3Dnnffgq69+wI4ddbjiiquSKsmvP4he+l2zBBRIS+t/njhmbBCPHqGB2boWyaZUCreqicqBx9uBoB27BEcE7sN1wUt7hAikkEIiERN1T+KvDzjggN2S31L4c0LpqMa1yvdRlpmGxwQBK5+6EP+c1Ya9uXwcuX0ebPuPR5ps+DkaUG/zgQcHS1srcnNjiyQlvkohn0s6AR3dS0T9oLKyAi+88KzUAnLVVddFnToXRgFfh8msEw1plABh5c2AQcGjZRgWr4leXD4ononnAofilT32glLVJU0qklNt4glWriYOlN7BKOn1OkVilEp6WUZtF8ln64SSSXkqdUfQQxUwXoGLSjVEPZXo5ipdyWOvcZFJy1tdfpzALkUG40QwNBfDCa5lHa70PSmVlJ6tzMexDz4g/b9GOQbzUQGIyfNdeTV4IPwhAW32zzGhJDLy74XA0VjB7AED70ByN20kN4hq9Q2+FGsmXIK9Sgpw+QC35zR0I6RnA0nvqRQ26g4j5GwdtFIp3DJE1sakuLptWzlCoVC/qon+sGFDd1Jp6FUVRKk0drEaRUw70jgeHDvwOKkz0fFQzfBwdgyfF09ZsxMXv7MO2bBhiSE4ogMViEl3iVn231SbIaoiD5boDWeeeS62lt0Aj6cN/2VPxn9VT+P2AwywnvVkj8CYMIk/2PNiKMD6ZFLJywzo9zu3SImjzCp86qhBMsHpdOC0z7zo2OdenDD11EE/nllLlcesSoOWNgsKC5PLQyqF0YmYlErXXHMN7r//ftTW1kqmbin8yeGjbQb2IAelUol7v6iEH2qMZ5uwp7gen29NfvlspHD5edi8lIzhbc0xtb+FfZWWsJslDwpDw88R3WfHju3Sb3LODcYE0yDSBX9OFq0qKjSmTlJptCmViGT/m78fgJeuOBGa9EKAVcC5/31wHPI4cjLi3+anMNH3tNKni4hU0nJCQjyVfm5Lw9utpahT0KomF4Vv0J8Bbn8A24QiVPmja80l45s7xOHj8iAc+ftGtNEiaLW7pMX7rco3ELIO/WKWKKV+rbKCcbdC9eEZULEiPtsuYPblb3T6FAVUmQiJDJiw2m6YQVJ8CClMIPg9MJki+6zS9VSdF0DshrYpkPY3FyyWDjDGAmQUTBzw9krZB4SoJN2e4VXjRWLUbZhzJNZpl0h/8607B13NDyuVFi1aRBWNXi+qq2mox+CVSkNLKpHADpJgV2SmhSuvOrLWO4PBjIBIzzuPffgUj5UWN4qZVkkN/AZuwYIxI7elv7a2GiVpdIwOGQdPDMyYMROffvIVXrziOHzB7IMyoRhswAHNljd63C6s+Bluw/VIEAp4pbnL4hUHtG9wqSjxGQgGko5USj/kUrxoGYufdg5+Ha5XKyQ1JkFje/ITgyn8iUmlJ554AsuWLcNhhx2G6dOnS3Lf7j8p/LkgeOmAZQuy+Mc/boIrADyzhiolLlB8iaUVxF9mdKC1qQYfq27F/cxjEAPemEkl4qu0j2qH5EGR3bo8ovtUVHQZ6A5mAWxQUiUCJ6dDhE0BOUYcloroUECv6lKk+KafCf+k4wBl/HvMVQz1qwopNJGRSmwoIUqlR76pwelPbUS7ulT62yArl0YzSFvXjjYXaiNoL1Pqc7D4l/k4c/WMqJ/HCx2Of8eL8mk3RnwfM99l4uuXfYKGCh2eAC5/fyOu+WgzfB9cAIPoxNa2ELZOuQ7jxndFbW/IPQkT/a/h5sb9kQwgLVTzmXJMZOohBDySujMSZJroucUzo0cdOxwgc4Gn7BdcM0eH0+cObNas1sqkkuiBMwGtxfECKZyQVjfDjIOwxUm/K5k6poc5cSwgj/mXhWORY1uNKVOmDMpXiSi9tm0rG7b2t9bWFmk8LTTROTOgjWydwylYlPvz8Nm2IHzO4dvIVrZ7cJJiqXSZ4T1AYOQWy6qrd2LyjFnSZSsX23qzN4xJ1+LMBSW4mz8LTxuvhHfOJT2uN5vTR4xS6QXNeZjgfw33bcwYUKnkZ2iHjSLkS7r0Ny49Hy1+BbzBwScTswyDT1W34A/1xfC2VcblGFNIYSDE1D9z3333xXK3FEYpStRU+WJzevH3W67B559/igeXbcTl80ySX8HkA0amQWJvcDWVYxFbBSNDqzf5+fkxPQ7ZIFl9tFLC+iMzlK2ooEolAovFgokDF493A5Hjp+mILDYItyoTUs0mrQTjfa/BZ2nELQWjjFTivTAsvRWCoRCe+VcCLCeZJTc7fDBrlChOHzjVKBpoWLoYEAfwVBqbmwlsBTSCp9OMNZ4IK84MxITaRdvhyCZhuA2iE4nnft6Cr9aVY9Gs2bjp4P5PjkLWAdvSVzFp4eKon4eoEELFc/HUmnacqMjEkrEDe/dM1nYRSUXaoWsvIzHvl763UdpkGdQK3LLajOvSQ7irfCIev+3qHrctzsuFUNGIIKdHIBCQ2tyHE0FrFd5X34V20YhsIRSxUmlP1Q6coXoSq4XJ4EMHShvdFGJTKv11JodprR+Ba1WDz5nd7+05LW3L0cOLoMeZ3H5KAKZksihmaItWplEdF6XS5pNcgPVVbNzrQKxbtw5btmzG0UcfF/Vjbd68SZqrwxhqBXFDA32PCvWyUlAfeZv/mzgB9799D846a/jaaCvb3bhWJpUIWG8bBNXgfWqGi1Tau4CqhtrYbMSPVgLOXViMj7XH4pjZ+cAuyZphpdJI8FQi3QMiWPjcTqSl9T/38zKppBT9SeepdE/+LzBya2AIXg/E4ZPOYhzIZJzwOYavFXUkgayR33zzNcyePUdS9KUQPWJabS1cuFD6mTZtGgwGA/bYYw/pcvj/Kfy5UMTRDZPL6ZJ8fh544L+odwLvbqakiXHzCxgtCLVXSL8rPHqpR9lgMMbc/tZcTR+rWOuLqv2tt/jiaDYKaToqT6/n5cqyWokQFGBU2lGnVFq+fhO0Ze9A9cdTAENf90+/LsVbbz+HH1ativvzqVm5HZjrn6yaUEDbCbRMMO5KpSAfgt6gh9ZkhiFrDJaFZmCVMLWzlWi04vy2f2O5+mq0VKyWFgf9IVz5NxqjP39JiopmzEz82uDDtpbINs9ssOu8ylcPney+yeGTCCWlgsEJxlq8+s4nWPxyCNf954XdvF5K8ig5pjCko7l5+AMWAm5KtrtFei5FqlTK0Ckxha1DCdMCp5xMmkL0IHPBKdOUKNn5Gri2TQPePmwuS1pCA0lMKjU01INR63GBfin2U2yU/lc0be6glEpkvOluTM2kFQxKqbRhQ08T4aGel4lJNyk/5KpowYM1R14862qbGh6FC/ksWttbkM90fR5vLu1qJRxp2LmzCsvF2XiSPwaewn3j+tgapQKnzS2EMky8h/xgPG27kEr2pLc5CVtSCF7HgEqlsIpclWSkElEqHWMsw6nczzAw8Sk0ukNUravxjB4LkkTi999X4JprrsAppxw76vZCSU0qkSrmrbfeKhFIJ510khQ5edNNN+H888+XBqAU/lwQ3HTy9kpRzsC8eQtw1lnn4eGVdNBWb/8YorMZvjhIOocbBSFq8LndImLSJBJiGhuMRjNam2nMuz7kjGih1F2pFGtV1WG3wczSz0Vnpu1vuUY1Cjo2wfH7B6POU8naXC39timzIYgifv75R+zX+gqeVT2M0o5f4/583zpK8CJ/ONr1/VfLRHlhQ/zrvd74mtrWt1lRfY4VnmtEZBVNwFnBf+JG/iLJD2y0oqzZjrw22kZ6auBDlLf2vyDI6liFLVeYccXk6BdbxKy7Yu4bqNScBa2np+FvX7Dk7gvHvvfAPf8qhMwlGCp0eIKYy2zHEvVO/O8uUv0EbrzpNkyYsPv3M0cj4nHlo/go7wU01w+/iWmYfAsE6IZhoFSfMFQmStiaGXfEIQgp7L7GIz8ZRWOlvze0CRGRSk/zp2HGky64vMm1YduVVFKm5aGUofNvm2jGd8LCQSmVSLuaRk5uIvhIcYj0u6xsy6D9lAjcbueQm3Rn6Rio5OAJTRSk0lphDPLPewyNbGQhBvGG1RNEvp/O+2E4LcNPkg+GVFqhXoIH+NPATqDfq0SArfsN2pf3hvHH63sYdZO1J/H7SWZ8nP0MXg7dirxQ04CeSoJc8FMjeTyViCqRFHz1cmovpxmcGXsYguxvpvaM3O//UIKEKxC0t7fj2WefHO7D+fOQSg888AAqKirw0UcfdSbA/f3vf5cqPf/+97/jfYwpJDn8biqPDXSLHb7lltuxM5CBZTU8tvGFuPrNZXhrLZVUj2RMVNCF6KatOwZFKhGlktVL1RSMf2B5MWl3626YSP6OBW57OypCOWgV06Ax0sk3Q6fCfca38dak78G5GzGawDjp6/Fo8vDFF5/hlFOOQ2Uj3TxwgfgS4GTx9W7HFNzFn4229P4TvjysHj95p+D5req4G3U77V3fjZyCsRAC9PEt9uRVDwwWFTup6o8gh7FhaUX/50ezn8W0TBFmvSqm9jdekDeQwchUZhe8vR6zvh2L57nTUY2B/WnihQ6XB0+qHsGzodsxL9MtEf4XX3xZr7fNMplwKLsai7gdaK/fhuGGWqAEt0uuQkfa/qZOo606Jn8zjJrR03o9lOhU8nGyRxwXQesQw+It7I+O455GqxCfTVGiSCXiXVLCUkL54sA1+ILdFzZn7JVpQkhlqikB4xbVaPQpoTBkSsldsfghrV9PlUozZC+doa6akxZBUvAo44tQq56E8bmREboEfw19iIriu/DX7K4i2FC3vk1m63r8T+kfmb6ehMyxunxgVZQIyTP131Y/mDbpG3+0QONtgbrmeygbV0Kj0UhzXdKbdYsi8lt+wf76KiDgGZBUCntpamQCJxlAxghGTs4kUMn+dINFUEkfh/Elvy9WMqC2toa2fGezeOKJRwfdEv1nREyk0rfffotbbrkFkyd3barJ5bvvvhtLl3b1Mafw58CVtpNxkv92LOW7elDT0zNw++1344g3PFjyRSF+c+Xio41N0uQ1ksHZaJrLNouASZOoGWcsIK0cNtCFOu8eeMDvrlIaTPubzR3Ewd57sdD/JAy6ro3CTG0r/jJJCcUom3w0Xlqh4fUF2LRpg3S5xSlP3EF73BNzGI6SFAZN/2TFFquI85jbcU/GXXFvf3PZ6QLaIyiRkZUtGcoD4qgmlYKtXefH6YFb8Utl/+eHCv6IDNV7g06nh1egFUAxGBkh2OKkz/fwz1V4fU1k6qZ4IGSrQR7TgZAIbGzn8MgjT/YZcW7QcLCG6ILb2xp7alW8wATo99XuE6Jqf9OSlEdye7hhUqX8lGJBmMTQK6jSSxFh5dwDNZQZBfDwYlKTSvqMHBSAjhF1sncJUbXF2uZD5uMMFX2v3AqTpJI7egldH5eVbY2aSAiHcuy11z7D1P7WiBq7iA+1Z0N7wY+YXRh5SqZKyUHH+GFQDM+mnRAvx+f3XMfoArGtl5LBT0mTno0FTDmmaW1QKxLjiahgGYyfNAtvhw6Q/tb8eo9E1oTVSkntqxT0gBHod63dI0p7j/6wUz8H+/sfwpWWU5BMrW/d61vKeJFKKvr5MUlmSp6s0LRvxBsn6LDlMoM0Dj/++CPDfUgjDjGtuEiLTJjB7g4yIXc3F0zhz4GdoWysEafAx/VceJx66l8xc94S2Db+AJb3o8nhx8rqkUtahII+sE5aAdvWPjhSiaS/OTXUd4Ej5MYAHjDd/ZQIrDEy6DaHA6ySbqSJUXUYPob+T/SPLuJB75djjU2FnfHO7S6qfNDw8ZV0+/0+ZDQuh37Tu5ic3b+nkpqjQy/LqeD1xnfC9znpd8MlqiVj7tWm61GpPhNqx/C3NCWa7K1I21dSTOxoc0t+Qn1BLQYGQSpp4QvJi/vQwKQSaTt8WHwAn6huxVHsCrD2ofscGDclGFsDGlx+zS39qivJd8UH+n4IHcP/XWlpp94eVoGS3yZTZGoJXSYlldI0pN135M43w4lOo3+5ch5OCB0IJ3JLcaXiQ6QheeeR+vp6TMjVg2VEBFitpDCaytRIPksOhz3m5LdMLdNpjrteexleW1AGkuodra/Spk0bafJaYRFKS8cOm1KJID8/elWloKbnqVFB59mhBkk1m6WkCuX3ywU8y/8FK/mJcUnUGg5SaWxeGt5T34UPxZ7BCvHGWQuK8Z7+dHhENTSt66Cq+qqbP1bykkp2K+0e8IcYeIIYUKk0Mz8dvz5zB+y/f4xkMuk2yO2zIbDQauNjKl8P2gqu0Jvi8nijHb/uaEeAVOAAjE9n8PzzT6OlhX6/UkggqXTggQfiv//9b4+Jrq6uTmp922+//WJ5yBRGKIjyKABa+U7Ta3bbpNx//3+hgAB208c4T/EVPtowclvgGprqURPKRkdIixY38VTqiuOOyai7w4MT/HfiZPx3wNuHSaXCC59B8TXvo1VWPkSLNrt8zooi9GTFK6MVlBDkhdHjQUIW5uk8bXFQphVi586d0uUWG/Uw0kbgZRUNCDn00dzV2LLgYyxS9fR02BUaTgEDPMhR+RD0xdfHyh82OBZo6YsL+aBgRJhCo3eDzXs64Bc5cNkTMLuALqCW9aNW0jDy+cNRZU40IAUVD89G3P7W7PBjIVuO2WwVHlc9hoOtb2CoMEtNSdW2DicuvfSKAW8fUNEFOfHAG260yi2+dnVuVEol0nYUjlSuqx86VdhoQrhli6RGEnARVs5Pxbe4Vvk+cpXJSyo1NtYju+xd+oc2HV/jcnylvhkqrUEih2JVKtnYdHxrLYR9xrkQtJkwcCEcNoGL2lcp7KdEEoiaFVnQTz8wpha6wSqVSFKqMj0vai9MVk/PP7MyODxFZlEEa6MR6nf/7MG/vSdhmTALFnfytDtF46dUmkHncSuXSxbVCXsuUuj620EL8XzoCOlv1a//QUaaOemVSvXku0reH5GOURkZ/SuVstMM4Dsa4ba2JJVSKUwqMSo9MvTUVmaw8OnyUSaMQSs/MpMPhxrlVXX4tZaOWRcdOEFKZv7vfx8Y7sMa/aTS7bffLqV8LVq0SHrTTzzxRBxyyCHSRvm2226L/1GmkLTwBEI4X/ElzlV8jSLT7v4VU6dOw6UXX4qlMz/FHcrXoKz5QYpzH4mo8qdh/8DDmFl7PbKzcwaU2fYHskEKet1YK07CRn8OBmoWqKjcgdzT/gMuoxCsSgMLKcnEgIyOdfhGdQP+zT0vbbrC8MgKBTaUvAar0YK0M+TJLQ7ajDHSAo2gqZ0qlPSCM+5KJa1Sfk+V/SuVNEoWS9VXY23GP5GB+JI9vJe+PrdAlWhumQAJukZmC8BAIJueez3HYJr/JYTmXYxH9C9hc+Y/cfy0viuWGtDzh1XrY2p/CyuVWH5gpZKlw4o0pos4VMeZzOwPPisl8Z0hlTRnDwS/lvoRcaHhN+zfpp2Lh4In4euWzKhIJSjUqPGbsEMoxNIdKYPSQbW/yaSSShtZpTsgK16VTChpCw2k/W1COp1xxbw9Oq/LNGljTiwj3hubjYtxke7/cKvnNAQmHC39/9TpyqiVSuvX/yH9njJ7AT62ZCHrqGthDwxdOyEhgkj645PHpeOA9Rfj+Wfvi+r+KhMlgUk74FCTEeTz/bW6A1/NfR3Tn3Rha5sAwWPrNPAeaSBrlhwL/T6YcqhqLZFYMjYDW8ecDYtohNZZjZMn+pNeqRRwUTWuhadEzEBKpTS1iH8fqMY/ZidPqJTTaYdBbn8TZc+neKAq/1gcEbgP/7OnxB4R+ZdZrfi6khbWz15CVZqvvfaypBhMIYGkEhlgjj/+eFx55ZV4+umncfnll+PLL7/Ec889F3FCSwqjA64Aj2u593Cn8lXkm7raqbrjmutuxidV9Lq/sV/ik03DXwWPBfV2Sob5ba2YPDn21rdw+pvgo5tLorZ0B/pfhO+wBqAvmd75tyPGRSYxr53M1mN8kFbywvAxlAThxJG38OovZvZ03Itj8D906Md3tjY0tdEF0hitf8Do+WhAvJF0socLz/ZfadJwLLygtxGD8d3Ah1sYPbJSyStwPdriRhvqbfS81KnVMKbno9CyHAZ3NXQtq/q8j0426WQjbOvZVanklFNOT545cMKR29KzlUwzhIRN0EaruC4xsoVqpUhJJYOhf1J0KLBFOROPhU7AN82mqIy6CQ7acToOCfwfqn3xW6D/GUmlI/z3SmpapTErovuRdjICNZOcileyaSDj9HeVPGyLboFv8snwKugYYA5ZYzZmJcEZrKzmStMpsUpHo9+PmabBzu2bo5pnwkqlnAmzMJ8pxwHsOriHcFpua2uViKVxGQoUMe0wdlM0RwKlrBRM5wIxk3SxgnjXXf3hZlz5gxVbLSyIhWe2uwLzFDsRDMXmlzXcpNKEDLqmUGSUDslzXn7QLDwlnCBdnpijTnpSKeSmxTJrgO4xBtyDsixu2UeNy+cyEGP0UEuEUmlDs4BDf5qBjlO+jtvj5mWYOhPvYvWL+7OgpqYGTx+lwbX70u9PrqcchxywP3iex4MPRkes/5kRFam0YsUKHHPMMTj00ENx6aWX4uGHH8Yll1yC++67DzfccAPWrFmTuCNNISnh8ng7q5lKI+3f3RUGgwGZR94BXgCWKLZi07rl4EegYXe9jaoS+I6mQSW/ERBVn8gHcAx+wTXc+/C39J22RBbBDmMJnlH+F68q75X8H9yh2AxoOT9dNItiTxm1XyaVlEmUiDFYlGTo8PXfD8QTFx2PnfVdUufyZi/qZ/8D7AG3Sy2acSWVNHQRVmXv//ut5hTwiXJpio/vex5SaPFuawlW2+jk6JTbhyo64psylyyolc9L4qXBsCwCJQdJf6uqf+j19mSDZ4MRDWImFHKrRjTQanVYWRfERl8h1GZKwvSHYEfPFiydSNsvhwLVNkpgeTQ5Ed2eNeUjJDIgll/EeH44ESbaBb9HMhfX6yNXlWlYuoB2yMlxKUQHEm9N2p/q7CKCefOh10TmPcYrZFKJTU6lUkNDHRSmbFj3uQkvikchMPYQqPRUcWx010oJxrGAkFF6nQ4sBKTrVGg2TEedkA2DIoS9cz2SOioSEBImXBU/eU4e3lffhZdU/wctO3QK4tZWOlcWm2SFq47OH5FCbaDvZxrrGXIyotJCx1YTWZMKPErTGKwb9wTeUt6JecVpI5JUmppFP4dQ2vghMzrXLjgPt6Q9gDfd+yR9+xvvc0lzlsWvgMFghFLZe3E7DEFLlUwcK8Lnjq+v5mA8lRQZRajZ40Jc963sAxoHFGTS18pqDEn9GSZL8tu+EwzIVdEiJcN78J9Lj5cuv/fe2ygvLxvmIxwZiHhnunz5clxwwQWYMmUKXnvtNaxcuRJbtmzB77//jpdffhnjxo3Deeedh3XraJUlhT8HfO6ugUqb1vfGZf9jzsZSK612nt3+GNgBG76SD6fvvAnvq+7ELGbHoEy6w6SS9JiKH3AV9yHE1r4l8lVVlSidNBUHsOuxr2KT5P8ww+yNya9AEaQqFp4z9Fph5pCcm4HBQKNU9JCvtnlEVKQfjMA46h0QL/i8HmgYuonl1AO3v/lASSUmzuowP5eFU5/ahI+r6CbcKz8PGxj+lqZE4OD0NpQV3IMXs96S/g6TSr7yr/HYLz0VeQRk5LlpQzFm/nYoXGMOjfr5dDodbvvJj4ea9kSwaK8Bbz9BRcdIJ08r/gYMHbn3QWAx7gyejY1CZCS4dcKpmOh/DVe2Hde5uRwupLkqMJmphS7klKrP0RDAerkN1elPTsXMiCCVBB4TG77Di3/dA1plhGoVFR1z1EnQPtmXSbcyqwTchCX4qoxu3gQNJRuI0XasSiXiqfTklDWoUJ+FxdZPsKg0A1+Ki6XrTptjxNatmyN6nA0b1ku/S0pKkV/1Zuf/h9L0OmxMm6+lzynqBybOu2Ni6Visa1dj+XbbkJNKVe1uXKX4AP/GE1hUqECLi64zVaTdeYTNf8RapKm1DTPGUVLPZx4aUongjEXjcdVfT0ehkUt6pdL3uiMxwf8aLt04Denp/be+EaRldJGkbltL0iiVFFoD/NpsVFri9z3NZyz4QXUdlmbdG/PY9mdBTfVOFGlpgfcPYaL0e5qqEUcddaxUiLzvvn8P8xGOMlLpiSeewLnnnosHHngA8+fPlxZ5pHpIfA6ItxL5/2mnnYannnoqsUecQlIhi6OsroPnYErr22OIbAjyjr1HunxIRj02r/gWIw3jA2WYz26Hz24ZtFIp7A/SEaQb/qCn7wrpyq1VOCWrUjJbDqPQrIhtopeNuJ3Knp/V6/pzMMH3Kv6vagJGC7iW9TD8eB00W17v9FMKY0eTFZubHFIyV7wQ8HnAMVQhoVL333bDsQxUSvrZK3hf/DeEZGOtp8ShV6TqKQU/shbVkULZsQNa6xZkuKiZfaBob4RYFTKCTVi/aQ34XdoeiJeY7ZeX0fH9M8hOj7ylqrtSiUsvQLl2Cl7+vXbA2880UiJ3UzPdpKUzzri2XfYF8hwrhal4OXQ4OnSRbUgyzQYSqwCFIaMzAWq4cH7HQ/hGfRP2MrdF1fpGcFXOask7bi9+RcKOb7S3vxUYGZw1pkEaPyNFSTadV9iG9UPyHY/FpFuXniOlMC7R1kmmzoKGbkSzTJqYlUqkrY54CJFEOZXOBIOaw47Mg6XrDh8rYkfZxogeZ/36tdLvvZcsgGYrJckJNNzQta60trZCpSDta1QdpTDlR3V/QZ+LKzdMxUnveYe8/a2y3Y1DFH/gKPUfyDMwyCkaB5dsFcB64qcAGQrU1FRDl5mHsXJRApmxB8NECwXLSGv2cCtZMqtc7N4gRLBwu1wD+ikRGLQaeGWVuN3amjSeSvsWAfdxz+JE8fu4PS5R945nm1DKtqK1LTlea7KivX4HjHLzwHP8X/C19igESg/CjTfeApZl8eWXn2HdOupvlkIcSKXy8nLJR6k/nHzyydi6dWukD5nCKECxzOza3IEBF/7Ze/wFFU41VAoGofq+/U6SEbzbinRQqWxZTfuglUpkw08GqtZwdK+y75aY32rtOFnxS4//kQUTqY5GCwVHK/gNTE9Ju1qjBw8OATG2trpkxOrVS6EtewfOzV/uZrS3sqwMb739HHbUU8+ZeBpkEyg1/bfqSAs2HW0p4eJM9thcLqgNZmj11OOjWsjDstAMNPHR+weNBCg6KqTffLpMnKj04Av3lC4u4v/A2vqehpzEWyCcpmQwmGLyVCIJYy1p0/Dl1kgWagw6Ahw2NlMCk6T+xbPtsi+4/CGIDD2fC7Mia/3IklNnFPp0mgA1jAh7T9k9gchNumUQCTvxjssQR2/iYaJJpXHpLE7O2gbtumcivp9CNvTWq6jSIhmVShPzjVIK483tN0j/q/bQ73zRHnsNQqlkRYZSNjU3UUV2wcQFuCF4IfasugAbt5ZH4afE4NBxIhg5NOM5/ki0BnUIBIamNZ0oFAmhSOAXldDLrycahDf3Q61wqW53YiJD11SbW0NYsGARWv10p/j2MqoCGykghTBVegH+yZ+P99QnQjRER+4NFt5t3+Fc/iW88de8mMnWofLOJBC8johIJZJy55aDaVx2S9IolWZmCziN+xkLxE1xe1ydfO4SstttGd75PNnha90h/XZDh2+Ehbik46/YqZst+eeefPJp0nX33HPXMB9l8oONxi9koIUdkR7GGsmawsgE46cbaZtPjGhAt4lGyTzR6pCj7UcIBAvduDYEjSC8THZ27/5RkYJsKo1GE1pb6KZUL/T9fugCLVK1wScqUVdwlPS/LL4lpnNNz1BVDK/s2f62V2k67L+9DVf1pqSsMMcCn7VO+m3lsjuVSoTII/gP8zieVT0MdMQv1cHpDeAl/jC8ze8PtSoCDxI55YMT47tZWCCuhu86EX8ppN+tT4V9cVbwn/jaPxujEVu30pZrt77LyDRQSlUCBynWYWllz4Wj1W7Hd+emYeWFJmSIlpja3+6c045t6nPwN/8r/d5WEEXUzrgSSz7Oxz++9eHhlX583DYOEBOvPOjwBrEfuwFz+Q0oyo6MlMnSK/Go8jG8n/YILPV0zBsu5KjpeWFprIfJFJ0fCq+g55Z+CFsNRxMI6ZqRXyRdrnZFXmgIzP4b9nzBjUd/DyQlqUSUShOyqOeKU1ssRbS35OyLp/mjsJEvGZSnUoaCzq0aM10bLBmXiXdDB8BRsARbtlEV5UDYsGEd1MUzUCXmwSKa8OnUR3DDMjXKdjZ3EuFD0f5WaKSfebOYjixj9PHm9eOPQv55j6G+Y+jUsSFBRMi6E2omCBL0VtUhSqRSi49+3i7ryNpUkzWLkFYkfYd+KLhU+q4OJXa02FAgNGJqkTGplUqPpL+P9zX3YZZQFlH7G1l7e2SlktvZkTSeSmFD/KDsSxcPsJyqM/2Xl0M7UugDDup751BmY0ou3R99tpm2Al9//c2SV9fSpT9h2bKeBf4UeiLi1QLZaIY3ZP2drKNlQ5pCZPA4aJynzR9ZOs813osw3v863nLPx0iC0UMTnMrrOjBx4uS4KA0ISWv10vOF8fU+aRND8+MEKoet1s9DRhFtu0u3bopJqWQLadEipiGk7jn5HpvdhCeyP8AN43fA7R4dbVI6H50QRGNhp1IpnNrnkNOwBHf8FhW2AIN/8efgJv4iycdpIDSb5+CVMiW2WeM7ZqpBK9wCS4ktg0pOfxuF4R8OXxAaJ/1s19Y6cNttN0kqCyJbdurGYL0wHksr2nvMS9sarZg/RotFBYBKSd+baEklMSRImxel0H/rotUdwBFPr4TniLukxd31K8x4vGEq3MEhIJXcfjyrfAgfGu5Hgbl/89IwzFollrBbsIgth9MSmblwoqCU/d86HK6olUqsmi4KcwPD+xpGKsg5ZNTQjZdHrupHgt8d6ag95GH497kSHo87KZVKE8zUNzBooiS0teRo3Mf/Fb+L0wblqZQm0gJbbg5VlIzL1CFbx4HhVGgIaqXCbH9oa2tDfX0dDDMOkFpW753wDvY84AT4Vr2DQNP2zkS+oWh/I4W/bSiFxTgNpRnRJyg+ZXwOlcX/Qq5/6IjpRrsPYwW6TtvSGkJaejrGj5+AFjcd+5X+5FClREMqKTMKO0MohhqcHGKRMQwpftGgsGMV5mMjTII9osI2gUe2BPB384QdbqWSQUX3FEFF5IEUkcAlpwAHHSMzdXsoQNaHal+bdHmzLxNziszgwKN+80/gtn2EMWNKcPbZ50nX/+c/d6V4jn4Q1Yr6q6++kpK8+jsxUvhz4dm6Amz23w6b5Ve8FMHCX6FUSf3PrsDIMlDlOqjh7zaL0ElMDBZEqWRnSNubHy47HdB2hYL34ggN8WNgEJx6MgQdfd/y9AwqYyCV/t52AkTlDJxW1JMg1oXs+OtMJZbVMBKp1N95PlJg5ul7KuiyO02H99hjLsrKtsIpaCRKPeSL32LJ7ZMVR2IInGJgvv7S+gNRP3Zf2NbdiXhCGyaVOLoQNWiUxK0bAWFoK51DgTqrBwuYJunyvU+9jp83N6C5uRnPPvsSbGcuw8NProDfGUBFuxsTs+l3usNBBM6+HsbC0ba/eYJ0Y8oJ/acyNTvp9SHSGikKyD3lX6jNLEZ5iyvhaUROp60z2t2YOy6i+xCy3AUDsuAAYxvYLyphCAU623/sfjFqUkmpTSNsCNJ8w+sLNVJB5oAiLSXG/WzkpIKPD0GZMw5CMACPZ+hSDiNFQ0MDxusLpMtsBj0nTBquMyEpls0zIYvEgAdajrb+pWfno91Jz6XDpuWh+esHcMZ+O+H49RloDrqqz8fZsGEtGE4Nw9R9pb8PmVFMoq0wOU+Phlb/kJFKRKm0uj6EX8f8A0cffVxMj6FiQtAyATCBoVOkp2mVuHiSF6gGNrWGMG7cBGRkZKLMQcdAbWDkkUr7TZwClinHOEPxkD+/ykBJpXSFF3Z78qYCMz5KBLd7REyNQKlEcKXzXPDadOwXZHEAhh9OpwOGHLo+C8nrtnjBAzJ+++B1jqzv/1BC2h8IIXSIaWgSM3HyHgVw71yBRzy3I7TMDOvEY3D11dfjrbdexx9/rMY333yFww8/crgPe2STSgUFBXjxxRcHvF1+/tD2/aYwvGjwKrFGnAKXbT00EcQO64kDJBnohqBSH0+E5PY3QipN2nNwJt3dE+A8mWRjuQH2jjb0tm0i5nr//dqHwycosdcFx0JoXCb9P9/IYpUl+qoqz6pAPoEMQ8+Jy8fopOc36ZRwuwk5HF2McLKBSOGzQ20SceSQzdCJNLq0dKx02R5S0dGvD4VYLBB8Lmg3f4DMQuLtM/BShaqZeAQEWimJl8+OlsQpd2uvO1i9GQ8Gb8N6DdlM0d7w0YK25hroGT94KLB8KyUQPvnkQ8ydOx+XXnoFFo5Jw7IqK36psHSSSnaHTdr0EIhc9FV4nU4Pj5/eXyX2TyrZLE1S+kqtQQuSSZfBtyCXYeB1k+9hYkkl0UXbH90Ch8y8yDclnNYM+BrBuYZP5cP7ugpUzgAbtVE3S6rsni6CNYXoQFqtjOZwO0bk50imvx7nK75Ecxppf0suNXIwGESbyyeZ1hJocsaTTDCYlQIK0QaFzo3aGFrKibopS0fHbpFVglGRcYaSKVftNw6ffrIKB4z3o7ryM6AfUon4KV1x+Hj41RuxSb8X9ig0Q73sTqw/J4gHVqUNoVKJFmCys2NfA7gYStZzQ5gCaNRwKGRpy/umVkFSKWVmZuKbHT4w047Bb8JEHM0LkqfOSABRVz+2TxtmcW9gS5B449C1y1BBa6Kkkon1we10Sl6EA3WrDDU8fh7pni5SKVKlEl+9UWpjOuxm6qs23CCCDL2c8EjHj/ihVZGDsbAOiTp6pKKmpgZPlRnw2QHPQsmKWGbS4JazT4Xw4n+g8NvBtW5Abt5cXHjhpXjkkYdw77134dBDD0+682FEkUo//vhjYo8khREJm0c2p5RTrwbCPtoqXIrPUc6TSfJMjBT83qbAFCEL1fo07DVIk+7upNIvTUGcMPVOjM8rxa7TG0km+25jDV5aH8QvjjFYqdYgZCjATiEXLdnFqLNF71khKimZlG3uqdBY1RzE0VJLngmWUdD+ZnX7MZ6hi40mG62yEUIpPZ2mE9kDCtInBkUcSaXi4A6Uzf8ADX7y3l454O11ShZa+KBXKyUTVrU6eu+KXh9X3kizsgonz6BCus+PtFEof7ZaW1Ep5EPFKcALHVIiaSgUwl133YZZs2bjgHElMDT/BhU7pvM+XlfXZy7KxFu0SiVvIEwqBfolBL3ttZIfmo7TgWFZvJP7Kqap2/FN2xPAlMRWn2fpqOqi1UGMrqMgsIz5gK8M6sDw+SN6ZK8Lt6hGSHBGrVTijNlAGznHRHiDIWgjaEdNYZf2t1w2elLJU4nblK9jlXEsmjwnIJnQ3NwE3mlBkXMTkMZATKNKpTzbGvyquQpbVSVYYu8KW4gUFosFUOnwcUMG9lq0ENm7jAU1BkKufYmiwHbYfDaImt7PxcrNq/Dy3BYYFP/D68XFYJk9UW5jsZD4i42b0pnqmUiQsaytrVVSTIn6TPiCoYhauXeFlzUAIqAZYlKXCXqk1j1i0j1///HSfP/xZjfWHnYKGE4JqyeAfFPk7ZzDBbIeaGxuxngFJRjMhdOG/Bj0xiwIIiOZPKdpWUlNE9U8MgRos9lQIntSWjxiRJ5KBBlcELy1Hj6pgDr8IO+txkE8j5TYa3JxWEcdFzQyudgqeCV7hhR6R03NTnAm6oWXbdRK6YfkswgW7wN15RdQ1f4MPm8uLr/8Srz88gtSt8NHH72PE088ZbgPPemQotlSGBQmu1bjHMU3mGvumbDUF0pUDhyl+B0zlcPYWhED7hbPx96BR/FtZQiTJsVLqWRGm8OHteIkVPK7G3//XNGOR7YyyDnlLkycSONkQ9nTcUzgXlweuhbtruimHt5jx/e5/8O7qn8h39yTwOA0NBnMwAaHrCKaSHRYWyXPGwEMtjVSEmHs2HGdi44OP51guUBk39uIEKDtHsEIufqzg++iTPM33DOnFT5f/ExtiXKHQKmln6lCRxeCGrkVajRhja8QBwUewo3CtdLfBx54sDTRE2LpwgvPwRl/nICnhLvwtzHU+43A76afeUBUAAo5QzYKaLU6uP1ySwX88PN9E+qCnap96nwajBs3HnaeehuFfHH83vUBT1u19NsW4KKqqIWM1KDZqGGl93E44BI1eDh4Ip4PHk62ulFvZpTmfLSETLAaxksx4ylEB0JgGGWPj1AUaj5OQzfBejaYdJ5KpPWNCKXHmOTXZaaeSmojVWSkMU44/Tx4no9aqdSqHYurMx/HSdaLd7s+fdJe2NzOgGMEqHZ+0yeZsye3GQZFEFuFEkxeSAM5oKYtdWZlaEjmZaJQI22LP5xnxrxfTsYjr7wU0+ME5FZAPTt0pNKXW1vw3ezHsd/30/FLdUhSKqlUKslmIOTp6PS4Gwmoq6tBvsYPPROAyHBQZ8nJpkMIg04Nh9Q6RYuQQ53kFwncdqo69Isc3MGu1MGBsFe2C3cdoEaOpwzJYtQd9lQSlfH1VHpNdQaODNyLz2xD/x0aKaitrYHClCNdzusWTOAt2o/+3k59bcn364orqNr0/vvvkdSvKfREilRKYVDYj1+KfylfwX4ZkVW1WY2pRwrZSGmlarDT41X6HSgooOaJ8VAqkZYpAodv94WssOFNnKf4Cvq2jZgwgZJKBAaOmsR1eKNb/Hos9ZjCNWEOtmF8QV6P68IEhJ4NDElFNNGwwYj54qu40Pg0KnbWdCqVwosOSwfd1I83xG+RGQrS70gQkZkihz2PdComrklJujCppKPqDqWBqrN03PAQBIlEbQd93+yN1Kx72rQZeOihRzF9+ky0t7fjlwq6sVXV/NB5H574G5GFgpwAE4tRt8dHFxPz8tVQ9dNOwbpoS169i8XUqdNhC9Kqf8ideBWQz0oJLWcoOgXctgD9vhQW5KC9vXevt0TDxpjxSOhEPOo9Qvo7WqWSongh5tTfIKUe9ja2ptA/CIHxTONEnOC/E6szj4+BVEo+T6WGhjoQf9V/biyBa687IOpoIYfR0u97mkDnBLvdHrVJNyvPn2biX7cLOtIn42vzSdJlTcWnvT5Ga+12XDCVjlVl4y5AaSbdWDIa+r03K4NDMi+HW99KzUAhY4FWG9sGl1dTEtjIDg2Jw4cE3P3Ndlz+/iaUN1hAOn2IpxJBVmY6clw7sEhdi0BIHDF+SlOy6LwSSislZqRDfgwcy6AGBagQCqDW6ZMyAc7vpMUia4iSXxkZ9FweCNPSXLhtXzWyhORQbxMy95T3Pdh48EcIjI+vV0+6nirzvKPQUzNeqK2txorDK/Gq8l6M13ftTb8PTpd+p9s3Q/TSNdsFF1yCrKxsqT31zTdfG7ZjTlakSKUUBgWtIKszmMg2LpysmjAOYQVrsGhz+kDECGIoiAkFmXHzviGkEnwOiTg61f26ZMoZhi8QxKHW13GH8jXsG/itU6lEkKahG1O7P7oeaW8HNTS2+USk7eKppCQ+KuTzZHm4nYlXUSQaC0vS8dUVB+LuM47oTH4rLR3Xuej4ZkMrnPv+G+mLzo7bc7oEqlCycLTyHSmppOUQN1KJRNj/YsvD1x1joDHTyguvoa9ZbzBI148WSK0aLjqONG1fJ/2eNm26RPq8+OJrkrrltVW0kqmq/gE7LXSs8vn8aBIz0C7QjWAsSqVGp4Cfq3mwuTPA9jMeaLx00VprEySlklMmlQTZByKRKG+mmwCHkrQaRw6FMQ+8yELFcWhoGB5fJbcc5CAGvDGRSuT2guzLZHUmX7R9soMQGPUWDxzmadDmToz4fiotJZV0DClOJBupVA/dnqfjh8ILsCrn1M6IdlFDCw06RQiqoAM2W3TnptVqhVZvAAsBZjKY74JD50zE58Ji6bKybjkYeXPSHfyvD8OsYbDdpsS+R57T+X+FPC+bFERBnPhWnZYWSioVaCkZJBp6Fp8iBaejc46JGRq1Wq3NKyXlapUs2mu2dyqTCWYUmbF23BN4g70NcwppUXMkkEpTs+UCRDolx4YDq/d7E0esnI8NteS8SEJSyW2X5iorT/cfkSqVOhT0+ykkSasjMftX7nU+blnDYnlDfInYLBNdZ/qjy+X6U8HeWIlZJif2ZjfDbO4iJhfOmIEKsQgKCGjY8K30P71ej6uvvk66/Oabrw7bMScrUqRSCoOCnrihkokvQt+FzAxaHTT56WZvJIBd9wJWqy/FtaFXMKkbuTNYmExp4L0u3My9iQvF98DKKRYEFet+QDHTCqegxru/N2L8+K6F/R3M09LxzNHTBWCkCNjp7R2B3U97ta5rgx10JW98bLRQKlhUV1d1kkrhRceyCju8M85BsIAu9uMBRk4CCyAyBYwok0p6pThg3HSkIATHP96vxRGPboY2ixp76s30nDPCK/nLjBYQcvf3jDuxpeh+CFXLpf8RNVB4Q/HUU8/h64qQ5LGhtGzFda99K5EVrT4lZqw4ApdZuzZv0UCn0+K3uhAOeMWDtvk393vbqVpK0Na0uZGfXwBXSK44D0H725f+mbgjeA5+DUbnx+EuORST/K/i/I7z0NhIfB6GHkFnGyYxdUgPtnSOldHAYDBC8NENbYstOXwzRppSybX+K/zfYcU4fW7kylyVrNjRiV44PN6kI5W04xei3The8tYJQ1QZITJ0A5+hZSSSKFql0rVT21GlORPnu57Z7frJBZmotrPYLJSCEUNQV33Z43rG78AkK92wfOWZBTDsbsUeoyIwJO1vRKlE3gM1K3S2kcaCuTNmYnktj/VlO+H3J76AWNnuwVWKD/AudwdOmc4hLy+/K8FWT4srnBgEE3SNGFJpj9l03K5lhz75LYxjZ+Yj01EJwe9OSqXSVtVsTPS/iiM2Hyr9HamnEs9SEkqJxCnpPvvsY9x5560DFgzDqenKnLHY1OyGQ1ZBxwsLma1SWMjjBT3HnRS6IMpJt27OjAMmd415xIuxPmNP6bJ9+y+d/997b9oWFy5Yp9CFFKmUwqCQAdpKwqoii8HMyKaVLyOTXAvOfmGtQDZjp4u/OJl0h5VKIa8LdtDFD+/uInO05e9Kvz9qKQDpcps4sYtUSlP4pePJVEW3WHPaaSuLk9k9XUKn1WIP3zOY4HoeFs/IbxfRbH0Thh+uA1P5HerraSLM2LFjOxcd/iCPbY0dKG+J34ZTIfJRkUpjcmhFRM074+qpROLAwxUVAlMGXVQTjym7beSr0DoR9ELZvgX69g1osDgl/wzioxHGwQcfhvMuvxEr6+nnsg/W4ffqDmgcdbB+9xSmqmwxK5UITAtPxOPLdqJdVkv1hgkqek5XN3dIbbM+L71tviLxRMc6fixeCR2GGi66SnemUQcBLBSGdDQ10fa9oUZOwzf4Vn0j7jJ+GJNSiXhIvT32E3yjugGClZLKKUReOSc/Z8xUoqj+I7B26s0VFanE+BD0upKOVDowy4rD2VUYq+pmyM0wnebZWUY1bLaOqD2VMuX0N6YP4//MQAs+Du2FP4QpEAwkhbML2k0vS8qubV4zGkt6thoq9fS4zKxvyEilItlzql00Ic0YWxIVN/kI7PeKD//8wT8kChfimzaX3YGZ4jaY1YykCg1Dl5YDp58qdFnP8LTzRguyWZ2SRgtAVk3JsB5LWhr9DiajUsnmDUIEC6eLFrcj9d4LF8HVA6S3xgpi3n/FFRfjyScfxR13/LPf2zocdE32r/zluJV7DelCfN/nDKNWCgsZo3IMCcE70iCF5PjpuKAwFWJSTs8xT7fofBzjvxuXWE+D3UsJvzFjaPBLR0eHZLKeQhdSpFIKg4JZpJsjtWz0PBC06XRBZVIDoSgNMYcLGiddVG/vYDF5cnxMusOkkhjwwCGP8wo/nUxCXgdmuygr/uIatxSLm5HR1VLFa+WUAjUflZGu3Wahv7XUiLc79CqF5EPEc7rOCXoko2LNV9CWv4OdW1ZIUbikJSonJxd6vQFKpRKGtEw89s57eO2dl+P2nAo5hSTIROZ/MKOYRjZreFfclEqBYBBBMGA4lfRaCfTpeVjPl+LX0HRYOoYv0SveUNhplcjP6qQ4YUL4ks+2O6677kaUC1SxdQC7Dt9ubeysDBqNsbW/kYQ5ktRnnHcU3ttsQVs/5q+CJh3tXqDWLqCgoAB8gJ7sGWJiF+ikzdEvfw8LMqJr+cgyUFJUoU9HfSNtmR1qiH66ULPLqspoSSWCyRorJrP1EJwjYyOZLAi3WV25SIXsPx4AZ90R8X3DJrNEMRn2LksW1LdacLHuRzyt+h/G2lf2uK6Fp5vMohnzo1YqkQ1kpoYqewRt70qJGRksng/9Bad5rod/zAE9rvNnzcTGYDGeUJwJc8nUHtcpTfl4m98f7wv7wTlE7W9FJnrONYsZyNLH5jtHSN3wORstSRcLqiweTGZp8WhTq9CjuEDWTq0B+jreWb4eIwFVO6vwQOh03Bo8D6pSqpQYDnhXPosn55bhpkOyk5ZUIhC8DmmNp9FEmOynkkklJjFGyy+88EynQomkhX3+ee9eat3H2zONa3AB91Vn0Eq8kJdPlW5m3ioR4Cn0BCk6F8lLJDZ9dwK3dNx0+LJmwRdi8FVZa6cSOmylUVNDPVtTGAGkUlNTEy6++GLMnTsXBx54IF5+uWsDuHXrVpx88smYPXs2TjzxRGzevLnHfT///HMcfPDB0vWXX355j4UC8eJ48MEHsXjxYixcuBAPPPCAtPFMIXpo5QFQYYjMR0afUdBpAtjWlhwmeQMhL0gXK1tr2uOqVCKpJAR2OcVNGaSTdusf70upUlViPn5ZV9Wj9Y1AnU5JoUy+JaqJXpRbblzi7v5XOhWHMf5q2H97B05Z6TKSoXTTzXC1i/aRl5SMldqlyA9pgcviPHhXfTceY/8rmXzGAzt8mXiP3xdlTGQeJKJSNupWxs+ou2LHFniuEWC5OaNTqcSptTi641qcEbwFrZ7RM879vna19LuOpxu5qVOn9bq5OejSh6XLpF/+t/JaTFTU4tcLTDhcvSbm556Sq8GGzH9ipfpyBPpIfyOtdtv2eRo5/+fC2iYBeXkFqA1l4+GVfmwXaPJUouD08dhTUYb5TDlKsyJTkYaRplXifu4ZKSUyaKNj31BjkokqC9rqqzuTMqOFI0Q3kqJ/9LTzDgXCipi0TFq82GSJomWW0+LRjoOw14tuOD3JVRVvcfEoZeiag83smYS01nw4nuaPRgsyYlMqqUI9TL93xT5TiiEEvAhyWmxr7ak4+rS9AMeE7sMnnlk4de9ZPa7TpufjReF43La8S42RaKVSsaxUahIzOwnmaNHk8MF03O3IPf3eISEjWtuakcfQz21La6jTpJuAFOVafPR1uKyJJ8lJm9hNN12H1at/j+n+JH2wod2OddwsvBE6BDmFkXuaxRvlNXUYp7JickleUra/XWf6AV9lPoqDQisi9lOSIJPf2gSQSkQpTkglgnnz5ku/r7nmik7FfG/Jb+SM08nHElZ7xgu5OXTPla7wRk2Y/1mS30rSaQGuLpQh8QPdQfYMpA2U4ONNTZ3XjxlDCai6upGVZP6nJpWuvvpqiX3+8MMP8c9//hP/+9//8N1330mpIhdddBHmz58vXTdnzhyJfAqnjWzcuBG33HILrrjiCrzzzjvSSXvzzV3eFy+99JJEOj3++ON49NFH8dlnn0n/SyE6BAPezt57lRzHOBCU+nRMsz+K8f7XUWXxDmlFo1FOcIsKQQ/0furrsb2sHMXFVPYYD4QreRYPXZCyPjppT2n5TPq9MjgVEIUeJt0E+QX0GDJsZVFVHlwBAS1iGqyh3RNdCMl3k+YjPJ71PrIDI595zxSoOqHZEepMfguDVBja7Z7OSpUrTqk6P7tLcT1/CX5S7BPR7b3qbHzfmo5vaxVxI5XcdpqGEhRZ6HRdnzPD0w2e1TnyVWhheJrLpd/bPcbO5LfeoCtdhO3jLsRxzhvgVZog5ozFkkJAKcT+uSvVOmQq3FL7r78PUunnHRYc88IfyD7pDklBlZWVhRp2HG74PQNfu+KneOwNHZ4gHlI+hffVd2GiMbrNPVGZLFRsx0J2Ww+ft+Hw6rPZ7NJ7p9VGR4wRuIN0eVPCUoVmCtG1zxrktEheEUUCGMPgPfXRqDrgQTTJ6pBkIcp4lQ6FDB0fQ+aepO4fhWfjPv50VAn5UVfziaeS2U/Jqvzc3j2IZk6fBl81VcmsLSuHeged4wk+Xkc2JQzUbdtg0u8SoKFgcXBaB2y/vAKPK/HKr5aWZnT4RGxjxoPPmoZCc/TnHYEi5MeK4kewc+JDCSdyfMEQDA6qpmvwKOEMkOS38T2USi1uuhFU+ejnn0gQVcqLLz6Hu+66Pab7S+EIRrqezjf1ny6aaAhyWyhR4iWjUqnIvRlT3StRpLBGRSop5BRqHRt/UomYN5O2KLLm/OijLzF37jyJkLvkkvMlwnBXEOW0TknmXbFHEnO8P8M0DQObNaXa3RU1NdUYk0nHuU9q2F6DmI4ucOIh1bO42fcw7HKa7JgxpZ3JcSmMAFKJxLquX78el156KUpLSyXV0T777IMVK1bgyy+/lNoPbrjhBowfP14ikEhV/uuvv5bu+/rrr+OII47AcccdhylTpkhKpF9++QV1dZQpfvXVV3HllVdKpBRRK/3jH//AG2+8McyveOShwuLDSf7bcY7tQujSaCvPgGAYuIOM1AfdZhs6efzl723Esc+v6owgjxQK2U/C4hGQXjhJan2JF8LV93Ze2+V5FApA1GdDVKjxww7aVjNhQk9SSdDR9zrfwEjS+0jxkX8xFvmfxP+8R/V6/XRVI86YpYSRH9mbMH8giGyRVi2rmjp6JMEQkMWHyy9IqSEEXmd8Xm9A5hY0ysi+Ix81mnGB6Qk8oL48bp5KPtlk3S2oenxXWYF+lzpGEalk8lDyc0uTr0+lkgSGQfoRd0CZTohFBgYtlciHuMjCBfozWVcxIfiDvS9Mm530uEIuq2QcS1RTyqxiFF78HL4JdH0fE4EOTwCZoLJ6XXb0nhykbY9A7RgegpkJ0GO3+0TJJyOWxM1AiN4nj48u0ODPjnA7hl5OaFVoovPVcbEGqPMnwhlInqTJxsYGjM8zQ8GQtlANBF3PIphJQyvVCo1R2hBGA6u1Q1IBEOTm9E4qkQ1mYNNXED6/A1duORHGby+DbtV/oVz1CKrtdGyequ1dIZyu4ySfI5878eul1tZWvLuFx/opt2H+X+9FrjGyVN9dYdLrJcJdywTgtCSWVOIULO5aQC9vbKLv5a7tb80OuhHUBBO/tgkrFzZsWCf5tcRi0r1onAknKX7BYlPiSbB+ISvvMlT8kLQxRgtWTlK0eIWITboJOjLm4Gj/v3FF20lxPZ5gMIinnnpcunzZZVdK7XhPP/2i1JWwatVKPPjgvb16KhlUdK4SRAYaTRQkfgQQ1V0qX0fb8KS5JrtSyc9q0SEa4NP2nnZpUHE4kf0ZBwkrkMbR9V5YYEDun8IIIJXIyUiqk0SJRE7UqqoqrF27FlOnTsWGDRswb968zoUm+U1a5AgJRUCuJ4RRGPn5+ZKfBfk/6RknbXULFsizkCRRnIeGhgZpQk0hcpCK0BpxCr5zjoPRHPmAzoboYrXdPjRtVkSuaG1rRCnThM82R9dyJ7TTCtg2C/Fsia+6gHgqEdjNVN7c3t4KKFRwHP4sLH/bgFXldALobtItHZOcZpJniC6pxumnlWddH8miAdm8kOG9Cfd7efOPemxtToxHhK29DhwjICgqUFZZt5tSKbz4sINO3n5XfNQYOkcNhLJvUaKPbDOlkSuQLKeOm6dS0E2riS6hp7fQi6ZnsZb7Gya4YpPkJxtIfHS4LXVVOTWTnj69d6VSGGcdOLfTRJhA7MNUNxIwsicDAWlr6Q35jd9IqSu35fwiJb8RZBjUyEEHCpnEVgyDPmenX4Q5P/r2ifQsmvil9TYNS2u4tcPSmcYSi58SQYCl5zcjK0BTiK79Tc8EYmrHOJxbg/MUX8Eoh3gkA0jryQR5iWLTFElEc3ekK3kUMW0oMIoxKZUytfKmUNN7+xshlCeYWdRt+QOtqhKQspp+9X+Rtvr/cAj3B3hbM/ab1rsK+q8dD6LuGiPyuMSHLLS20vUR8R8cDNRKRef86olT0aYvEJX1GJ4W/9Y3BaT3uqSktAep9O0OP57mj8KywGQE49Tu3heammhiJpnTt27tacsRqUn3SRMCeFD5DE4I0VTA4QKrk0klZTAplUpOG92ztfsUUSmVZo0twrcvP4b1n8U3Ev7jjz+QxpqsrGyceupfO9eeDz30iHT54YcfxPLlS3u+Bqezk1TyQAOtKn6FawksB4dIi2CWIVAqEeXgSFMqXbZ5Jub4n8WO9AN7vU0obRxCxmIwQgCqhhXS/1LtbyOMVCJKpNtvv11qXyO+SER5tO+++0o+Sm1tbcjJ6VlpIn3Tzc10QiTkUF/Xk/sSdL+etCUQhO+fQmRwBejgIfhdUS38r9B9i8eVj0BvjX7CjXUD+qrqPvysvg6WnWujum+dm8VvoWnYmH5gAkgl+p692DQBJ/jvxMqsUySy5YEfKlDWIaCykhJaEyb03BSG9Hmo5HNQY56LHa2RL9zdQaHTw6c31PBUJityiW1bWF1jw8M/V+GcN9bB0o/JcazwtFOyoZ3JwM7q6t2USunpdKHkEOmiN+iKz6L39uwfUDvnZZyp/DGi26slUkmERsnGTanE++j3wR2OrpdhDrYjg/MhWxgdPfXNDh9qxBzUidnYXGuTxveBNkIHiL/j49yXMJOplP7WmqlnTCxQqLuqiaFA7+ovtbNGSl3JZBydpNJkUxCrNJfjY/WdcfPy6g3TtXQD4AmKSM+NPpJalUE3uNlaISo1ZLxgt9HvqVdfFDupxFGFTbCPzyeFvkklDQeJmCdQaqMzej9f8TnuUL6GfNaaVEqlvPrvpcuGvN1J1nkt72C5+irclL86KqUSUaK4XS583WRCa8ZCBNV9b2zD7bm/Obqq4W2KHHwU2huurT9jjz3m9Hq/VrldndHFdh5EClK8Jec6UWsFNel9tvVGAlLotYXnV3fivwekQOBVZmBTSwhFRWOk/UMYZG74eLML9/pPwXJhJqyexJgzd/+uhbFmzaqYlEqTdXQe59OjS+6MN5SyV2o65086TyVCXgTktZvFz0alVMrPNCPQvAOettqY1GR9Fa8ff5ySRxdddGmPlu3jjjsRZ5xxtnSbyy67EO3tXQo0kh4Wti7T6k1I18V//b09VIitQglszsQkSFa0urDh2bPw7SPn4I6vtmEkgbSvcWa5UG/uw+idYRAYsz+9uPNHVFs9KCmhpFLKqLsn+tAsJAcqKytxwAEH4LzzzsOOHTtw9913Y88995T8R0h8dHeQv8ODA6kQ9HV9WBHQ/frw5WgHlxgU+UmD8LEP5jWwtmqco/gGFUob0tIOi/ix9lJVYLGiCs95pw7Je6hkRUxj6Ym/Z8cn4IXjJa+CSLBBswB3Bm+Ft3497j+8NK7Hq9VqJL+QCrsCreIkHOS1Y3VZLZbb0lCkCUjfc3I9qbp1f14xrQRHee6EV5WGQ+w7Ij6mm3QfIFflwSfiIb3eh0hACVRMIKGfS7XVhUsVn2KlMBXP/JaHWw7t2d43WPjtlBy2cjmoqaEk4tixxKibXh9efISrNyFvx26vN5bzQ8VQiT2r1kV0PzMcqFKfCXaSiH/5/hGX91z000WDV1T2eLwAyBjnRshrH9HjVhikjfWq4FXI4gIobz8Be+89HSzb/wsz73gHOfafALkQqNSRtqrYnl+r08ErKKBlQxCC3l4fR+ej38OaDl5SypLbGNKygDbAAC9a/UGk6WNrLxkI7pYq6Xe7l4FObveLBl5NNgglU5BtRlNTA3JysuM6dwyEH5X74VdvMbZat0vkeyzP5VZlo1VMw06uGAtHwXd+qOB2u2CUK+cEWr0xqveftJcRcGIwacaahoY6TMygcz6TPm6341LIiox0LoiODkfEx01UTYxahxvznwEagRVyq0lv9582bTo4cy6ebpuB44pXSmqlpWknw9vQDn7Hr5g69ZFe7+dldKT2AC3LJ/T9tFrbqar7Wg7Or/+C+/Mfwe0n0Y1ULHDKpJLodyb0uD/Y0AgYz0ebZhze2nwpDjhgfI/ny8oixIiIkMcOzpgptQbnkfjhBCuVCP74YzUuvPCSqO5fXV2F8RNIyyCL2bPmQe7iHRZojJRUSlN4YbdbB/U5xnvesHsDmCi3eLfbfdK6LtLHNmlY/GOJSvIycrudUKsjCxrqD99//y3KyrZIqbvnnXf+bsdyzz33Sy1wO3Zsx1VXXYo33nhXIl+7K5WgjGztGC3O918Hu8KMBd61CXn8hrrtOCn4Ezaw43DXTgs8QR56VVLTC50g7WvcXgdJl8m40Nf7EyzZD9otr6Fjyze4q+kk/HNhSbf2NzGmFv3uGIp1VayI5piS9lMn3knvv/++5IVEWuFmzpwpta499dRTKC4u3o0AIn+H4yRJlaK36wlz3J1AClczwreN1gt/Y58AAQAASURBVAw0MzO+hmrDgcG8hnR3Gf6lfAW/ZuRj3LiiiB9rI+jnpAy5kZWV+PfQ1kSjxwnc0GCnK4gl46k6bSC0+2nVibc1YfHiE+N+vKQKT1pVCObUPIfLfT/jXuUZKFEe09n6lpe3ewXGwAkg2hZHIBTxMU1HFcayDuwwH9zrfXhOD/DEvDryx4wFudaVOFn5tnTZdkgZ0uL8XC0lh2PBxhzslc3A7z8GHMdh9uyp0m+CwkJaKfbxjEQwjE8T+ny90ZwfKgVte9OnZUT0/mVmZ3aaMxIiLx7vuVKgiievqOrxeOWSB1AHVKJnSM65RMNSTit96gCtns6bN2fg1zX9L0DNT51/ak3mmN8Ls9kEbyAEwtccNTMbxl0eh2zOzMFWYuGEWosXU/YbJz1XQel4iVQin7tKST7zyMahaNHioYSWPchhTAyvcUXABEIjFeVnw+bu6PV9SuT894nyMGznXWhpvQXHLMqK6XOqyD0c1zReJF2+KF0fcSHhzw6G4WELcjjRfwd0jB9P5qfDKHsORYJ6hRYIQQrxSJaxpr29BR+t9CN/ySk4ceHp0O16XAVFwAbAHGiBwxH5cTc314CVlVw6lQL5ueY+z40991wgpeRuyz4AjzMuXD6bRdA9BY3PnCN5e+bn9946V6c0kqoANByf0PezpmY7MrQMTMoQTLAgO7dgUM+3VW4/VYvehB73q6sb0GDz4gCRKqKmT5/a4/nS03WS71KOaztm5JdAZ9wzocfTnVRau3ZN1M/VUl+FsQvoLi59whzAMHznkCtQijbRBJugg8/VGpf3LV7zRovL1dni3e4RpXVdpMfn7PDi/w6h+5BqLhiX1/X0049Jvy+55GJMmLB7Kyt5jvfff09KHP/uu2/wxhsvSmFUPO/DqoYQ/iv+DdeecyWyMuL/eRtVLOwhwBVMzNqe9dE2xHQ44edFrG/14Ng9aAt9MoOEeI3T2PDGjA+xUVEObeGzfb8/hsMgfs2hRGiGvbkC+oJjOoswLBuUFJHxwEjnFZKWVNq8ebMkLwsTRQTTpk3D008/LfkldZcPEpC/wy1tubm5vV6fnZ0tXUdA2uCKimg0e7gljlwfDSwWJ3ZJHxwxIMwj+fIO5jW4LNQA1R5QIBhk0d4emUeOR9R0VrAivc9gULFhPRZLiVgM7uHPxKm/V2GSOYJKlSiivIK2TwmOVpjNOXE/XoPBCE6w4DLFB1ji/Vn6nzNnEbZu2ChdHjt2fK/PqVcIaBOBFrsv4mMyyIlKWrW+1/sEZKUSmagT+bmIbds7L2t+vBPtBz0U18ffv8SM/a84AL8sW4pH5N5nm62rvUwtty59W6vB9Av+A23Rgt1ebyznhyaDjC31WN8GmCN4/7z+LvrfaW2Py3vuErT41laMHb6en7GFI0RaI2qc4pCcc4lGq8UOpYKBq4ESxqWlEwd8XWzW3ui+bXMEVTG/Fxynwu91PKZPLIXBz+/2OE4fj1yxXSKVqlucWGDMkG4jKvTwiUpomCBa6hugUyVmAfFreT2mk7FZkR7TawypsiRPMlbBoby8AkuWOOM6dwwEu9yiIvg9knFpLK9Br+wiQqobOhLSVjAa0dzcDp5RYpsvC9n5xfA4vPC7fFErlZQIJM1Ys63RBvfh/8GbzFjsx5UCuxyXUk2JIUIqtbdHvpaprKyFUmeAAiGY1GrpnOjr3CgoKIW/sRwhnwsP4WhMGzsbvzx9n3Td9Omz+nxOP0vbOA2KENraiIoqMaXsbduqUGyij90umqBXawf1+bkZOs8qg/aEfQ/cAR6NNuLNyaKpfJ30v4KCMbs934ziNKwd+xgEQQmr7tSEHQ9pZSLKkzB27tyJrVsrd7Pj6AvEv05hqwbLKMErjbB5NYBcdBwOKKDCszM/wL133gyPZQdaW+2SZ1UsiPe80VRXi8kiixBYuIOk40QX8efa7ghhiny5vqoaBlPvBs2RgrQ5Ll26VOosOOecC/s8joKCsbjrrv/gxhuvk4KmZs6ci9bWdohphXgvuBhVX1hw5xHxLzRJPqohkv7rT8h339tGfYWaQImVD1bXYa+i6Nqmh4tnGJfOYpKqHTahBR5G6Of9YWDOmw9l40rsy27EW6vmIjc3T0rMXLduM/bYg3p2xoqhWFcN9tgiQdKW7sggTHoVuyuOiFk3IYKIx9K6deukajAB+U1MvMn/CcjvP/74o/N+xJib/JD/E1KJtCJ0v55cJv+LdOAPgzz9SP4Z9GvwUO8BB89BoeAivl/4S5cZbBmS17m+gra+1QvZsH73NNLat0T2/rjb8HzbqViuvhK56hA4Thn3YyPJRnlsB65RfiAdY7lQjJIpiySJbDj5rbf73ap+C6vVl2Khojzi5wpHRCuN2b1eLyjpIlCn4BP6edgcXYO2puwdsA2rUd7sivvz1FaHCYexPf4fNnT8soqFd8bZ4NMmxOX80DD0/VWo9BHdXsVxEsFAIATccXnNVkURDntkC76tM/b4vy/c2ih4E/rZDtXPlaFXsc14OY5ofLqztWSg+4SMReAzqC/a9eKV0M8/O+bn12p1OPJND57yn4CQeezuzyWIKOHo+FjbEUB+fmHnd88hUELb77Il7P352TMWtwfPwZeembE9xpglmOR/Bae5r0NjY+Nu1yd0/hNE5AaqkQeL1M5J2t9ieZw0s1kipQjs3sSOaaPph3gqiQEPlrhX4N3z5oNlmKjuL8jJiGrRN+yvJfzT7BahGTMTzSFDr9eHZC8kotQh6XeBQDCix7VYrDhmIotKzVl4XPh3v+dGWloGCvLz4ZN9He/5dgfWbdggr1nn9PkcvJJuzkjHltebuPeUdAIUmejqrEnMQJ5JM6jHy8rIw/JaHm1NzQk75qp2Dy5SfIE1mstwuGKldOzjxu0+n/MautllhSDgp5u2RPw0NtKkOzJmTZkyVbq8Zs3qiO9P9ilTxlP/vTrFGNJUk7BjjeSHJNmes3gc3Ft+lPZZRNkxmMeL57zRKGZigv81LG65WXpccn5Fel+tWouASPvgnTbLoI/l0Uf/Jz3WySefhry8gn5ve+65F+DII4+WPMwuvPA86TNX6NLQJBiwsXFw729fP2dwP0ihIaca1ybk8RUuqoxexJbjMeWjWFFtgWMEzLnV1dUolse8nPyxKEnX9Xt7/5j94TCMhx9KfLmlBUVjaAAQ4SricTwEw/2e9HdsI5pUOvDAAyXW99Zbb5XY/h9//FFSKZ111lk4/PDDpcHtnnvuQUVFhfSb+M8QM2+C008/HZ988gnee+89lJeXS4zw/vvvL7XNha9/8MEH8fvvv0s/Dz30EM4+++xhfsUjDzksNRN0+aIzPlSr6KZKFxgaI0/eRaWZbV4RzrWfo+7XdyI6Szh7VWfMZ3EWNbGON0gCnMXRVQV+P7Qv9p2YjYqK3k26wzBzQWQzdpjZCE1oQ35oOfk1G3o3NF6TcQzm+J7GVTu6khETgVf9+2OM40W8vp1uPmyfXINzX1+NzU3xSQsyLL0Fhh+vg6d23W4m3QRhUqnD5ZEM9xrt8UleI54XBEptZBHcmTpSy6ekEtnExQNuN01U1Ot7xtLyrNwaDJq8ONLB2SrB+qyw2GxS5X7yZLqAHwiBUto7f+fE2kEpV0irtGbMLKwL5GBF9e7jWJrCB61A/a3q7IKUQCr9Py0DNi81wM2UY8gTgc3BfLwaOgybxdjCBbKMOohgodCZUd/QZTw7FCAeVZ8y12Gl5u/Qih6kpcU29uarfXhP+x+8obwHTj89N1MYGIRUGZfO4Ij0nVBVfRP1/cdkUBJE2bwJyQCyGdZwIVys+AzH6Lf0fhs1/Y5JKW4MG7FZN0l+yzLSMZxRDNwiSMhvb+Vq6XKVxYPqvH2ky3PmzOv7+DX0/UxTd43viUBrawuKzVSp1CxmIn+QvkONJSdjn5c8eG1z4srule1uTGbrkAVbZ2rfuHHjd7ud3pwFu48eB+tpS7hJNylSz5u3oNNXKZrkt6l5dF1Ux9JOiuEGsQkJW4MkUwKczUvGdAZ2WUUZjVE3CUchSWsEbsfggihIAfjrr7+Q1iGXX35V1xWiCIWlHKyjZzoYud3DDz+GwsIiyZT9t9+WY//iEP7JvYFDQZPF4o0MFS+FhuRwiVG9aXy0a4XgaMVKjBEa8Etlz26hZATxQwqPeTkFY2FQ99+85Z17GTxn/YylusNg9/EwTt1bfpxUAlzSk0pGoxEvv/yy1Jp20kkn4d5778Wll16KU089FQaDAc8884ykMDrhhBOwYcMGPPvss9DpaMzznDlzcNddd+GJJ56QCCTiW0PuH8b555+PI488EldccQWuuuoqHHvssTj33HOH8dWOTOQxNOLW74pyoNLQBZwyNDSpPD/5JmMv3yO4budeWH2hHk9OXQGhibaX9YdAK1ULVYkFmF4yuIjdvmA0mlBfW9/597asw5GlV3UqlSZO7N3EOigbC2ZwkW1MGb+jkyALantv81Qb0tEBk9QGF69EjN4wo+1n1D18Eq77rB1WrwjebZNi1knlNh5xv4Etn0Bb9g4a2to7lUrdEU5/4/In4sGXX8MXP3w96OfkeR6aKEmlLINa2sAQiIH4bBZsbg+gUEK3C6nk4LKwURiLhlBiyNGhhsJGCd9t7YJEGobH/oHgLzlY+q2t+xkQYo++1en00JTOQblyHFbs3H0Dyvid8KZPxbb2EDw8I8mkCchcZLXRc9HIJ45UJybiBDnmyN6XXZGuVUpGwgyrQIMl8VHm3RHw2DrHKpfL25mSGcvYukhVhT3YCjh8KVIpUhCl0tx8BY7XroB2/bNR35/VUJm8Wg4uGG4Q64OZOQxuVr6FswNv9XobQUM3pKSlNqOoFDZbZKQSITIy9fRc8ysH/p6SBDhvtwRaZ/lv0ljSV/GIwGKaiXf4/bHWly8RfokklYpM9LU0EqWScXCkUrh4k0giorLdg8kMTXtd3xSQCtHFxbv72WRkZKI1QF/Pu7+uT7ifEkn7nD9/YdQJcIRkeNk2F2cFbsKWgpOQDAh+czP+uMSEo2eYkyoBzualxWy/w9rj+xYJiPrSLdLvg9cd3Wv6o86G+77fgaeW78S76xpx14vvg9GacOMpSzB/yy3gGunnrf/t38h4+2BoNzy/22OQNehTT73Q2Uq4KC+Ei7gvsEhIzHfTZKZrXoM3MQnnhgAt3IexhN2C77YljryNZ/JbWKkUMkbgAcWwULAMjplB94Oe3Bmdj5NCknsqEUyYMAEvvfRSr9fNmjULH330UZ/3JWQT+ekNCoUCN998s/STwiDgp4NxUNFzAzsQpNjdADE2xpDA4hPRgGyUZE1Ca4BUXAJY//UzmPu3J/u9n1JWKpW3+jF9fnwTysIgm0ynzYrbg9ejQVOKOZMnSH35pE+XoK/FJkd6wN2khTCySSIcveqEFnlZvU++c4oz8OTzL8Jfv1kyn1OpejcOHSzCKqxLr/sXrl/9Gd74pQzZf1NC0e7Ga6vr8bfFuy8KI4XI+2AW6PdyYw3dGJSW9lQqhStae6or8Zz6G2yykCjn4wbximji5E/+achjgtAYI+/P5xmVlOzDBOOjWjlGvRT33WLCc96eJNVGzWLc7zgWWk8jTsXIRnWLFZkOupEobxew6EA6sUcCPo/2vbN+m0RMhTL63sz1B1K5fXbKSuyv/hGfd1wLoGd1vBmZKJ94P066+gCprTocEEHM4j+t5LCi3o+D99JjcE4OvYO03s3iqqWK0dj0yA2Wu4MsnP6jehkTUIM7xaGNI1GH6HfXHeIg8AFpjIwFukzaRqJn/HB5SAEjMePZaANRwxTkEZWEFWUdIgqivf/EE3HE3S+gwirgNEGI2YMlnslvE+TkN6+hBL06Qyi1+Fx5GOq9Kig0m2G1Rkb4WiwWTNeyXeuaATB16jQIHjt0O5eieNo81Gz/FYsXLJTWpH2Bm3oUrvigCraKtfirKzGR4ASk/W3/TKLeENDKZCJDPzgPsiqvCrmn3wu+vVpSiyXCC2pnmwMTGErkbGoJSd56vb2XxES3za/ERATg7qAtaolVKhV2KpXWr18rFZ3CQSEDkUo243gsE2Zhv4LY5qZ4o7pmB5aYvBhbnJtUSqXT9etwTvFHeGDNWjwZpVKJwCOTSgF3dAr5B3+sREV7eH0lYv8cPX49R43FOZuABqBt2cM4w3MtDudycBOZzyq/gnvvOzsLiGEsXrwnrr/+Ztx//z0wqOh1PAk5SABKC4vIogQGf2vcz0VBFJERapckKqSQpu0ow3HmStTuEe3MMfSoqanGmInkPedR6TcjUmvxJcV6fLOyBVYus1sCXApJrVRKYQRAji8PKSNTZoSxxrA/Zvqex5n+f2Ao4AxQ2XO6Xo0tBio3n+JeKZki9gedi7LPG9euw8SJsbWRRNL+RmBz8rj34jNx2tzCTtIlJye3zyp9QSFV36TZtyEUGlhxYbfb0SKmoUVMR0567wZ6++d4cJ/2NdxetFqqVicEQS+emL4Gn5+uxdxZ03HXs1/gqMMPR8cPz0lXP/NrFaqtsat2/B10UUe8ijZu2dpv+1u7k7aCaUODb7vz+/243XMaLgteDS47ssUgmdxrlZPw8Q4GVm/sqpnu0MEvmUATI+nuMOvoAopnkrqOEBEsDdvAQoRd1KLNI0obtYjBcrAd/Trci25EKH1CzMdAPJVMCtqCyvG7nyuPL9uJ65a6YFp4glS17o7Xa3Jxw+/p2OpJDMnh8AVxh/I1vKe+C/NMkSkuesMsRTUWsNuRzhEfrqFzjlTy9Px3Bej4HCupZMgsgiAfdoEiPq21fwYQNYxRbg21h6JXq2wIlWDd/H/BMucCyZZguNHQ0IAJRqq8FdJ6qla74+W0K3EffzrcnDkqpZLJR0mNvBza4jqQUomg7sunUFT7PRDiBzR4XVSSDvWmj+He8lPi5mVZqbTdImIbOxH5Y2dJao7BINtXhfUTn8YPc39KWNte0FIlBYv4oUK1TcT48b2P6USpRHy1CJS+xLXlEP85AjLmT5o0WVJLejweKWo+EhBSicugW9sx6bGpTOMNPycnGuq5pFIqFfgrUdL2I6apW6NWKhF4Qce4oC/yc4rMgw12LxgIuHnMNnytvAEvq/4Pi3N8EBVqeGaehy8KrkO11YuXWsfDJWqgcDeBa+ldgXT11f/AsceegBwzLcyHuMR85lr5O5WuoWby8QQZJUpm7o+OnMUIzDhT+t+c0CbsPTa6z2M4QMigMWZKg3zdEFkBTln/K/b7Yk98kvkULphC71tXl2p/CyNFKqUQM27w/BV/C/wDGzhqkB4pzCYSPKnr9HlJNI7mVuAm7k3MM7ajaOHxUtx6CduKuvIBZMnttAVthxV9LlYGC7LoICAeYaR6QGKvB2p9I9BklUq/8wxMRBN9lVCARf4ncZDtVilxrjewPhsumqvA6TOUCVsErt64HpPTQ9hnnBbjJk6TFBxPPPEszj9wJv5mfwrzmO248JlvEORja51wdaZQZMHZYZHeU5L+1h3Eb4g8r8VJNxp6YfAtBT6fF4ySbsC0qsiJm+Mcf8fVxW9gsyM6YrYvaBnZM0nZc3Fi1mtGDankb9km/d7pT+uxUYsUwTH7wzP/7zTSIkaQdjvJ0oGcN/zuG+dmmbDknW27kUqaecej8OLn8XlFYjaIHd4gMhj6ndZlxu7LYQgvRP2NnX4lQwEmQN8Xu59uAmNtf9Po9J0+Krni4Hwz/kwgxIVRHXvl3C+y0JTMhipvfFKQSnX1dRinoeeDOmt3v50wTBq6qWA1hoi/78RTKR10k1aQNzCpRJTHpEWLEHdffvm59L899iBK2X4gishJ0yNHz8DtdiZUqfTvH+3YMfdeHHb04D1GjSYT8pgO5CndCSMjHt2XqpIaAkYi+JVMuvsklRy0cKNJoJdnU1OXUoko9ObOnddp1h0JnC2VuCnzFxzJrkRxemJUK9EioJL9xnRsUimVWB8lfi0esYfvU6S4y3c6jvXfhQ2+yPXCxJvPGxTwnPIhXNz6L0xRNMAVEFGWfhgsZ62Ae9+7sc+cmXjy5JnYa2IBfhL2kO6nrvqy18cjqrrnnnsZc2fQtX6Ii67rI1IojLRdK9PARazCjBRkjR3a73Zc9FM6/vHGOoicjirB28uQzCAEYX1dDZp5PTpEA5Rpka2V+MwpYEJ+pLu2Y/+x+k5SaSgLb8mMFKmUQszYJJTiR2EuQnKyRqTITqNEisgNrmc/Uhyi2oBLuM8xVefE3vsfju+dlGSwr3697zuFguBcdIHg0RdDo9EkVKnkdHb5lnSZdPdNKrHmgk5SiUjwB0KbXa5OBDx9tiME5QmNbCgS5d3QXEXNW+uDZuTIPjPkeP5zWBpuz12Gu7kX4FKl4aTr741pQ+LroKRSO5PZubjb9bMjkyCpalkcdONvFAe/uff7vBAqfkWgajWMA5j9dT8Opcxr+ILx8R/Ry0bcrKonSTVF3Y6l7KX4RHsXRjpY2U+pvINuKKJSKsUJZAHrCZNKod2N3s+x/k9KWzkhq6bTpDsMk1KQPMTgSUzF3OPnkQk6nuhzeqr0okFOPh0nczl3ZwV+KNBho++LPUQ3CSQhMxaQ88seoCeYL4EtL6MNpPXZqGZ6zAnRIEtox8mKn3GkqQoeT+KMpSNFdVMbShn6+etz+1aRZqmCKGJakWHURmzULXkqEXPvbr5M/YEQSpMm0TDz5uamiEgltnE1VhxdjV8vMCZMqUQ2RG1t1GyXJCTHA1oDjUZPY73oiPNGNow0V4X0u9zK9mnSHW5/+7bCj6f5o7E0MBl8HLwbewNJ8iIIj/nR+CqRz4AYKf9d+QmuVH4sBXkkA0KdJvbJZdTd1k6tHyxBpbSei7alS+V14etXnoTgitwzsN0dAMsAKxQL4BNVuHupH4d+WYjM05+HqM/p9MpcMCYdJ+9RgK9C9PNXVX7ZbziQUqBrXWGXYmC80BCga+BMozohBSJCqrz//jt4/qUX4cqi45lj28+S71SHJ3H+rINVZnq8PhxScx7m+J+FOSOycU/UZoLPmSVdLglVSvsXYn9BHi+FFKmUQowgE2BQtuRKM0RXISgwAP/hnsNjxpcHbEGLBzKCVB6rT8uVCIYVHiqBn+JY1udAz/BefC4swfLQdGD6XxJ2bOENE2lP251U6lsdJejzsCOYje3qGdhQM7AhXpudLu4Zvu+ksy0WoZNUcido8Rqy0pbCZt7QYxHgnX2BtCgnlZ8zA+9j7dKvcfLJx0Y9AYp2SgS2w9yrSXcYpP++3UGN4g3wAMLgSJ2AqwMN819B/dSHkamOvJUt7CvmC4biqlRS6nqq0QrSjRijsqMAPQ0VRyJI7/uy0AysaBAlxVBfn3EiQdrffH2QSsRjIJ+vl9JWBK9DIja744KsDViluRynBT9IyLFNNgvQMtTE1JTftzJjIAjyIjnfyHRW4IcCze2UJHdp8walVCJw8nRjZrUkxqB01CqVVEzMlfMc93b8n/JZXJ72q9T6M9zoqK9AEUPnSDG977HinLYHsFx9NU4pbo943iEFnVX2dNTqZsGjicyRgyTAhUG+27t6/u2KGg9dZ2WY9AkjlUgRyef1QpVZCL/ShAA/+HWZ1pTZadjutCbm/BOMhQgU7omlVXRT3peinJBKn2x24b7gqVguzJTUnIlAeJzMz6ffhfnzI0+AI4by4wx0/m5XlyTEgyoWiDJZmq4WIm4LHQo0t9BCh41Ji9pPiaBQKyDQtB1+V+RE2bhMPX69eh8cc/JlmPECg9t/8uPMi67t9bPao8iMP1TzJSsGzlELrr3vFshxRroPOXA67UCIN3SmLDSImagVc2GxxLeY9Ud1G55aVgHtxD2lvyv4fIicBr+VV+HF3+vw447kTIGrqaE+SJoMuUBvjFw4ECjeT/rduukbFMwMJ8ClfJUIUqRSCjHBzws4k/sex7PLkGeMztAxJyMNf+V+wrHcStg6Ej/gpAt0IszMK5Z+GyftLyU/FLIWBOr/6PU+otqE673n4czgLRifmziD1+7tb2FUVAzc/iZqM3B4x404k78VDR0DLzTHt3yB91R34jzd8j5vo9bRY+EYAV5nYhYPWh9dXHYgbbeFi3tPapx/S9p3KOFrsGrVShx99GGor6emzJEg5KEb0tagfgBSKQNWe9eGJxhlAsiu4H308yNVLCgin5zuVTyDHeqzcGpxa1yIXgNDCQ61ruf7qzbRqrFROfIluq94luCs4D/x1Gq/pFIaDiNgvV4Hb5C+l9wupJLVHUA+6LhW0+pE3i5tMbyCViO1YmI23PYmWr0nx2fKHLglpy+4lHRTWJhlGlKlUotyDJ7nj8BXtpJBeSoRWLhsyUtueYPcFppCZEolFf1uC8roSSWFmqokdSyfFEoloWMnOJYBzygh6HMHVGRkaJgolEodeCj339jXehO2KSIjcKdO7SKVZs+eM+D4pdLT4zIyPrji7IfSvfVtdh4L9+UumD88Fl+XDX4+MhqM8IuUEHNa4j9+vL++EQ+0LcKy+c/gwR+pSqA/TyWIAkQfVWBb3fEnlYiyOtxaVFBAN6pz586XfldVVQ6oKCd+ShM09Hs3Zza9XzKAkUklEkufTEolg2xb0O7wRe2nRDBd34Hr9lShWIguuYuMJd999SkqG9pQVFSM4447sc/bzR1bgF8Eag+iquo7ZZgJymsBVWLa3/TmbOzlfwx/CdyLxjinuTrLvsHztnPw+ZH0u/9hXQbaL9iC5tlXS38nawpcTc1O6TdroOucPFPknTPBQkqgcW2bkTZtL+lyilSiSJFKKcQEt8eNu5Uv42HVU8iWTeYiRVpmrhQXTdDWXI9EQhQEZGpo1c0gt4Icf+RfcI/9SJwfuA6f1/VOiHmDIQRkcmD2+EgzAQbT/kYXiyQlhCxABmp/I1CLVFba0o0c6QuEpCGmu6XebQOSSgRBd2Lk6hkC3Wz7Ndm7XeebehqCuXMkKfCy2w6QvGi279iBa665IuLH/6HoSiwQX8Orlpm9mnSHQRYhvN8LN+hnzMj9+bEiKCeISOqVKCqMCgZQMiHpZ7DwB0PQgxIcOmPPRZbGTDdTao5BKDD86oHBmFCHq8x8R2OPDdpQK5UcVrpJ2GtMz/Gv2eFFHkPPn1pbaDelkiCbcWrFxPjNuFpoe6DVx4LtJ1VqIOzwUHKgMCdtSJVKOzVT8W/+LLzcPF5Slg6m9fiGugMkL7kfgtH5/v2ZQdQwjzTMwLmBG7Ajff+o76/U0vNBzwaSwlNpbWUr9njahY1Tb9ktgak7NEZaPDIHWiJSKpG5mngFsVqqCjVruKiVSgP6KZHjMtCxnMwR0cafRwrSulFkYsExIkQwUW2w+oJOxcEGOoa47fHfWH67rQ2vranHHzvqpLASnU6PXLmlvjdSiRR8chzl2N/YBH8C2t+amihxRtSzYQU6KV6FE3zXru1frbRzZyWmZsnx5unJkfxGwBqy0SaaYBf1SWPUTYraZoRJJW9MpFKpogEPHqrBFEPkJAtr2wn9jzfA9eP90t+XXnqF1NLaF0jwjnrJlWg7/GV45l/Z5+0chz0J62nfI1hEVS/xBiG42BAtrLTa4qt2ZF30e++WA5HWbNxKKgs4eDJd46+ts6PdlXxFHUICXb2XAd/qbsWFis+Ra4x8zAul0X1FMdMKndw2lyKVKFKkUgoxIaxkCYkMdGm0TSJSKBQcnPJm3i73RScK7c01UJKdu9QKMqFTCv2xZSy+5/fAj+uoKmhXtDTVQI0AQl4HZk9NTPJb9yp8WKlEBqZAICBtpEgVpD+QSjBBJAO2lqeLgVCg7/5mnVopKbgIAs7EkEq5LJ3AVRk9zbMlMCxc+/1HWtTmtPyEf995MwrOfwK/rlod8WLm/MUl+PKKA2D/44sB299IHOxPocVw7vcfKI1UyRMrLE46UXsV0RGsQdmsXskOnlQSBB5L3WOwwl0Avbmnuq77OVrbkFgiN5GwOd2YkiZCGXBADPp6bNCG2lOp1urF5nYWBjm6vvMY2xuhYkLS2NjoFDur1mGwGkreGpjEbLhX7aCfr43pPeUxUmjS8xEUFRBY5ZAqldx+Oq6Jfs+gWt8ITPJG382PfIXeUIDMPeRnqwWoNC6CIjN6Ty6lRlYqMf6EBT5ECvJa3NOPQ/MB/8bWjIP6vW1BLj1PDc6dEbX5EJUSy6k6N5Vp2sg8cKZPnxEVqaTTmaSxhCDgSZRSqRnFJvocTWIm8kyD95CUPM0ESqDzrvga5RNVblO7BXp4wTiaO/2U+moZI2u+IhOD1SWP4KXQTZiV33tYSTxIJVIM634ckfoqUVKJFgH4QSSTxhvZU/bBjdr7cczHyqRRKjl9QRhB5892uzem9jdClBGwqsjJhF9WLoeu7E38ZWwQGRkZ+Otf+ze0n5ZnxPT5BwLjDwYUfXd0PLHeh6uXhbCqJXF2ICqR72GFEbfH9dDzr162Yd2wgSbd5Zs02CNPIxnoJ2MLXE1NNabkaTGRbUCO0g+NMvICnGDIR4hVSeu8XAM911MJcBQpUimFmOB300WXQ9TAHEOVQA5HgtKXWGnk+vJy6bdLUEGt79pkHTVOg7pH/4rmn17r9X5FP/8dZerzcKj/xwEVQ/Fofwt7KlVWUj8lkmIykCz+n6YvsEZ9CZaEVgz4PIoAXYwGub5TxvQqBVyg/lgBb/wXr8Q3yM3o4BC1yCntnQzgs2fCN4NO1Ps0PQdlZhGMS/6Kn3/+Marnqqne2a9SiVQQCb6ylkrPF/YNiBVBH52oiXljNAgnIGrYwW96dRoNTn6pHkseLIfOnLNbEpZHPjZnR3LKkSPB+EAZvvadgXd8VL02XEolUhV/ek0Q+77JwDv38h7XlSrp2NjkUyEkAnl5PUklpdyaGG5VjDd+d+fgtuC5eNtBk2dihbZgFib5X8Exwf+goXHojK4ZdyvyYIEy6BxU6xuBSUs3DL5QcviTJDvCAQ2Ole/hnXPm4vS50at0VbJyh6gmncPsqUTMsFUFU6ApmQWNvn8iITwHEOPtSBKSSPLbxHwDKjVn4Vf1ldL8GQlycnKl1naiaFm4cPGAt1dyCiktl0CIIv48WqXSmAw6FzWJGVFV7ftDu1+NZTU8gu74ttxYPEHsE1yOLZrzcVj9Q/2adBOQxFurnxLMjBAEI6+H4onGxq7kt+6YN4/6Kq1Zs6bf+9vqy5GuZSQVfzV6zhnDiTHpWhw8RglvZeTFvUTD7uMxy/8c5nieRLVNiEmpFFYMqxF5K6S1mbbK1ft0+NvfLpKShOOBjY0OLK20JKQtM4z/aF6WwkPGBOieKF7Q+ul6ssHOd5Krrk1fIP3NA/CIeH/StsCRAn6hms5PC2dGuY5kWNRN+hvuC54GO5vew6Ppz44UqZRCTDASc2MyuAfYmNJ53H6qzFB4EjvYuORefkuo5+B/4pGHolTrwRGqlWB/o4uS7tC5a8AyIlp8irhNHL0hXIknPhZEwr1jx44B/ZTCMHICshgHTMLACzaRpyyeX923P5SGY3Gc/y7M9T2NbXLrSzxR29yG05vPxHTrQyiec0Sft3Mvuh6+CUejbt9HyOgNw6xD8dX3EZBKATfMn5wOzTdXosPSFoFSCWi1u1Bt9cAZdl2OEbzcUhb2kIgUWWa60SEb6HhEkoaVAbt+Z0nl1CVoEtaKMFRQdNDW0Cabd9iS38JKJVZrAiYfiHfW9mwNGyeTSjVtbmls3PWzMMgGtpGct7Fgmz8Tr4UOxa+BqYN6nAwD+b6wYFgFmqxDt5k4vOlRrNT8HWfnbB+0UumgtDq8q/oXLuc+jtvxjWaEjaDPmaOFoeJDMDEkFGr19DMjZvG8d3iVSsSP7/zccpyv+ALjlf2rZQSNnHKlV0SoVLIi2yynNTFMxMbK5HYffvg5fvxxeZ/tWrvCKdLnEfnEkHStra0ozpTV48ocqLn4bA0+xpHY92UP1lnim55b2e7GJIYqMpvtVH09fvz4ft9zvTkTNh+dY9kErDu7kt96EkJhpdLatWukNV6fx2inm9I6MRtphvivvwYDs5mul5LFqJuu1xh4QwoIYtd6LirICblaNnIiR++j+4kaG4+jjz4uovuQToKXfvwDa1+/DqavLuj1NifZX8aVig9hZBJHwuezNik8RBOK77rDHKTnUp3V11kI31rTDK5jB4pdG6AEj/UNDrSElQRJRCoVy3WG0pLolYHBPW/E06FjUCNkAAolamuj8+YarUiRSinEhEINHSCsHY6YqskegaomeFdiZZHrPHlY4nsUl7Sc1OP/RAl0yB7FuGkvJTQbXpRMHMNgAk6YBbqJsomD29RE6qkU9lUKm3SH+/D7g19JJ1IzM/DCnQUlTazaMf0uvNpEM6wwweGNT8R9d3Q0VqPx+UshfnQTDMa+q8aiJg3Ow57CjOlzkacl8mQNfmv0D5gUyDgboKpfBqbiawgqvSR5DyvBdkW4stWRPgkPvvwaNm7ZMKjXFgpQksOL6JRKs8bQvnNV0A6/f3CTri8QlHyVCHojQsv4PGwUxsLuTq7JPRoobJRU2mYRpMW7ZMA6TEolhc4M037n4bkVu1SoFCpYNaXY2EqOcXej7Mys3E7j3f5ihmOFWz51s43RpXL25sNgUtMlQovDFxfSMxIoeUps2H2hQSuVcrUCFrLbMImtT1iM+GhCmJS+e38lTD9eB4Uz+lZZUdn1vQskyAMoUuyobcTf1D/iNuUbKBwg+bJDpJvMnPz8iDyViPFyttwm5mKjazUlZBJZg0SKH8V5eIffH20+NnHtb2aqtPLLqYvxQFpaWkLICEIqTWZogMe6RjqfDfR+krmiNUAVWO//Orj5vv/kt56k0pQpU6HXG6TC4bZtfatEPt3JYl//w7gZf4cxQn+uoQBJM521/X5sujILZiYx7ZexKJUIGLmYF1aeRwNGDhTQyjYSkbwPaXKSdK3F16cKfleQWfPNdc04yPYe1FVfg5XJw64bCDjN/y6uVb4PgyL+6+5OyMosvZea2scDvCAiS/ZJrWt3Y/HiJdLlpeWtELSZUjLuyTmNkt9ctSV5vDxJW3RDQz2KzbKHmSF6ZaBZy0Ent8xx5hzp8UL9kMZ/FqRIpRRiAu+k7DSp/MSy8Pcq6IDukQ2OE4UWN49GZKFZ3H3S8c8+G/aQGnrBDmXTqt3UEM1+NQrjJAPvCyqVSlI9hFvgduyInFRidfQ1ZYUGTmoxyBUQcYA0n/FMO5xrP4fPFf/PJdzaN358ZCaUhOQ6fWEp8mEBM3EfqdLXH7wW2tNcL2RIfjv9RTWTfniCC9Tf4B313Shs+ByDgZVX4ZvQfKwKRlfxUMj+OjqOgc83OI+dbWu+gefWNKy+cZq0iN0V57Wfg2MC96CC7zsBKdlRVrZO+l0ZyBw2lRIBOWcPKfTiJ9U1uE/4b4/ryox74Wn1ZbjsC/9uGwwCTUYhXl4fwMtbFD3I7HhhAluHhUwZxpoH3/J1s+odvK+6E4sLie9bYpRVu6JETxdmloaaQZNK4VZDQrw7Za+mFAZufzPqKVmyxRoDkajQ4H/1c/CXNz1wevv28BsKVDW3o5ihaxVFRv/pbKqMMXiLPwAfhvaFlxfh8/XfnkqIpyw5+dajGJx/2UD4RncCLltdhB3tfOKMug1Cp19IvFDBFiL39HtRp4xvO1dVuweTWUoqLd9uGbD9jYAUmdr89PNyd8S/nTfsO0fanUk7Ewl7IVAoFJg7d550+Y8/Vvf5XXIrTFLkuzMj+UIFGFsVZqQHYFCKAxb3hgJzlLVYWvw8rvE/I/0di1KJI0pjyZs0MqWS1RNEPuhY4hS0EQdIZBvUKMwrwEqBKofVlV/2vEHQu5sfXSKQLhdydZ74ffetLj9yGUoYN1q9OPRQ2oGwceMGBAopwXTV2CZ8dcliLCodnMVEvBWsWk5Ehlb2QyJqoyjBCAEsNrRgDrMDmowCKbihSfZV+zMjRSqlEBPcNmrOZvfTfvVocTt/AWb6nsdrnkVIJGxyUpRRufsGa8mSffCtSJ+fLf9kNzVEeZMbe05I/AY8rKYhZt1h4iWS9rfMLLpQy/INbBBHvH58orKTxOgLV+h/xl3KlzFZ6DslLlak1X6NTZfqcd3cCL1kRBHn2R/HMvVVWJwTwNs/rOz35j4rXWS2EAJRCPXZ+tZdqWT10yGQ8Q+uor49mIeLg9fiAe/xUd1PMJfg+2oRWyyEVBqcx47f1QEtE4Ca4SWyclcoZKNGh2dkKpWIUibLTz/jcouAadO6DG+HGmRBSWTdY9kW5KGn2vKy9zbi2ZYiKHPG9koqmbPycf43HK751QhfnAtbIUHEVapP8a76buxrpO/VYDCFrcN8djsmZg2dWbdepAS41dIBkyn61uruUBqoAX96sA0KEv+UQkTtbyS5jSDIxqB2Yxi8ozsJa+beiQbP8C4xfbYmKTWNzH2CoX8FDpdWjDvEi/B46HiwGsOA6hriqWSWlQtpGfFT9/SGv4xhYP32SfjbEuPb0dLSgmX1DLYrJmHyxMG1zXbHnuJqrJ/4NP6R339BKFrYba3IZeic/fNWuhYdP77/gk5mZhaa3ZQkVfraE6ZUChmyYX3vEjz3ztu9+Cr1btZdXb0TygzqxTQ2K3F2C7GAZRg4GLrGzzJpO5OKhxNZfCPGtP2I+SqadBqLp5JKn9YZeBOJwoS0bxUy9HvDmIuieq59x2fia4G2QaoraYhMGEyQqkOJl5ZamzhSidVTVbw6jqmzWToGTRl74vs6DqIhr5M8JWbdwcK9pMvpbb9DqWCTzqS72ESPiXi8ltuiPz5l4+940XslXs96FZliV9DSnx3J9UmnMGLwYssk/C3wD7wgHB2xl0B3BDiDZD7p9CW26rGnsAY3c29gb+PuG6Kj91mATz10Y8pu+1QiIggUtqrOFptJkxKX/LZrCxwZkNrb6aQViTRelUkT1DJVA1daDvH8G1P8ryBk6N94dRJbi8sXqFCkiG9aC4FfCGBGjgImc4QTJ8NIGwKOEXCL8g381tJ/1Zy301aNlhB9P/uTJ4cXIRYPfUwuMDgVhi9ACRsSyRwN7q0ejwvy3sLjwgnwDNLUNuihr8EV6j2FSMnQc83uTYxBdKJhcThRJLewbK1pH1alEvEOCIFKnzUIdLZWkahjUtEkCDnaeiWVSEtI/nmPIfu8J7GlIb4pi3ZfEBkMVZvo0ge/0U3LoovnTH9D56Yp0SDtxwQOf2wq2O5QmmhRwCzYYdJEls71ZyeV1IqusYLTxabAcaqzoC3dA/bA8KoadDw9v5qZbMlctT+QdUz4O8JqjFK6W38gZt7pcqx5bk5iSSWD3gCDihh1J2ZD39bWgnPfbkHjPo9i7z1mxe1xDSoOeUwHsrj4tr48vBf9LD3qPLgCVKkyUCs0USc3O+gaTxuI//omTLqPd/2Kk7mluNt+I9odlDCYP39Bv0qlnTur8N9ZOyVfnUnGxJk1xwq33N6ZaVQlRQIc66PnZpsrFLNSic/dA2cEbsY1luOl1sSB0GZzIFtu/9PmT4nqufadkIlvQgsk4kjZuh6ss3E3UskNDXSqxLU9KoyUVDIo4zcms5wa36mPwSEvWpFdUIIZM2ZJ4yhR7DRpaGFc2bwW4L1SUdDqGV7lahhkr0U6TLeGilAujkFeDB0pIXOp9FvnrsPYQqrurE2RSilSKYXYUB0w4EdhLjZ7YotiN8hJKW5ZIpwo7Kkow8XcF5it3d2YkeM4VATzJC8FA2mBa6RKmEAL7XuvcGmHhFQKb5zC7V2FhUUwRGDUqMuhpEmOTpSkl32BDOYhBZXqZpr6r4KFlPR5FUL8B/9MBZ08PYa+fZ12hWfR9VJC2jx2B+ZXPCV5P/QFRp6oG3x0guhPqRRuf2t10tepDA5usc4G3fBUrEJ6lCaIGiXbOTkPVqkU8tLn9gi9b56vN3yFn9nLcKD/e4xEWOq3Q8GIcAgaNDv4YVUqEYTk6VMDP/wyqdTq9GOp6ir8qLoWY7Wu3ZKACEhrojpgQw46YGkfuHU1Grj8IWSAfg902ZR0Hgyy8+i5msu5hkypFJI3zk5RHVMIRHeo0+hm36RMeR1EArK5Mqi6ikRqTWzR6/twW3AiuxSa4PBuQLN42uphU0ZG+uSpAyhi2qAz6Af0VSJKpUwdfa8Gmx46EPZoeQvOm004f0r809+CwWBnMYsk08UTCr0cSsDFdz3BWamSup3Niqj1jYCQTt9W+PE0fzR+CUyWVJ3xfA/b2uhYns12rSUqVrwn/Z47l5JK27dv61UB17CzHH/L2Sr56hSmyebvSQSv3N6ZaVAmRQJcSytdB7Z62ZiVStPGj8Mn776PXz59v1Oh2R86Aiyme5/D3uWnIq80uoLW+EwdVOY8rBEp0aKu6mqBEwPyuhhqaKOItY8WFQE5iCAvX/q+xgt1dbRToqioWNq3hLss1lRZENLnSW1iDVuW4rjnV+GqDzYjGUDInw1tLI4M3I9TAncgzxQ9qSQYCiCySun17TGOpi3XpkilFKmUQmywy14JsfoJLtDU4D/cczhO1X9L02CRyVNTOpW+90ln78lF+DpEJ3z1jk+l3ysCE/Aevy8qp1846E1NNO1vYVIpUs8hQ/4EbPdnYQ2mY/W2nX3ezscLAEsnq6wBVEJlfnosCn18PSII6ZWvlFsrcgdu7QtD0OfBP+9S6fK/Fzjw03e79KN3g9pNN721drrB789TKbwIaZddjdWDJJVOwA9onPYw/q17M6r7aTj6uTBKQioNTpYsBuj76xF6PymLhBaUqmwoDA1dPHw8UecS8AZ/ED60TZII4UhaRBMJkaHknYYJIkDOMbmaOYZtwzi2GR0+Ytq6uz8JqeR9kv0UVmkuh7rxt7geU3GaBhk83SAacvomVSOFoKeLpXwD0xmZnVCIAji5cutijINWKukyCjrnKafsF5RC/6RSuGjrFVXQqmNTd12l/BgPqZ5GgexnNFzIaaPtRln5A5MOBC/4rsFy9VWYncsNqFQipNN2Xxq2sxPh0A/+XOsPm5y0DdGYE/+o+fb2NpAan750JtysLq5ki8pEx480xcBBG9EglDEJ3qmnYZ0rN2JlN/FU+nSzU4oBXyrM6rRGiAdIsYsU70jbuV5WxxEI1ctp23ZWVqdyeu3aP3a7P9+8RfrdLppQGGEi4FDCr6TjcJaUjDj8pFJlPVWld6hyY1YqpetUUFoqEWzdCadz4Lnh+Fn5ED6/G7999DrGRbhG7z7n7yerlQhUlV91XsfxdL7LTEuHKYEG7ZqMItSLWbCIA6swI8WPm6vxm90ATclsFBfTAtSsWXtIvzds3AD/pOOl8zQ9I09qHyxvdaGuI37td4Npf+OMmVI3BEm6TNfGMM+xHAIGquQ2FdDftSlSKUUqpRAbpnjX4Xh2GSZoY4sMLlXa8FfuJyxSUf+iRCEtQEmljKzeJ+qzDlmMz0KLJLVSbQf1mnm3Yxqu5y9BmX9oJvdwbPa6dWul3xMnRjZhqbRGHNB4Bc7gb0W1te+B2t+0RYrVvp97ekBSiVfoOjfK8URNzU4UK6mCImdsdAoTz5xLYRd0GJ/BQrv5lT5vx/D0Paho9QzY/qbT6aBWq9Ehk0ra0OBIJSbkk9KyWEV0laax/q3YoL4AS2d/MWilElFLEXj7UCqF5PSPsNx6pGGzNwu38Ofj2q1TJUKpN9+oIQVHN3paolSSSSVnO63aeQUOVq+I/Pze202dAt25B1zxTUVyWpugl5UmprzINtL9waOkSoDCLOOQmFDyfBAvhQ6Xkq46nL5Bk0qGzCKpHbRFTMNPm2lbcwp9g1Tsw8lTLqkdI7bKuZ+h5yYrDl+7A/F+GWOg85imYHpE9wkbbhMD7oGUSiT97TXzRTjU8y98ISxGIiGq6XEZOD7uKYzEpPuiBVrYzq5DxeuXwBVHQ3tjJiXB0jl/3Iz+X1lVh6v/SMN34/6Jtyt0USmVCGnNBmXPtji24nSZdOdjnpGSLo8LJ+Lr4n/Qot4AvkoqOx2bAuocjM1MPqVSUCWrXLRsUiiViNKXwOIRpAJTb8EkAyLkx4Xz1Lh6sQou58DfTUKK7qzcATHgHdC/qy9fpd+US+BSpCOUObkzpCO8HiMhOrFYiUSKYPE+2Nv/KC53nhdRumUkMG58Dj9l/RsPHSB0kkqzZ1NSaePG9XAvuQWuAx+Erng2FoyhxN9324a30EBQW1sNhUx45xrVMb/vbp3cdcFQJXRtilRKkUopxIbjhW/wsOop7GmObYBgNXSzYGATaxpsVsqkQWZxr9fnZ5iw3pWNBf4ncdtO6iVQ3U4Jhlzd0JweYU8lj4dOLhMmRK7A4Hi6QGqw9C3fNfLtUqz2NPeaAZVXotz+po1ztOmWbduQL6dEmHMji2LthFKH+ol/ky6elF+HNeUVvd7ssZInMCf0CpbXCdIig1QH+wKZRIhaqbWFStYz2MERLaxAv8ei3GYYKRScEmbGAzMXHLRSiZW/C8SUtjeE5OQ/hVwZG2mo7aCvL2htwNSpkW0SEwo5Ol3PBKSqJ0HARs2x6/10Y9CbUonAJZNKgje+C3R7IyXpSeKymlTiBom6IB2bCjK0Q6JUcvMs7ubPwo38RfB63J2Ee6wwmc0o/eMMLPI/iVpvYpM8RwupVOdR47zA9bgxeFHM7Rh+ho6DymEklRoaGnDGh17s/xYDTDkmovswWrrxMYc6IlIqKeQEKXOC/brCx5WmDA3ae683lU1xpk7yLiSt5vFUS+jSZKUS64UtTuqIX3da8XOFBW2uACor6Vogkk0+IZWIV3+BdwcONDV0qkvjgbDfHPHQCxe3Tjn9ctx15JTOc2j+/IV9+iqlyYp6ISM6BcxQIajJQptogp9VJ4VSSc07OpXmZB0XCyngDwTxwN5ePHyYBh77wCSLd+XzeOQQBkdP0UitXtFiTpEZL116NLwXb4Brv/90erwF8hfBetp3cB78PyQS5h5+cfEhldQ++r21uvlupNKcTrPu7jh4clbSkEpEqfTeYXZ8p7oeh6ipSjAWKDKoQpWkApJugzq5FfDPjBSplEJM0MPTQ/0QLcLtaEYmcabBwSCPLC2t6ulzqKlab5iXp4F92+/YsmoZGK8FhkCLlOxUmhWbn0S02HXjNGFC5AsLYhJM0OLoe6Gp5am019rhgFGOFe0Tanq9XhFfD5K6qnJp0RoQFVCao48szjr4Omz1ZAAKJZYu690T6KaDJ+L6qUHYNvwgqZQGWmgQyfTO6lpszj0Z/F43YjDwquhn2MhEt5FXKOn5o2V5eAdpoO2CASvdBWj2957YZFFSqbiXS650mUgxTdWKtEAbeEs9pk0bflJJUOpQYRXgVGZDrZC9Vex0c1HnUUkJcenpvUfVeiArOQLxNd5dtpVWvK0hjSTtHiwMmQXSORtkVGgcAqNud4COOyIfkIITBm3UrVSC5Snha3HGdzM+GuFyOeEIcVjNT0aZYc+YU3uCLCWVVBg+0+F1VQ0wnvcC6hbfDFFu4xwIebl0bjK5qiPyVOK0dCw1x9I+EQU4eb1EvMEi8X+JBq2trRiTQQlXlyY3rmoJnYnOhyomJKkoBwsSiFDT3IppTDVm52iwc2dlxKQSaX8rNDJYnvsAXuD/iel5xrgrlQoKCtBx5nK0XbQdTAZVTzG+DjBeazez7jU9WgHJOVdioOs4ddFsJCPUCy/AiY0X4NqvvXFrnRoMNCF6DrQ7AzG1vhEEGbVknE3gcQ5s3F6x4SdcPE+F/WYUQhGlIj2coiclkO4SGFDrZnHNMgH3b0hc6xuBSUsfn9Xo0W6JD6lkCNCibF1HoJNo627WTVIlyTzOtazHwTlu6fVXtLux0+IZVgUr+Q5PTgtiItuANF10heDuYDNpgbyUaQFnypEKb4FAcpiRDxdSpFIKMcEAWVUhK1uiRXYWTSIwhhJX9Sirqvp/9s4DOo7q/OJ3ZnvX7qrLKu7dxsbdGFNMh0AIhJDQOyGUBEIP5E9oCRBCCB0CoffeOxhw773JVq8rbe8z8z9v3qws2yo7u7MrCel3js6upO075b377nc/JNz7tqLuBx03njIXrvfuxeZv3gL7xY34MPdfeFTzECaWZ6v8bd/8IjlZMTfkLsZK3WVY4P+w29vEfHRloD0sdOQ3dQdZxSCYVHFFMxBIsOIWvgw7hZJeu/B0+brUGjzimoeFkQfxqmccWvyRblvz9hbSnYBM+N1hYJn2UIQnnoV0ICIkIcbKc0OodIZOolJ6J9oqdgzm3r8Vy1q6HizH1ZILjRmAJz1BwA31f8Ba69WoCG7sF6JSXGPF6If9eMl6dYeAM9VCB7vVrrBYCtHdBC0E+r2rY8pOENd7jbg1dj6eaqMTmHSx5g/HmMjzOCL6IBrbMp9JFAz6UYA2GKJ00JuuqETQSdb0dv/A6Xq4vt6L0/67Aot3Kd+lqie8Xi/4gBu/1G3B+xfPTvlxYizdvrXSokdfsK3OBbWtAGpbcoISQdBRJ6/DwHQZqJyAtCAnjo0dRbeI5cu5yOz3pDLQ12VVxRAI+BR3Kg2z0uNU3KhsUHeRw46tbg0WV8Xha0/fobCjNYC5/Bp8rLsZk748vSNgvKdS985OpaYAXWRk+BiYiHJjz4SLUzVsKq59dxPe3OQWcyz1G56D/bmZCC/+p+iuNRgMYhngzp07Ou67e/duTBhGj3NfuZPfVrPJwaU5GM82IVq/tV+Uv53N34ZJ4afx0fZYSiHdBINWLXZcIwS8PQtlcV6AKUa3tagxvXkBLwjY1uiFqn4FmIgHrYEIvtvlwtI9mRXrrFqVGIPxje46BFzUUZ0udim/sa4t1NGUpHNY9/r1a2Be/BfY3zwR+btexZzyRAmcsg1K5FBVRUvUSrTUsb/goMkpP1aseA7+qz0LL3KLYMwvE0uTa2uV+WwHKkOi0hApYYW/I9cnFYoK6QHIykYUzwhI0NqwR7wM8FrozN2fePLz80Vr8k8XGOGooQF6lUIhZo2XXzedrqhkNJq6bEPeHQaVgFzGC1O8+xOSq5WuEHphFgc1PdFcsBALI//E+VXHK2qzX1YbxaKGK3Ct+raUH+Pg+ceiqbYKAsPi/Y37doHT1P0E23tnYkLr+0mLSnQwwqC21YOqtmBa22FCVBJkikq5OXQwaWTjCIfSK38LBOhJstt8AR39u5HJbMlpJmBCraKrhwzIdrfz/aL8zWAwwjh2Ppa1adHopYLFSC3dD3fXt3bZ+W3/U6+NU2bFMEFlyIgXuaPwaVCZzngataqjHIYEwJNV9UyirV+KZfo/4E3z/eLvVmv6jRKuK90oDqbH+ZdgoHDvlztQ1R7Cn95N3ZqfCsR9My6XxQJLFdQNXbc/T4ZhNnqeMbZuQV+hce/AHepncYYu+WYgvF7KjjGp0NbW/b5JBCWSXaZn4mL5ssnctSNRKVQGW0cXNeWdSk0YZpIcgpaejlnyYVgWF64Yj0OfC6JZav+eDhvqfTiE3SBeb9RTR3dBQSHMZktSohIxQraH6HmeDdIJsRIk8ua4nBJ8v8uFrU30O/quxQgVF4Zj52vQCmEcdND0A3KVqnbvRKmBnj9C1uyMOVMhId70dfkbEXj8ER5+GBEKBFJ2KhHXTFCQsg1DPTuGW/0RFDNUONbZ5Ze+JSAh+L/67wrwr58Bxzu/hLbyU5galuJK1duYz6xHJlGrVRjJ1ItNRAKe9EUdkiOZL4npAV63T8ZlR1j3urWIFdEFLk3tTzhqLDUTfLlNuX0vldI3ux4wSebSklKZcRyd4HIn4Pv8s/EDPxm55bRT+GAvgRsSlYaQjyDAIpW/WS2pDfpNUlceMjALBzMzUdnqM2Bu+GGc2nhhr7c9+ujjsbptr+BSyeVjbFl2g7oJROGXYz8Ps3QwZRO6DxpsaqErhOHc8b0+ttGaiyqhEB6VXewEpBR1yz9B/dOX45rZ3ecc9cbChYcjuP5zsgGiadW7YBtosDkhUL8Z2trFMGvjSa9cksEIqYP+3q3D/f97HpFA6itFOkYSlVTyRKVxJXtXJuPh9PYDvyQqkRDyLtE7sJsvQCOf+Y6GSsO20+yMPW4BenNOL4JNdiACrXXO6fghVIBdkp2btJmtEwqwtZXvURw2qqlQY4spmy/gi1J3YSLjSQnyzXQ1V2VxoqEhs50DuTAd3Ps4jWJOpTFGn5gpZ4/tK0T3ZyIxZTPtksXlasWiESocHXwThnXPpPw4rFRG3ZdOpWKuGueov8DxBipCJMN2H91vSsZM7tGpRErj8my09C0iaGBNQtRIB9aSj0+4mfg8PElxUamluQnFOipqaHNK+rUYsaHegwUq+n1uCtFzZ7KhyeR4TRbtmmP0ePbWknVQ2qm0SL0aL2ruwjHxr8XfS6adgJ18MYxCCLE1L3WEdXfOVarcU4VxdbfjyMh9cBSUoz8S8TTiIsNHWHZ5bp87lfwkMFCCD/tTdioRgtAlNfZq8oZQJIlK1tLUF7SIkFVuN2IlTwUI3a6PkdO8BNdq3sQ8fu94NlMQ8Ud8HYH0RSWXx40cRsrnNO47ru8c1h0rmSdeV7dswGHD1Ljq0OE4aybtltYXkDDtUhuVPni9o6PhSqoU2+jxxFxAI1YGe1j3kKg0hHziYWhYutqjk2Et74wuZ6/N2tOSmayORk8QDXBid6z3VUTL5CPwzbibOn5vrm+k9c9ZFpXkdpXgtdTllM93r/wTiy3BF++9ZrvcYURk22KE9qxTzJVAapjJaqjcvKiuPqcpOTwu5N/Gg7gPzFc3dXTQiLXTFrM1Qr6s8jchFsYjmofwqvZOcHX7BgvKYUusAIu5SfBp5e0PgnpvPXc8nN5k4SzLd2j6yzCU27rOMfHYp+Lw6IP4Q+BiDDTWrKcDrkq+UCx9y2SXlGQhE5S3C57GV9profFWiyt3y4ddgFuaTsDrm+I9ikp+UzmeWxvFmjZl861KhAbMZrZglFW5TLRLhDfwpvav+MWovZOnTFFqpBMGV2uLKI4q0eEvJJU46PiBE1Af77SSrGTr894g7hy71LZ+bUvq25B72CKc+VYQz29QNptPDnkMnfz6tckvDoUc4/Fy/HAsjo/vMVOJdH7Ly6HivRtm0QWQSWx55biq+kj8aWWh4m7BaHsdtCwv5suYnMqLSq6KI1Dw23tR6U5fYGyv345hTCt4Ro0f6Sk/qc5vnXOVWiL0mBJsV04gb2ykjzVcqMYhqk0oVVEBYpjdhC8tp4rXDeufwcyDZxzgVNq9pxKMvRS7hBKUOfpf5zdCtTeO0diNWflRBJIItc4kBiGI70ufwhPsfWDBd5tbmAwhyakUD/d8bvC01om5YHGBQcEoGkSdKoeOcuITnoa2a2sWQxuix/p4muJGMggMnQOoA+kvsPhbazsqQZxF+2bWdg7r5k0FiNtHgYEAu2slzp5Zil9Mys6CfbedqK1U+nCp8hCOpXeOGq9pwZHsKgwj9icMiUpDotIQshHC7g4rZ6K7h1xYtRbzwv/C5PDTqPFmqPzNR8uJjKres4GOPXgMVgljsIkvR3NAgFZqiZzt8jc5eUoEkxSEmc9R0aYrYhwndgQLSKv/PTHVKeBm/mn81f5xRzlVupD8gLd/bcDmP9jg9CS/atwVi444Ei+ui8Av6OHwbIJuBy13Y3x0slsb1MhyKhG8Ap3YRwOp52L823sEzo7djD1mam9PGlaN7WEnvtnDIZJm9zcH40c+64VW0/W267DQASuvyt62rRRqyam0PWjB+PET0B8gK98jNC6MZBuAiE/sTnf+y2ux2HxIR2hrd7hypuHCz9R4qVrZDJOzNV/hNd3fcJxlm2KPWcE0Yga7HWOcbMZFJYuU1dfe5k6781uCmNQNsUTIbj5ROgQjURSjFYZ1byAnwyHQnSEZNVbJ5eaKpX6c2MiMxlejrseWkpPRV5Rq6GKKkNN9k4794Urm4ub4xXiNO7zHQGIiONlVdFtlTel3WeyNYTkGFFZ+Cvd3zynuVGpva8UbVTZs0B2McUXKl/FdmfMN1o16FJMjK9N6HCLaH6am5aCRgoOxtZJO3kaMSH4hjpTANUq5SpqwMscDkq+VEJVyeSoQaHP3jj8M036DNsGMnGgDFhZSQXDr1i3iYhthZ30rGLUWLAQUWVMPDc4kRosdnBRqjVAfi0pxN8pavsFh6vXghb3juFQISU4lIdbz2CvUQrM666MmDB9JXUapcugIB3YIJdjFF4HhoxjRTNz3AKfKfAOVmJo6KplQ+vlNYwpt+D4wEm9u5VFauq/Dbv+w7ljJfPHvmrof0dcQ0ac8l46F13jNYoB6OpzS/BCe0T6A86x04bOmZkhUGmIIWez2a3BB9Dpc6T0XthStp2RHroua4YMRTW5lOyAlGBNei5vVL+EoE+0Q0hPFNgOMXBCnRW/H5E8monyMMpkkydA5PFuuqKSxUxtpjrr7nJx/4lyMi/wPb7rH9f6AXAxXTovhT3N18EuDHiVEpamjCjDeKaAxkF5Zx1FHHYPKFd/i0cAR4u+mpfeKzjl1gA7qqn2M6G5IJpeqw5bP04EcF0zd1s0zdKXaqJM3EdvTFsTReBjn2J5GWzi9k5tJ6qSokrKT9sdpk0o0NPqM5ZhlCnOA5qNta+UwYUL29s2eMBoNCPF05Y+PhdAk5ioJZAlc/FthYQ9OJZ0TpVe/ivrxpyn2ekhnJAdLhWCthWYXKEFuAT3GOENVGReVGKkbnjeqTOkbgVXrehXe+xsn8l/jJ/1V+J32a4TD2QkYD4VCCAYDsEgVvFwaXSIDvAbGsfOgKhyraMOHZInGOVRo6OQ3R0a5irWj7bapx/I3kj3lUNGSV5s9O+HKZrNJzAEJ+LovdZcLOQ9s2NOMXz9Xg+hRD6IiA04ZKxNBAeOGnk/PYaVTs7igkE7u+fKFqKyslO1UcjgcaPJRZ4I+qoyo1Nragng8DpXBgmJQUclasFfoOmx8KV4XjhKv27a/grKycvFzX716lfi30/N34QHNozjSsDNr7ni52Aw6eECPB2xcWVFTLmyY7pdeyXmfTvnbo9wvcXb0Rqzw93y+DDTTba0urBczWNMh16zDxEJrh1tJLUQ6uslmGk5qRKDi0lvAFLGV4851+TjvjbaOzm8J9g/rjpbMFa9r637CjhY/luxpgzsY6zNRKaS2iAaCGnU5tOr0ZBC11AFutI3ObRJB4IOVIVFpCNl4OA2+5qfjveBk2Gyp57N0tHr2ZOYkNVHYjkvUH2GOUfJJ98LMYWSdXA/1/AtROiIJAUYhOk+eRo6UVx6mc9IVAouGBx/rOljbH6GDKIO69wGLoN07kYj4lVmR2rVrO0rUdLLI2MrSeixyoiq2aPHXh15DQJUDla8WhvX/hTFMJ4w1HkEctCXT8jVhm/bEqRDESWJAKvCuaoSrNyA3kf6XJHrphMZqdGl3f0sEcHcXnp9vVuND9k/4Tn0lYpGB1V49P0o7amyt9/cbpxIpfwtxqg5RKdq4GVt05+M1ywO9OpVyrWboEYFdpZxgQPZzh+DeJ7NOCfIL6T5boPJ3tM7OFO1ueszxMWbFnEq8NJDWSm2oBwLXGD4SL2+d5sHmzRuz8pxEKCFYdPSYxKlTn+QUagI4hl2Bw6w1ijZ8SJaqunqUMnSCXzKGlhwlg1WnghlBlGl9ojOmu9dOygSdRno+FfSpT2rl8ML0VfDfbIXBTwV2JSBumYRomZ+vrGsyQUhFFzl0QprbAc+JDTkIkWGHYNeunbIjA4hT6fOdUTwWPwnfRsaJjR/SJSG0l46egCLQ45fGubf83qhVoXr4mYgKKpjbNuK4ORM7cpWIkHt4gRe/Uv2AcZb+20DDpFXBLSRy0vpGDEjgaqVjvfYoHWul41SymJx489U34W3pOWPos/AkTAg+iRuq5itSer+QlMBxVFRKIGRBVNoVp65KjUWZc2silLq09MBx/T5h3YlcJddW/Ovj5bjqrY3Y3JT5brL7Q8RcIiq92liKE6L34G3b+Wk/Jmcb3nHOIwyVvw0xhEx8EXpS4cOBtFaTz9F+g7vVT8HYlplBszNOTz6MNrkQzZNnjxcvNfYiILf3TB6lIAMd4q4Ry2lkrLoRLLmlWB8uwA/cBGzc03WddCBOB04mTe9CS2MAiAv0sBD1KdOhoal2txhkTR7Xlpd65wwCOaEfddTRCMUFvNRAPyvjyodgj0lOpfZIUqVvnQcj7piUNSWtgKXCp8OewpbSuzHHIM/JoZeyOIj9PZSGI4G4AUyg99ebuhZ6c525mKRtRIXWDSENAS3bkJr352KL8Gr8MKzb3dpvRCWyv4Z56RQaCyHuroGBiUIjrQL25JYbYQphq/58fGf9i2KusRyjBnbJ0WXMUy7wlTfSldkiC7GzZ9ap1NAsNRXIGaGYU4kx0v1cK0RFN9dAoMa3d2j298XJLYooEdJNsOkloVSTulOpNF6JJ7QP4gbHt30iKsXrN0DHcCDrKRpH8vuCWfBho/4i/KC7Gjpj924lIsA18TasjZbBbc3O8citottxOKRcNlhzczOsNhuc4+egPZYZl0xUKrlJnJ9SJcIJ8JzwPwRmX48GpkjMliLjgWTyEztnKr2/0Yu/x8/E9/xkeEPpB+InhPbJFflgGUFcmBQM+5ZELjxoAq6O/QGn6R5H6ZSFHblK1VW7McZEv8/84am3Ns805HP2MvR7NJIFzD5wHybYVUvPQa0sXRTMy0vdOTTGrkJ492qE3D03zJgS3oQt9/8WTlYZ4e/QkU5sEipQI+x1SB0xMfMh7RF9HmqFXHi59CMQ3l22GYGKQ6EtGtOlqNQ5rFswOOA79E64f/kmGAMdn3o7Ba5nC5LtSkR0jY0K6EVWeY11uoKz0fLqmK8RalsBWlqaRbF4sDIkKg0hm1jjZvySXYxp7I60nEqHazfht+pvYA5lpgVjTpgKDbYkV7xnlO1d8ZhUnplVu64gVtEXXngNr7zyljhRlYMzNxdHV52Ps2K3oi7W9STgn6qH8LTmPpQbez8hGnVqBKRg26hfmS4fcT8V95phh0qdfj4IKYEj/N97O9BuHgs2RgdlRLSqa/UnPchM2KZdYcktFEn9/dp0AuwGBhq9vO9Pr2HxvOYerNZdguEkmydFSB6TmuE78g+6wmSxwR+lAkZIgZay2aLWHcYz3PH4s/+30JsdSbWPzgYGgxFByalESjATuV41AbXolOtp5d+RTwNxzUwYoYgybqVoNAqngX6/lgLlRPGwjnZ2KXIYM+5U2qKdhNfjC7G+Xa+YU0llzkNYUMOrz0O9t/+6ATrTuTQgl2tCiz/zr5uETxNs0jhb0HRdRpsMar0kJLCxtB2YqRCopQtV9SEtwMoI0dbZwIOKK7k5ZtGR1F2m0hfG43EKfy/+GT4B2cAv0HOLEFXOcdfU1Ignf5mDxtO3Yuk7DyIT8Fq6H5vTmJCT/M5jHl+G0z9nUD3+MlTupmVwZDKr0+lkLeARx5Oal1zywfTDwxNC+3QzFSBVjgqiwuxzm2klNhxzyoV47PyjMWPG3g5wjTtWwawREOOBY+fOQX8mqKIxDblmbUceVJ8Qpvtka0SVtqhUrm3HhdM0GKXteTxUWblTbAozKskFy94Y4TTikrkVaJ592wHHzEyytvi3OCTyb9zfNDPtxaxJ625D9exXxM+vpOTAbm6dw7oJ4cnnIVY8B2YDnV94w9l3vCVK03KK6fiowKJXTFTKidTBWjZ+HwfXYGRIVBpCNjmNP+FB7WP4vX0p9PrUd8qAQO/LRDNjgzSzdLKmsSVXCqLXqPDY6VNw94njxWDMbHL44Udi3jwa8CsHUsLFBehgps7VRdaCwGM+uxGLVGuQm8TYy6hVww/63sPB9LMbRAeNQAfBrSplhLp58xaInaEC5fPx+7bTUKsqxf3Ov2Fy4DFEfe1JO5VIvgKhSWqT7pTyaFJBr5LCPw1787GSgdRzW5iQGLKt4lMf4Hb+rkzWrsNWWZaFT3qK8AASlUgANiHeVofx41Nv56s0ZBv0S+VaBxVooQ1Sp2C1V0BBQWGPJZj5RXsdey0NtLQvXVyNNTBr6WTGnK+cqORi6MJBkUWVcafSl7qjcH38UnxRZ1DMqeR3HIRRzffj19Hb4euDgaxcSPvq55xXdgjlpKx1c6M/a06lx70LcFX0D2iy0ZXmVNDoqSBlYqN94lRa6TLB8Xcv/lGdfOmbCKtCkKWv3a6JdetUIgIcK5UZ26QcpkzjBy2PYZXIQ+m0cj/MIogLEmrJ0ac0rJGej6yq1EWlXa0BBKIcGrxhOIyajtI3uc5uIiqR2KLyaCWOtNYhqoBzsUEaPxTlWsEbnBBs5V06feYPd0CtYsUQ4+IcnRgEv3s5LXNtjBoBVfYC+VPBp3GgRbCC0erhdiuz4JhOppIrSMdcubmp5wfmBzbi6V8YsHBYz+eFX2q+xeMn6nFQuTL7CNkeLp5XjrKZv0Tbb79F+6nvIF6QXle5ZCi0S+NTrSntRjzWGHV3+eOqLhfD9w/r7rifnlYGePrAqUQ6v5ER0topr+BL7XUYbkjf9clZS8WFCLJAOGYEdWxVVytXojzQGBKVhpANJ3UO8MXTOwmGJPFCHVe+1TOplc/R0oOWITf5kqsZZTk4aqxyIbeZhpTNsVH6+TW0HSjOMVG/OIgiGM29Cx5qlukQ++KR9L+XurpalNroCwgYlMl5IULmggUL4d/0NZbwE3Bo4B4cdvQZUH16N8LV62U7lb5Zsh5t066GftrvUhbOjNLEws0bZAfWR6TuVBBSF5UYQcDyUAlWBQth7hT8vj8BLXWd7G4YOKJSXrQGB0fXAHUbMGFC/xGViFOpzhVAXVADu8UES0QSldqiKCoq6vG+Wr0JAamdcVuzMkLNd+u3ipdRngF08sTNnrA6hyFCnD4wIRTJrEjgj9BjNh8JpuWC7YwtJwd8mIoyXunx+zN1Le34TjUH5a7/oHDlufiWPwgbatuylqm0TajAT4bDgJyytEUlI0O2F+XP773xUbsThnOfRlOR/IUavYWWLtliLaIjqSvI30kws3g7g1Q+nWGCkoNYzYcUdSoNM9IJNZvk4ptczE7qyrQyIaxbtyalx9ha24i71M/ggpx1Ype0yspdKYlKTmcuii0Mvsr5G56O34zx+SbFMpXqc+bCdcE6eI99otvbMqE25H5yLrb+3gCzFnBtoxlRbarshL2nQ9P8ezBt8WF4cqkXHk/fiUoqqZlDawjiwk06mUp1MSrU6noYG9d5QjiyJIBLD9ZieKny+8jT27W4Za0Fa12ZD2nPs0lh6wZLt8e2ZMmVuqnGpfLW/dk/rJugrfwMv3H9B/lo75PyN5J3lGdixM6dI5gG5OTQ8XBaqHTwaOiC+dgiet6rrh5yKg0xRNIkyoT8fHp1uWrJImyNKd/q2ROMItdInQI6Z/IthQci1xWvxSrdpVjY/NwB/2Mi1MESigkwWJJrFxyUJru8AqIS6fzmV+VgC18Kv0XeALAnFi06Bpy3BdrWHeDB4p31Ddi9m3boqKhIzqlEVleIdX5dE489pWcgOoKW1cklFAxAz0gCpln+RDjC0P2I5VM/yebkleCIR2ow477tMJm6HygHJCE3IrnbBgLzml7EW9b7cFH85X4mKhlw4fthHP1RESKjT8ZkMx3sVjV5UVREJ1I94ZO6Dnrbus5Ck8vWdh63xs7HA63zDii/SAet0YYZeEm0zXMGh7jymCnUYReMCIOP+JUTlWx7RSVfHwxk5VLbQgf7fDQMXZCuBq+sVGYbScapNIWvxCeXzcGvp/W+DXeH1iCJSojA58++U8nN6aDJKUS+M7lzXmcSwdsOAyO6Sbotfxv7NtbrLsSYYGpCiVxCLD2ua6RuUUrQ0tyEIi0VqXT29JpodMch06egJmzGlj3N+Pe/Uyuxi+z+Cb9Tf4ULws8CDJtSSHciU6k5QB0uDB/rGB+lQ+J4+DUzEX9+bxNag7QxSld8VBlBY+0usbHK+QdpMdxBx6hL2cn9viPrceMLYGlYhXh7fZ86lZ42XoxJ4afxz81OUSQkDuyU0dLjlIHt/rxQVV0Nq1S6WTh233DtdFld68ajP+zBx5ub0eTLfIlzIdeAd7S34d2S59MSlQLhEPJA9x2VqXthpnNYN8G46t+Y7Xob89hNfeIarqragzJpkTtqyMNBZQqISqSUteIyXBn9A+pNVEQbzGHdQ6LSELLJ4enkKRRNzzpsltqvm2LKr8LWN9RCx9CDlqNEXke1gYaW5eFkfDB2Ic4lOri1R1WwWJKr2b4pdhEOizyApf70y9V27dqBp/cU4mTvjagacxGU4sgjaYve2u9eFS/f39CAYCQqDjC6Cg3sCmLNFTvAsSpUN7nQmmJuSbBT9pTJKn8irFbT/UDDpT75Im6phCPAZOo+CyUg0OeKh/owE0EmbNt28XJbK48JEyahv0C6v2lyyxAsmIyV1W4USCt3e1oCvTqVCL4YPf2qQ8oE4tcEVHiROwqveVIvW+oShhHbIBNU5syKSo/6r8Jm/QWYZPYqVv5GHuepgjfwhvaviHszL86kS1NrK45gV+Ng1Q6MdmihQRx1bl/GJ50uFz1XHJ7XBm3lJ0A33USTwWSiK/9ig4Zwdo81JH/nLvOruE39PKbnyx/i8pKo5DSy3U68iKvLoQ6L7hu9MfXsKTlEWOqqMCD9HKAEYVe1OH7gBAYWZ2acSoJzHHYf/zZOfT2EDz98T1xokktuyxLx0lMwb2/GTQqiEil/i3JAuxRjx0qibbpOJUZrRE1Yi293umDQdl/2zLAsnorRxaurZ2vhsOjFz75ZPUyRrmKZJiH096VTyRvhxFJQjy+YVp4SQaWnxymjKt7t8bW+cp142cYZYHH2fl6Xw/sbGvfpEphpTHotprE7MVVb221eXDJ4WurFUHrS0TAnv/uA8c5h3YREFzgiKvVF+RsRe0qt9JygspXAZlCm5NQ/+lR8wM9Dq5YuxAyJSkMMIYNcSCeUUHpZSIxB6sqjoJ07wW5XEHPCD+O41j9Cl6RDZ6ASEOhg08ofeKJvb6ODJp+uAJYeyqI608TkY49QhDYFvhYygPT8+AqOjq/EyZOVOyEPG1Yq5uuEdq0Uf/dFeZRe/aoYGCgnuJNYpy3j5uPx7zfhlffeTOm1+H3uAyZTciiROlBooqmvmpJuE4lBUU9OpUbOhj18AfxpCsLZggv7oW7ZIF5f71InnZeVDYxGA/TDp0OY9Tu8t7ERsYKDsCtkRZWbT8qpFA7TyaExrEwpYrvUyShHyixQklyTtkNUSpR7ZAIT6EHH4w8rKirNNtVhJrsdUc/ebIf+SqytGv/V3o+Xc/+L28dXYYPuQpzILkGzXzkxoSen0m/0X8P2ycVgw2lMHLV7j0HRQHYnoI1t7fil+idcoP4U4yvku622eun+M2zCwV06lYiAT/7uUFHRTWdVZrW7N+rUFfiEm4mtIWUcfIREc4EW5KAwJ3NBwaRj59HHHAeBYfGf//xL1n09oRimxOikVDfqcPHzT7iShw+Xn6lEaI7Sc+7bS9YjHcg5l4jsGnshPtHegFf098LMdX8eP3xULj5VHYZ2wYyRDhbvtlVgQuRZ7M47Av2d8O6lePyQJvzvjLxuHXzZIFE2Rdynubnp7XtqKQPTpOK6LesONdNSy4a4ciXlCQ4dtff1G5Lozpwu1hwa7aFnYvC6Ul9gCbpoDmQTZ8Ww0p5EpX3DuqOSqHSMcTtOnaKsQJe0qGSjsgdvVk5EL7ZS13lAKlEeCuoeYggZqGJUTOLTzO2IG6jYo1cpP8GtbfWhEU5sCe/b2vXnSJSlJU35OHDlISqVOZEWolZrct9XhT6CwKZvEVNgMkCcSoRRo5R3i4ld4AQeDi9dteSDnqRL3zrnKhk4Hz7V3Yg73DeIXbzk4guEsYSbgOXcGKjV8if0jIZ+fyohdTvw1h9eR+NfyvDGVTN77CD4V8/JOCz6IL6PT8FAoH7LYrACh9qYDfrCcT2GX/dFptJ5xVV4T3srZja8hCVT/o6zlk1Ag19Iyqm0LpCH59ZG0RRU5j054o2Yw27GaLPyNvrfxN/F29rbcNaYcOY6wHExGEBfO1lFVar7G+kW6I7RFcmot/9niXEBKu6080bkFpaKE4BZ/Dq0BjIrKhH3jYYF1KDlO9vTqQxSafHPHcPxu7eD8IWyW+ZQv2OluIreypkwbOx02fevsc7Ay/HDsSVW0KVTyev1QAUOJkYShS3ZyWBszFuIc9ZNx6s70u9YlEAbo19yg+BEoQLttbvjrXX1aJ39exx7+Gx8+cGrsoTpHXsqMY6tEcNwNSMWijmNkUgEGo0maVdygkT+TlOEiuQhd+odVxPHKfJaigrzMZ6twWxsgCCVVHXXDOaQsaV4iTtS/P2KUU2IQIvhRYXo76ypasRB5lbMqjD1afnbs86X8F/tgyiM1aQV0k3QmqhAa1Tx8Pu7XiRX+ek24hKUOR91Zk6n4G+SZ5pprFY74iRzkTgf09j2Ix4pRyxqRGlp95m1+4d1x/PpuNMWbcCCiux28SXdccmxo7yAfo+VUeXE+WJ9FIexa3AMfgKrNw8FdQ8xhBwSwdqCLr2D7DLtXMwPP4Tz/H+A0jS56QlCm0b48UBBLQ1i8qXym85EIiGEBC3ccW3S5W/n29bhj8GHMZOntt90aK/bAe+NFlwQeVxs5askJFeJsPv1uzAuvgct79yVdEh3AlL+5vEHxS5LBDaFjIW2uB5nxm7F6YEbUrKwR00lWFHHodGX+ufDB1woYN2wqWM9ZgxoWepm8g+ALliE6O4fxcsffUX9qvQt0f2tQBvGVLYSOn81zn95LRqK54v/Ky7u3SHxaXg6LvxMjfVeZVZAT1Atw6vaO3GGTfmMlxLGhensToy1cxlzKjGxvR3OPL6QYk4lsj+4w3S7L9X0/7LPRHcjj2CCdRJtVz89shJlRj7jTiWLbu/xK8KkJ168bPwNPh/5Z9RmOafbt2e1eLktZBcFRblUlZyMm+MX49vI2C4dGURIcBqk1W6wsOVkxwl97GgbWj+4D4HttBRMCXY1tuP1GidacqZl1CkRjHK4k/8XPjlkI86cwOCxxx5O+r4V3hXiZaNhjJh39cILNDtyzJhxshdxiBBFSriapG1Sk2bpcaIUeNJwWoblVueJwb09cdKkQjwfP1osHZpnbcZkphJlDuo278+ozdRV41BH+rT8bXjzZziCXQFtzJ92+ZtaT48PZlWsW1HJytFxtV+jvCORlLzddswYnDm9BFOKlXdCHQDDICBl4cY8qYtKU0aPwcvbtPhgdSNKe3Aq7R/WTfZf4lYksOHMN5/oTG1tjegsHJ5P50sr3OmH9CcwhhvxnPY+/NP0vOigI+cNn6//jzUywZCoNIRs/ho9G9eELkalPr1JntnmQB3y4GaUV6xzvRtws/olnGTciJ87rJXaOElr+v2Fm02OozE+8hzO2XNC0uVvFajBzQt0OMiSXoA6sRMbVHFxohIlQbmssoPWGTNmIicnB25XC7a8cT+iTbtSEJXs4MIBeKV2zUwKJR/+sORu4lITaq5vPxGn2V/Gs96ZSBVeyi0J8D0PsvVqesgPRPt/YDHB4aITiu9qGbGEoj9BHGGhGJ3om8m+BwGBZmoLLyrq3VpdkzdLLNlcrVA5i52lMyVGcoAqSX4hXY10BHZnLFOJdKokBGMC4jxZVVVuZdgfoA6oCnX/D6i3RqmbKggjjGMWgmxiw6wsdqz6KuNOJdKRihAWNNDLKCPuCr+1DKax89HWQ3BxJtC5qTu2MpLa9mOVOnmy+q47JJHPyZnQAAx26LXZaQVPJmiEuBQ6ny6xWAyfrGvGGf/djdLjbkEmOXpcPn7k6XjxohkGURhyuZIbXwz30RL3nPGLsGXL5o7yuT//+aaUXovD4UCTj57/9NH0JrYNDVRgH1NAJ6h+fe+LCZOLLDDYi7FVoC6r89Sfotwur2tsX6A1U9c/6ZzldvfRcZSLgY1S8ccVEtJ2KsXsY3BZ9Brc4DkVfn/X+5U4riZYU29c0JvI+KfDR2YtUyvI0G0t6k99fO+2jMXvXm3FXV+1i1EUPbFPWDfDgtfT8cmGXbuzGk5PQroJzeoibOLLETZ3L4bJhbPSx1LHfBhVbB/UHeCGRKUhZLOYm4B3mcMhmNJbJci1UTFJ6GVlJxWGRXbgEvVHWGSUHwo50NDklIjhpMQ9KwT3XXnzhKhTKx4OJi0qMdLqjRbpuVlI29/hBfSxGhnlSwTIKuURRywSryfspnIzd0j5mxAOwC1IlnXJJSAHLhpDuGYTmHYqKMhFxarAsCrE0jm/JibkvYhKJxvW4gPmWpzPvI/+Dh8NYnh0q3j9600tmDx5KvoTpPyNCCCEU1Q/YYvufNxeRJ1VhYW9l79Z9GroEIUQTz+8LM7xcIAKizqr8u2pcwvoBKhAHUBdXWacSiGf5NCJ0W1Yqe5v4mNLHS05vzKh6Jkk30+D6dUaPaAxojJEj6G+TZ9lbBBOcmqIA8eipRMbPwxpB8cezFbiKHYl2Gh2J6AV8d3iZTOf2vZj0algQRCFJqHLyTMRmnKNzD6h3tkgL1yJ2F8sWHIOI35f6dLS0txxHiVCSyYpsOhQVXis6JqelCtgsj2Cp59+PKn7sr5a8TJSMh/XXXc14vE4jj32BBx//IkpvRaSq/T5zhgejf8C30bHpbVPJUqByy1UOI1aei/HI+LBiRMLcFn0j/iCOxhvcYeidACISgYLFZU0DI+oT/mOzckQ9NLjNy9AdJ/m56d3risrLsaXS7bgow8+h893oFOJbBt/bPsVJgSfRGj8b/BzoJGhjqtQPHWxv6ampmNRNiF2JxvWzUmi0lNfre5YlMsGifDsR+On4IToPWgrPky5B9cYwJloCevcsYWDOqx7SFQaQjZxlq7MOa3pWXaLrFrcqH4Z9+a8jVhEfpZNTzjjdMAUk/KGfs44cvOwNFSKxdxErK3et5uJyxvsCDxOtvxtg5+eJPQ5uWnnKVU46WTOo8lMZkCiBC5BKuVvfCQAD+h7jgXkr1yODK3FxpK/4WXTA0gFg5ZOojmBTXmAy8SoSyXE97xqPkbjwmRdA0ZwdOLVn6n2xHBu9Ab8PfIr1NS3YsYMZdv5pgsJhO88KDIwUYTDIbFttV7fe+nQb3XfY5v+PFxt/Djt16JWschpo65Ms1P5FVVeWkAoMpN8hMyISq3tdKIS1NLJi1Llb4SQtDob9vd/p5I2RsXBxIpui4GWD0TjATRmqO00EU+IUGHRUyEpIOjTLoe6Wf8antL+E8M4KgpkiwqerhCrjKktZFR4l2OD/iK8Wvhilx2SiMMmxJiw1JMHj1N+ZlOqrG9XibkrDpsZgUD6bqXm5iYUV4xA0ZQFHSH/meTQCSPwMT9bvH7RdA2eeeaJbkuOErhDMaxa8D80n7UE//tmG1asWCZ2N73nnvtSfh0k3PmDTT78I/4bfMtN7gh+ToXEsbBcQ48rKkdFUvc7fkIBxo8Zh8cK7oB93BEd7rj+DFmUJA5GghDsG1GppZWGS3sFoygspRvUbTdqYfXsQqRmQ5dOJbKv1z51ObY+8DvMmDIwcih7w8U4UCvkiiWpqfLO8s0oOup8FI87uNfb7h/W7TvuaRwSewTL+PHwZjGGISHyqKQMvCIpXFspvAbq2Bo5bpz0fIMzV2lIVBpCFvFoCKeol4otj/PSFJXy7HZcpv4Qv9P/AJ9L2ZKKnDA92Rt0/b9WPV3IytupVWfg7NgtqOX3PcnOqf8vntbch0WWPUl3RRO0lo42q+l2fqvIoYeYoDEz7YqJU6lzhlAq5W9CPAq31EEv5k/BDh/xItfIwqpNTRA6NL4Ei7VX49FpOxFOlNLJRB2n4mFi0NctUri+VlBWxM0EaxqC+ImfhAf3jMaC+fNldfXLBmTFOcbvO/GuckVQWJjcts7o6H5mYtL/LogYadfS/dWYl9zERg5RHT2uFOfo0NramvJ22hNexoLX4wvxaXCcOHFMJfS+OwK6PHHf2OSX6rv6MQaeCsSsmQp5bMUh4uXBqp3Y0qRM6dP+JEqRCnOpuycIPUxpOpXCoPsry2dGCOu2hJKn+4G2hK6Qy0Vnyd2nzGd/oZ/kZWzQH4Tf6B7Cxe5zkS000uuyqCLdlurIFZWW/CaMyhPXYPWK75BpjhyThzcF2uXszCl6xINuPP88zUfqju92tuLM/63C5R834/a/3SH+7aabbhW7vKaK2AGOi0EDup20BWNpO5Wmqqlw6igeldT98i063HvSBDxxxlT89Tg6Ce3vWPVqtIOesxIlaNkm4qPjs3aeLhKkW/5G4iJOGsHh/IM0CHbq4ptg1y7aBKZ02DAYe2iAMpD4p/YKHBL5N953pb4PXee/C/Xz3sKho3pfqN4/rJu3j0BIX4A41PCkIeimWv4Wl8ZdxD2pJI1qOu4rztEM6g5wQ6LSELIIe1rwoPYxPK55EHn29MoTbFaLaIcmeFqVXf028HTQxVqULwXpbxC3DSd1eXPt1yHoIGEbFqnWoDC8W3Z4IWmzmq6oVCbZwuPmzNSjkwHiwQfTLCIS2tibFber8jeCL0g/N01cfrgeH6HbWlRIbRJs1PAoZVtQZIiJTpdU0PD0ftFeRCVWT90fWiF7E71UWVdHQ9PDdZtx+OG0zLG/EQ3tO7mrbvajuDg5UYk10ONnootUOpDg1DypJMdaKK8ENBmiBjp4d+o4aPSGjHSAa9RW4Pr4pfhby0JFXUqED4LTMC7yP9wb6/8lDC9bzsJfvSejxj5H/L1w5i/xafswvM0twJrdTRkL6SbUohBXR3+Ph7jToFGlNzyMMHTQrsqiqEQcadNfdyLvPh+KK8am9BhGKxVvbPAhFosf4AoimUokb0m8jSF7DhOd1KlKx3AIeNIPuW1ubECRNgg1w8PkyHx7b7Hct3wudvFFMKl5/HqiBo8//h+xe1p3bJTOAbUbfhK77hHXw4UXXpr2mIFEBYzmqnCUrQ5REuCWIonjoKC3ic5CXspW+Tli1KjQIuSgRbAhFu+bRh/xABW/2+P02JJuUHecF/B/k3bjvycbEPEdWBrdvGMlPvmdEQ8c1f+dZHLcWYRUtVSB55CP9qRL7fcP604IlARfFkUl4hyaV6rCEu3leF5zj/LdLnPognaxmoqTQ+VvQwyRBCGpfIDYTx1pikoksyERkOxxpdfadX9MrCQS2DI/WOprSLlNQlRq9e87QDPzVCQJy7DLq6XBq1kV73HAl0z5W5mB3l9l6znMLx0WLTo6pTwlQiJL4rO1TQjMvh7aMjqRk0O9n54YA4YCpAKjofuAUc2n7ADxwoQtkTx4pcFWd7jUdMKkNvRzB188hCuiz+CQqicRqVzRkZ3V34jHgdZOOuCe1gCKipITUHVmuu1Z2PRdP1+t39XRuUudgWOezpKHoKBDPZxwOnJQW6t8SVMiPF6IBBUXlXJMdJU5DpU4keivkBK0n7Rz8Jz2DKhLqVhuKRyFyzdNxxPcSVi1Z9/yZqWdSnFTERbrD8dqw7y0HzPRPU6TxQ6sb6+rB3fq/eDmXSLbtZrAZKMCqoHlYFALB3SAIyVxKgNdvLBJk6NsoDdZwQtSO3APLQNKh2DLHjEfhxMY5ORmZtFnf06ZUozN+SeJ10+eaEFjYwNef/2Vrm/MRXFr5emi07r2x7dFR/IDDzwElUqVtqhEyng/Nv0FT0ZvxNj81M+FjY1UVFo77ha4LlyPeAEt9fk5QtwmL4/8F0Z+NA1fbe+bzlZckO6LbVEpgsOZXvkb6USWWNgO+g/s/FtVX41jR6kxqyi7zQYyicNMz4URpLYfBdzN0DKceCxyFCQnonYO61Y3rMDV3HM4XfVtVsvfiFOpPM8MExOBjokh36ysqKTNHSleloCeo6uqhkSlIYbolah0UPfy+rS787AMA19EGuArGKAqloLYaDeOSIYcMv3NqXRJ3gas0V2Co6r3zfVRRenJP6ZK3sGjN9s72qymmt1AvoOdO3diG1+CLXwpdHmpDfCT4bzzLsSpp56GP/3petn3TTiVXl8fRHDGVYgXys/I4GNUFAhLuS2y0dD7GVQ8QqHUnEqruIMw4d5d2OrtxY4sOZXMbN+sNCaLpnE1JtS+gnsKv0aJPp7yBDHTrPFYUf7Y3t9rPDyKipITdYw5dJXPwqbv5Khs9uLW2Pn4Z+s8CNoMtCZmWBxneEW0zberczMiKoWDJB46LDr/lOz8RnBa9k4c/VlcHZVLm8cHRk0nOeWFe0s7xubR11/l4zMS1p1wKhWYVPjs8rn46FL54vr+xFgqKmmZ7B1rNta0iE0PmHg4qbD8rhA0JggsFYucRuaADnDEqfTolC1Yr7sQRwY/QrYw6bRigDoh6k1fXIy300lPE+wokMZLmWb+CAfm/uIKuE95A+tGXyv+jXRz47gDJ+3xmhXIFdpxELsLTVU7cPHFl3dMTtNdhGsO0H2IEbiUOr52dipZZvwCD23V4PU1dWLL9p8zZx1cAt+q9+FpaVAkLF4uK6zHYmL4GVy+Y454jkgmu7AniBszIJXpxsIHlvQl2t63sMo3mukr5rIb8Y72NtxVvrLL/a43fK00pLtFsKC8PLlxWeewbrVrC04Jv4sj2TVZK3/z+bzi4kCJno6vK8pHp+3E3R9jxRxcE/09boldCNZoE51K2exu118YEpWGkIVOEincETIhT787T1DqTsbs17UsHfyROJwMPUHkFfXPyaiSkKwZgYvBzvihj+z7ObJS1k6i40IyxHMn4MTInTij6dyUsxtIXgMJ4bxs1wJcZnoI1uLxyKSo9vjj/03JzUIylQhurw+uQETcduTC8HQbjqa48uOQJtAGIZyyqBQM0u/JZOp5cqAz0fdLVmv6M5q6n8TL76riOOKIo9Bf0ZssGDmGWrtDHIv2MFBcnJyQnZdPy+Ssgi/tjKI6bwwvckfhqfZZGZvY5EoZBCqTIyOi0tTqZ7BFfwH+Vvi94k6lEVYOT6vuxcOaf8Obwj6eLarqm8S8woOErci17J0wzRpbgQKuCbPZzaj3Kp9nRYQSwpRCDbSVn0LVRjvQpUOhkQozFnf2OrCe3fA30dkyy9ScuqOFYSDo6HHSaWAPcCqJ3d90HKxMCDpN9jK6TJ2c3ZE02oF3IIkpjYIDDmP2ynsEgxOxkrk46+zzxfPv7t2V+OCDdw+4nW/b1+LlD+ERKLRbccMNtyjy/MSpRPoruKN0+sMGW1KeqJIxjjZvOKq88bQCvwcKiTE/mSyT959tPJE4AjCg1R9PO6Q7QUigx9l4hGbZdcYK6l4K6VJzofdH8owsprE7Md7kgdstX1ANtdNzf2PMjGHDkqtA6BzWnWhA4WC8WdtnEq6hMQX0+GnJ671Lo1yIQ/wn4xHYJFRAbSsQF+T3X5AYDAyJSkPIIoeXyqwaGxRZTU6EefJSrbQS1Da7cHjkARznvx3OikkYDPhidFBo5TodxAQBWl4KcJbh2NIZLdgojEANW4JA4MATbbJ5SgTjqpfw5gWzMCynf4YcEkGKUHLcZbjmiTewZPli2Y+h4iJJ5Rl1x+xRdEVdxwVSzlRKfE8k4LgnjFYnXIIFbYJF3D76K+HKH8TLb6sEHHlkPxaVzDlwHHUpPuVm4rt6eixL1iFROowObKyqKDxdtC6XAxFECYmW8Jkg10Qn0Cpz76JSvSeMU59Zjjs/S16cSIS/eqNQ3KlksxixSLMe89mN8GXRci+XxqZ6/Fd7P9413AFWEqsJCyYUY5npj3hacz+21bky5lRalN8G2ycXQb/pxbQfk9WZslv+JvA4mF8vZgg6DOmVSNVF6fmqaOTYLp1KDikUnzXRToXZwKRT43tusniscQXSL8fRg54zWuEQS5uyBREkSF7eUyubcPFFF0PNAg899M8DVvW11d+Il183WXHvvQ/IzkvsMaibOC1i9Dt+b+n6tPKULhzejK+1f8Lx7S/g546w9V0svjQfNx5hT0mQSJeECMGHfOmHdEuQpgTiY0b3HXsRJ1ahWhr/2n8+WVm5uVQgs3HtKYkeERcNoK6PGFBampyo1Dms2xWmssMoYwgzytI3JiRDIt9oVB79rjlzZhoHFdvo4zvLxg7aXKUhUWkIWUS9zeKlP65SpDtPWCrLioSUW/Woam5DI5zYGHRAb+y9O8HPgQBHB9H5zN7JqRALirXPBFbK7UmGXJMOQt0GhPas67Xlb0+iEgnDHDmS1hn3VwwGg2ihnqurxKe6G3HYzrtkP0YrZ8J6fri44psKgpoObslicaqOld/nLcaOW0bDbup5cmDKK8fBkSdwROxBZN+8niTxECxt68Sra3QzMHcu7X7VHykxC7hH/QxyGD/O/JgOKJJ1Kgn6HHxcyeCF9TG429IrZzFEWzCH3YyxxtRE4GQ4PvKJaJu/fExrl6ISmRSSbJ5169fhxpe+xrn+p3DG9isRiSYn4gw302NVW0Ot4k4lnU0aSCMAbzh7GT9y8bXSzzUiqMUyrAQjDz4SjREddEwc1paVGctUUunpsWhlY/rCm8s5B5d9GMK7O9MTeJLF31wpOjBJlz+tJb1csRX6+Xg5fjjaBGuXmUoODRVx1VkUldQsg3tci3D2+oNRFUl/XGNX03ONX6OM4yNZSKTZjR9sgX3tI7jD+hJ+M9WMTZs24Ouvv+i4jRBqR2mELkxVMRU45pjjFHt+p5OepxvDdBEo6E4tn6q+njaXGW0JYwTbCIe6f7t/lWDllu04pDCMg0bmi80hss1Vuo/wnP4hTA0tTzukO0FIWtgmJbP7f7+JcilLEXUj/xwolzonksoGciyTS9RFhZKGiA42W3KiUOew7s176P6WI3gwsTA787OaGvqaix10HrQrouz4IsE83W6co/oM80bRxx8SlYYYohdiPjr5SYTbpcs9uACHRB7CS34aSqoEdS30ZMfGqEtnMMCBfh9Owd3hQIkGPeIAOyaoYJARzFyWo8c1gUdxLf8swt7UVsVJK9YrZmrx4fw1MP3wV/RniFupLUglFl1Mvrj5XnQmfhG9C69HD03p+QWtBbt9Guxw8Sk7lSrYZoxSN0Gv6zljIM++N2/HJ5We9je42pXQIo5GwQ6TxSkKf/0Vg1aDuarNmMVsBV9GLd7JZipBbcAffsrHhZ+p0epJr0X4PKzFq9o7can9R2SKAtZDbfO2KFavXo077rgdl19+EU455XjMmjUVZWX5GD9+OH5184PYFjLiN6pvMI/dhPpKKhD2Ro4UWN7W0pz0YDVZ9Ha6MqliBORr+69TKSJl5bh48z5ljBarDWukxm9FjdTBkQmnEunKRagLp39+36SZiDcLfo8lliORDdqrqONkZzwfFeUVaT3Wp7kX4Ob4xdgSy99nNZ8Ip8SpRCZEhIL87JbFTGj7Ca3v/wNcML1FOPI+lmxrxus1TphK5ecIpoOKZXD0uDzomCh0cR9uOoaK8MStlODHF+8U99UdQTMuvfASRZ8/4VRq8FERWxtOLXqBuC4YjR5lGjre1Of+/KMW+ERZqJ4/QGzNBhXun3AYlqGIq1fMqRSWulSy+3WpJJmgwzR0PzPmpnc86U+QxSyCw8CgXSp7loPOUoBn1wMb93hkORwTeWirtlKhhY14AC475+KaGuquytPQMca2cGZEpYsN3+IOzf9wqnmz+Ht1NX3ewcSQqDSELF7wTME1oYvxnvZ4RR7Pp7KjVshDW0Q5+7WqZRNuUb+IMwzLMVgQTIXipQYxMFIZSTvrENtoTwg+AZtFhnWcYfD7iV7cskAHzteUcue30WNGwMDGsaq2bzqFyAnrdgWprdrAyX+tUakSQUd8/CmwuMWAI5gn8KvwXxAKpeBUEgSYQO+nNfYc0uy0WcWWsISW9gO7nfQHvNu/Ey+XhIfjmEPT70KVSdR66iZhGQGqsFssP7RYkg/KFn5xF0qvfhW7mtL7LmyMlJ2mycxgiZBfSK3uzkClmFHw8MMP4q23XsdPP/2APXt2i50ibflFuPqIQjAQsD5Oy/vadyQndCWOW56IoLioZLHnIxSjYvtoS//NPtEE6fHWwx8opDZqaXdLXZPyTqXEirWDpd9BUJv+hM0vaGGefBSE/Oys8sebNomXm/3WtIP9Ey2vWb1ln8kzce7yXBw5GroN5eVlt7tsogQsFEjveOH1evDcj40447+7Mf6Ic5Ftjhufj9e5w8TrE3QNGJWrxdKlP2HZsqViR7jW5a/R12kZj5ljlZ3Qk2MLydtqkkQlfdSVsqikzilCKUPd+xrnz19UYoxUVHJouT5xKrFhui+6ggLy8pQRlV5nT8Tvo1dhiWdf1+GmHZUwMnThzZL38xGVOB09t+rUDHxt8l16S915uOAdLzZz8j6TRFj3svVbxa57hB1ZEl2IuENmmBswCpv4cugdmSln1Eod4EY56Purrt6DwUb2+qEO8bNgazQfy5gymOI0SDddTBoWpA7Hn5iZK4DVvxMXqz/Gj8b+XXqlJBZHPjxhATY9A8HfDOisHfXn4VAEVoe8jlAhTgWdKo6Yvy3l8rfy0XS1u03dv0MOSVhovY8OHoy8HwEiusgIeVVF/Yh6KmEpSC2jiGVJCZwerM6YklMpHg2Jq7oEvcnWa6j7f/B3lLNt8DXeC5Qcgf4G10gnh4ubjTj9lP6bp0RQ6faKtafbd+Cb4mJZq3cqnjT2VaPNnd4kMQfejGe8OPOpqDS5xIxTT10ApzMfRUUlKC4uFkv+ioqKMXbXY7BseRG/MFZj6aYI5g0HSj2rk3r8kN8j+i39gkHx8jcSMNseFmDQMLTbkzW5LIhs4wjuASwkz+PAbUgYNheIb0GpUIe2aEx0ySntVEo4cEK69EtLcjURzGU3IWhMrbxILiUxOoBf3xDBojSdSkRUsiIA1qzdx6lEygTtekbsXEsQpAlatjgzbwue+IsFnwWWpPU4zc1UCCHZZX3hBB2bb4baXo7FvklYoNqIf5x5EE59eDn+/e8HRFd1fm0I04Y5MPLsy6G0BMyyrOhO/nxnDOz4X2AlxuMYQZCdK0UylTT2QpQxO8XfedvPJ3enO1ijFLKsifZJplIs0CZOWttjasWcSs5hk3D3/feh1LhvIMDOmjrMYG5HgVmDT+3KBzv3FVFGL5ZXk1Jqf7v8RePaWtr9LdmQ7v3DuteuW4foghzoom147tu1uGv4yKw4lcgI+ff8nxGIM3gpV5nSyf3hbPS8U2KIDNrytyFRaQhZ0JBTPYxqZZxFk3X1+GXsC7TplatHz4k1iVt2kM9eR5O+hgySlkZHwKQxIVDTjulOwCd1OSItui0WiyxrvEdtRw5a4A/Iz1SKRqPiwbTMSAdZQj+dwHX+7DZW7p04MKTDoYGuyCXDXzTPY6SmCqv1JwE4X/bz69VUwGLUupS6v5EyxwRmS++TnLHhjRjjANZH6cSiv/GH4MWIR36J9vrPcePIUejPaA17c2/8bU0oLJSX//Sm+T4cpN6Dh/1nA/hNyq/D2bIKGAaY7dSxmAl4Ex2Ildq1eOutt9Da6tsn611T+yMsW14Sr49cdDneW36reD3PtzGpxw/42kCkpKCxWPGgbvJ47SEBxRagvaUG1vzJ6I+wIZcoKsU0By4CFB50LALLXoBZHcN365dj1oz5ind/s/H0OBgxpL8QMJyvxCvau7A+txgcd3vq3diSJC9IM3hWrt6AC8vTm+Af0fYKbtM/ildHjcGz2/dmDhGBSaNV49smC6ZPngCosjvGqFUXQ82uR1xa6U+V5qYGjJ42EzA4EY5x0Guyk3uVgAg4xxK30pLDRFHp+GIX1CoWX3zxmfh/09j5GHfInbAXT4G85bDkcDqd+GhzJTYcR4+5gSgHs07edKihoQ75ek7M8eLBgLPQrJqfM4kMsRx19kUlgeehIk4lBmjnLYo5laYXmxDc+gOCpfsKR4071qHuq/vxpwceBtLc3/oTOo0KdUKuGDHg88tczBIE7IkbUHTUBXAOkycGdQ7r/m7qB7h1sRsqtTLfYTKiEhlfE0GJUGilJY9KEzCWiccrI0f2Daaj7G4w8fPZU4bICuNiG8WWxyUKiUCjtG24TP0hjtRvg1LYwzRAUcUMHs2U5AScXXUSzordjD0MzSgw1n4ntle+VP85rNbkh2bkwB+QOmLEI/KzXkgpDMdxKNXTkhyto6zfO5Wi4SB8Al2xFZ0MMrCxQQy3szCkWP6m17D4QHszfiz4B1Qh+UJPJESFv4Cggy2J7zkohbrHlGhLrTBxXsAeH1AlFGJsWUlWuxKlgtG4V1Ra18iLrh05hKUsNCGSXomoRUWddgZn5gRczkBFJSbQjPB+4dtVjS0wf32teD008WzESxcgVkBXJp1cMxDtPUB8MXOw2Nmq3htT3KlEwvi9mlwxY+6jdf13oKeJ0UE+pz/QcTZx8kFY4aN/D+78XrHnDAaD4o9NB+gEqZOlMX1RSW+iwryJjSEUynC+ocAjKBjgiwioi+fAbE4vAJaRFhXs2vg+YbZEfGtVF+A82xM4pJlu79kkxNBsRB3SG3/56rdj+y+2YcOiFWj1pZbjly7HjMvH5/wMtAtm6MIt+MuZewX54Sf9AQ+vDWBljSdj4yUhHoWWoe4UV0B+viBxKhW2rRKvC6SblEqZnNH+jN4iiUqqMHye7LZLj4T90DJ0obTF7VcsqDufdeO0CWqMMXkPyAUljBrVvxe2UuGEyD04JPJvbPT0nMG5P3zYi/fyHkb9vDdRWiDPFd05rHtbcxjNsIul7pmGlGmScl+tjbxeAUaNChaZAnKyMA5aAmuHBzZ7DnVI9eMuy5lgSFQaQhaXqN4VWx4fZEot3HB/GCk0zsxGFB+Y84bsdWbpa8jKGxeQ6s0DdMI3gqkT2yuPD6yS5VQiBIVEm9VASqVvpJOZU0UnEuZ+HnJIMpXiniZRlCGwEXmikloy6DPa5MPQO0OymMqZJlSoW8GH5Yt4USm01S/oYDLtFTm6I8SrO6zk/Y3tzT5wrFr8HE5cMBv9HVKqseDZAE5/I4hNLbxYAiaHIKiQycZS79pGsowcOjo5MmUw+0Ew0VVFVuBEp0MCfySOqndugtpXi4ixBIF5t4h/Lx47A9UeQcybcu9e0evj/407F5fF/ojK1ojiohIRJ89YO0vMmPuOn4r+ypvMEbjddwpWGA50IRmNRrzYMBx/iF6JV9rGKV76Jqi0eCz3VtwaOx9GY/rt241WWipjYqMIBDIrKpHq+WdMV8L29wB0CpxvjFbqTsrh3XC792YqEYFJpafCvU3KXcomMTU9vhuZ1LqEJvC10rKMVsGKAltq5610GZZjwJiiXHzKLhR/v2y2CRqNBr+cPRw6NZ2ITS6yZExUUjHABLYWR9tqEYnL74VKnEpkHclnGYN43iQMBoioREqnmngrwl5l5gDJEnDTJgbk+f3BsGLlb+bGH/HG6UacOUXfIQAQt/1hOXX4+LdGHMwk57QdSBhU9H22B+TNu/xttDupRzBi5HCa8SeHRFj3rq0bxMtgjEOMy2wf4kRY9u2HW7BJdwFu1b+escVKlcGGNslbObbUKXZzbm5OLZd2oDIkKg0hCzPo4FDQpj/oJGik1UyrgqKSXqArb4w5O9bK/lLCtVdUop+lXQrvbW1uhsViS6nNKmKh1PKUcqgbxisY4XTm9fvPLt5Why9aixCYfQN4o7wVMMZAB74tpG4lBUj5W1j6vIWofFGJnB+3RvOwM2QTg6J7I2CiNv369vQ6jmWCiT9chn82XICiZf/EoQtS66aXTYxGA36o5vDmZiosyhWVIiyd0Gn41N0Ci7fWoMBGRWBjXgZzPVRaNDNO1PB5aGvbO6H48KM38Sv+U/F6+2H/6Dg3jBs3Hmv5EWgWcrCxsudsATKYD0Rorh4fCShe/kbQsHQg6Qn13+5vy+Kj8T/Nr9Fsn9Hl/1sNo/EhPxfrgzbwCq2AJkrfjFYnVhkPxWs4Gvnm9MsDtNJxkYTdBoOpi6bJsK3Zj4d3W1F08RMoL08/MHn4MOr2tUZb9slUIp8Va6Dbt82Q/fL6uJo+dyJAOFViXuqIbRLs0Kj6bhrwj5Mn4Kgz/ojgtMugPvpuLP5+CV4+icNK3WVYaK5DngLbYXeiUr6JwbuaG/FE9CaMyZMnrBFnHwlwJ8f+ppPfhvf4ZzAYKLCZcCbux6gPp6ChPbvdlcM+es5x81RYVar8rTJIHWbW3HxxgYZQVbUHc2ZOxnGj1R1Cys8Js4aeC71inEnyeBt3i5cNnC2lDpuJsG5j1Ze4Vf0CjmOXdWS/ZopECdqIfINYqkpyRTNJs5qOAaeNKhiUuUpDotIQsjAzdPKjNigz6C/OpxN4s6DcBNcolQB59HRgOBggg6TfObZije4SnFp7195sIHISDguynUohqfyN5cIpdX5TGW34kpuGH4TJcJq0/b78jfBSpQPBGVeCt8jbbrQsXWnhpVXkVMrfQoJUBhWVP1DLHzkDU+6vxiH3b0rKqRRg6bbApyBgZZR4GDlNP+LU4WGMsjEdnY4GSvlbKqJSVEXvrxNSdx7sqHfhXv4s3N92KHipC2SmuNTxPyyIPoQa0Of5ZnszjqujrcBrKs6Aajh1HRDGjBmHi5cOw6zII3jN13OGUTAShV7sYChAiIQUdyoRTFo63PFL4lV/hFPR426xs+v3P2f8CPCxCOKMGjXtIUWdSk5nLu45aQJ+vPoQHDZ6b45Qqmgl8cWICAKBzIpKu1vp48c9jShPM6SbwOul1ulGRhQPeJ4e44nAdM2UINbrLsTvo/9F1tFaOtxfaT1MjC5AtSK7QeP7k2vSQnCOQ2DereDsIzHGGoU+5oZf0MNQPCmjzu6WIBVlGYEDI3UVS5bGxnowGh1Kr3kdV7y/S8ylGgzkGDRYVBCHf83HWc9UajSMwcTwMzi26QrR0abUwoPKQOcLZjUPv9/fUfpWLiUJeHWZPaf2BWepv8S72r/gBOt2Wfdrr6O3b4iZxH1ILomwbod/Oy5Sf4IF7IYsiEpU1BmhpRUsMydOzOjzfZx/GU6N/BVrdDPF34dEpSGG6A6BhwV0IGuyUGt7uoyQwvGs6pjYqlcJHCwdYJoc8iZ4AxmHw4FwKAQ744dRapEb8NBLN6eXLSo9K5yEkyJ34hPPiJScSls9WtylvhrPFt4OleQQ6M/lbwRXuxttwSg4Xp4DQC/V+at0qZURGDQqxKSeCVwKohKxasdidMUpqfI3ScBi4tldaewNTdMaqMGhwcdj9JwTMRDYv3OS3EwlnZ5OvE0ptrUmNLoDeIlbhMfbZwPa1ITNZMmVnANN3jBaA1Hc/eUuXB67BpusC2E46v/2uS3ZFg3iNsZgU6O/x2yBmGs3tugvEEVxIR7JiFPpuPwWPKW5H6fzH6I/QqzyC0zVmM5sR6mj62PJ9GnTMMa9BJer3kfb1m8UeV7S0Ywwr1wP7a6PofJWQa3AMTvhjlUzPCKBzE5A56+9Gl9qr8P8+ApUVMg/Z+1Poqubw8CIgpLPRxdoXK425BkZWJkQ9KR+KsswUuldIkMtVcygk2evSplxnBJZerXuEDQ1NCtsKT8e44uTb5aRyniJVLx54tRtxgblZRk2NDRAbSsEqzOgwRvJetB5X0I6aSayarKJJ8IhAAMagoxY+qZUCRMrdXA1iaKSr0NUGqaTMkF/Rp3fEpRovDiI3YVhOnkLi9E2KpA0x00pff5jxowVLyubqMDjYHyy3VKpOpWKTXRhwJqf2S6N4cKZWC2MAW+nixtDolI/4e2338bYsWMP+Bk3jmYJbN68GaeffjqmTp2KX/3qV9i4cd+61w8//BCLFi0S/3/FFVfsE7ZIBrf3338/5syZg1mzZuEf//hHx0rUEN3DxAIdrcvzHcrkFRmd1BVChAd/W/qth8mK0a+i/4fjI3fDUj4dgwVSwlXfTt0O1jidJNQ30c8zaB8tK6ib0KIuwQZhBBoj2pScSnF3I/6yIA+Pnj4F/R0ywCTEFlyGax5/Da6GXbLur5dKETQ6U8qikh50pZONyRd6AgF/t86ZrvDDAJdgQYYXiGTj304nyT+023HEEUdhIEAylTpTVCTP5TYsl06cDMH6lF9Ds5duMyZV5s9heWZ6PGjxRXDXZ9vhJqVkuWORc+YLXZZEjy0wQ+A5+GICmrzdO2siUi4YETwtFmtGOoWVm6I4SrUaE1CZ9H2IG+jXz63E2+v3ZkhlCuIYesb2FN7W/RVlxq7LwSdMmIRfqb7HDZpXYdjxrmLPSzh1RAC2Ty+Bfusbijwuo9l7LIplWFTKD2zHKLYe3rZWRZxKUa0kiLEMcqxm0a2UcCo5JB2Zk4SnbMKY8/EjNxFLg+kF8ifyDiP6zLTWlsPmRh+Of3wpnn39ZZiX3C3+7Qd+MqYUZ6Lv215nN6E1To/fHyyTl5tTX18HtaMY32n/iI/ZP0LVLm/MMJAZ5/4Siy/JxUy7PHdXunilsmU+pFxId2dRyazm4PNJolLlTgxT02OWKTezIkRfYLFRJ6opSnOqkkUVpPlA7bw55QVc4jJLuAQn2yIosOiykqnkVNPxB09C9TNIiRRFwJjpZzzYOsD1W1Hp+OOPxw8//NDx8+2336K8vBznnHOOWM98ySWXYMaMGaL4NG3aNFx66aXi3wnr16/HLbfcgj/84Q947bXX4PV6cdNNN3U89rPPPiuKTv/5z3/w73//Gx988IH4tyF6hpE6FEXiAix2ZeqZ1XoLEtUIfhft2pYOdS1tYleBTfESlAz7+Z0Meupu1OanJ918hp7stXH6fbUH47K74ZSZgeCuFYj75DkoSOYEEXBJhulwBVaMs+lUukT1Pj7R3YScjfKOBXv4Auzii6CWMkRSIc4m3EPyS1p2f/8ctt0yBvdfdAjU6t7DY1+KLcLBkSfwTOhI9CfCO6iotMZ8qJjHMxAg4ckJyGBJtiU8dwze3RrD8to0FL6gC3PZTRhnyEynpM4sCFHbvP2nu+DevRIaFYM7jh8HbTedDyeMHYsbY49hue4KeNa/3+3j2lm63bv94YyUvhEEqTzVgqDojEiGl1fVYrcriHu+2JFShyg5NDXUdmTlmHK67r5G8iC2SB17RoXWK/K8iQW3XBP9Dj+qVmhYyKrwz01O/P6jEAKRzCnYpFung6fC2Lo97YqISmqtER/y8/Fy/AioDURUaus4vzn1dNvhDdl3+VgLRuDUbcfgstXpdaTK19CxMmvt+4iACocRoRiHI8OfdfxtKaZgTF7myp9zc+mErzlKJ7Qht7wFTdIW3ewoQDnbjGF8XUe55GCgpaUWhxRFUZST3ViDw7if8ILxYSxqf6Pj+1MClbQYYlLFOxbo6hrqkcPQigdLfv9uNJMK4yqo+8oSaUYolPyY0xyj8wEfm9o5mmVZ0WXWHKDH0AK1H4VWeR3o5EJEHbIWRsvrgRYmsw2chhniOEv1BS7S0eNZVdWQU6lfQCbJJIgt8fP++++LDqPrrrsOH3/8sTi4uv766zFy5EhRQCJW+08/pWGhL774Io477jiccsoporOJOJG+++471NTUiP9//vnncdVVV4miFHErkcd86aWX+vgd93+YiKcjo8dqU2aVLhTn8Yv4vTgk8hCawumfpPY00sElH/btM+EbDISkcbuF5FNxEailPCR/lElKbOjMGXl1OK/+HhwW+0HW/XbupG1YF1+Ug/JXZkJT/R36O4lMpbYI/YwEmfkKZ0ZuxpHRB8DnpC6iBVR27HBxCKYw+WIDjRijbkSBIbn7GjVsx77Xb4iHURrdIV7dg9KMdefIZPkbyVMigyZZjDwav3w9grt/iKbslp3Ab8Er2rtwk+NLZBonGxRt8xerP8J7utvw1MilGJXbvTuOiIOGcAvyGTeEetp+u6esPrfHl5HSN0Kia1ce3Ign2XGGTHgTPLsssyuOrsaqju5GemP3nwFvpEJAQbxOFFQUy1TSUkFrk1+5yfyzmt/g9bzLUe3LXFtlrnmzeFkr5MLnakZhYZEij3un7k+4OX4RvKy1w6lEBLicGF3dL8xX5nnkMH9kHprf+Ctc37+IeDw1oY6US7+9wYfXqp0oH0MzTvoSo1aFhaOc+F/8aPH3iKkEl5y4qFuhWkmnUqOfbpeacItsp9LIAnNHswVhEIlKYQ09Nlk0fFYrPEoCG7CAX4Kpqt2KOpVUiRJ0NtZR/sYHqXjihQmsdN74OaHLKe4o7125cnnS99vQwuLZ9YA+DUE9P78ALZKoxIZSL/uXIyqVWvc2DopIOZaZYnyeAXdqnsUl7HswqIlTag8GE/1WVOoMCYR76qmncO2110Kr1WLdunU4+OCDOyYe5HL69OlYu3at+Dv5PxGMEhQVFYlZF+TvTU1NYj30zJk0RItAHquurg7NzfLqqgcbe6I2XBO6GHdFz1BsNZkMKLbxw1Ar5KHJnX5wsK9+m9hV4Fx15idY/Q3WlIsoJx2sg604i7kHY8PPYZVL/kG0iKvFP47S49gSj+zSN0JZrglszI8ljZmbTCjtVGqL0sMhK2OiRjowMSqay2A17puvI4cLon/CEezT+MyTwqqYFLgd4pLrRmTSUfGsP+UVa5rXQsfE0SLYUDqy/7Z876n8LZXJ7E4fg/Lr30f+b//eMaCVi0mgK6phVWbab3emoJCucKrAgwWPqQfN6fH248ZNwDLJhWV3d19iwkTpe/eEkTGnEmukA2E715Z0BsqZY1VYOewhnMAuFUvgGrzptXLviUg7deq28yYwPYiTI6fMw3YXBxJ7pGnsXqiTm6lkl7IIlQx7D+dPhHnK0fhuxZqOzkpK46uhjq2tsUIMy7MrVjppJXZbst3o93UqkdxCQnlx9l0+nR3HfinnSS6trS2459N6nPVCDUZOnof+wHHjC8QckguYO+A5+VUsGKWcE6UnUanOTbdJXXRvPEYy1NfXY2QuXQQNGIfRFqyDBE5n7xAkEllj2YCVFvtcQUF0uyiFYCvHDbGL8Vf/L8WgbvKjYXm4BRNa2f7duThVeD01BThNarz11utJ3++5DSwueMcLbenBKT93fn5+R/kbG/GgxpW5bYjkfnm9HrFD86fcTCzhJ2aso2QCjdkBXkfHMCMdLOrqasFx/WiwnWEGhKj0yiuviBviscceK/7e0tIi/t4ZUnbQ2EgtrEQc6u7/5L6Ezv9PWCkT9x+ia4hV+F3mcLwdndsR1pcuLMOA4egKaYs7tUnVPrTtFLsK/NawFIMNMlBKrLwJ/ib4ozwi0EInOVPkoDZJWS9SCHWykIBDrQrIV9HvkpPZSa2v3Cbkpz1MB4ZsNHkhLcYJiLdWI9beAIsx9ZNVFCqoDBaEk3RQdCaRwxQSknOjTdPV4SX2NtxmeBX9hT1V1VjNj8JifjLOWNSzUNGf6OyGlBvSTbCZiPVbgEGn7XBDyCVHoNsrn4WMF1ve3v05NOlsxEsX9Hj7UaNGY+kWmkc0jt8B8F0fT7weOqnzQZ8xUUltoed5o4zOWfoNzyO3dRke0f4bZXwtnvwpc1Z2vZ+2rg5GuV476PxQQ4/zwcofFXEqkVNEDugxW2VVzoEzxdiKOexmrFv6DY4+eiHWrl0NpbEHaJ7N+vqwIqVvCXL0KlgRgNlkhNvdLjrlibhEOsIR+qLkiSyqbrnCjNhfLIjXr0vpMZqkrEXi9pDtrMwQs8tzxM5iX4dGYaknM/t/V6LSJ9sjeDh+Cr6OTpBd/jbcRvfBuOXnF+ScjKjkNNAF/2wRcNO5W3tcr6ioRByH3+4W8PLHy8VMpd27d+EnlxVTXPfiX+WP4ufIJjcdK+bmmPHhh++LTSKSIZEPVCo1WEoFctxpCwngBHoc/WjlFmQ6T6nVMhaXxf6I2/Q3KtKEojc4Gz0PjcvTiI5ScrwYLMiriekDyIn8jTfewEUXXdTxN1IDSk6unSG/E1svgewg3f0/sfN0/n/ieuL+yTKQFycSr13Oe2h2U0WZjwTFdt9Kvf+T2J8wQd0EtI8Awxyf1mNZwnQC441rkD+Av59UA6eX8+Owh9MhUOuTyuEYmLWs7O9qi98AMswyO0mXDXlOpVIrvUNQ0MHhLBwQ+wlxK7WHJCt8zEM+NpHeXrs+7sG3xusRiglQmVan/F51pJNQDIjEeNmPoeGoqBTmNUndd6I1ivncTlRG9f3mu3lyA4ePo3dAFWjBsoLM1rwriclk3Kf8Te7n6Yw3Y5vuXAj5DH70vA2GkT8pzmteDlQAZqsz498n5xgDQWMEYylCcP4tvT6fTqdFRGOHN1IPqy4Mdft2cLkHTuCqGpsxjLh1ckbBGrdl5H3obIVAgAayuqNR6HU9l1s3+SIoKJoDE/4t/v6g5lGctvn/cN6sUlQ4lS+t5v3NgBEIMSZIh9AuGT9+PN41HyImzwR2/YCcNKPRiPumyEKfMCqoYMzJV+zzv9vwEsZy23DuyLF4/uP1OO64I3HllX/EddfdIEYYKIHVT0uuV65aj4pZ5yv22v8cuA8z9d/hz2NGimJSMBgQ3VYrWq1YOGwU4nqH6BZLd1wlh2ZfBDp7EdRsE2K+lpSep7auGmNmLYDeaO83x3+NmsWCEQ58sKkJV721EUv/tCCjkz+z2SRuf59s9mHTsb8W/0ZynYhzPhlItUNJxEIC2qBxDgfXTz7HbCAYJKeSnofH0570OSvdfcPlagIZGXgtFZiWL29c2hN2kwbDItVYvmMJAoGTxIXR4JbvMNkaxV9u/7Tf7CNKYrA64RMM8MEIr7ceX331OU488Rc93icS8KB4/kng2sIoLBmW8udCyt9IpOFl7guxzjANB8eVm0vuT20tFZVyR0wmp35UOAxZ+T6rhEKMwjpMmzIOb25aJ3aAKy3tublCps8d6SDnNfV7UWnDhg1iydoJJ5zQ8TdyMthfACK/kxymnv5P3AidBaTEoCZx2/3bQ/eG05n5coNMI+c96Hy7cAS7CbvYAPLzlVtNOl6zCseq1+Cd2CnIzU3vM83jaXcC0r063ccaaJSUFOHyqhEwjVuAm9kCPKm5Dm5Y8JhZK/uzMDrygBoSXhiDw2FKekWTrPIMd9L9qk7IxcThebAZkyvL6kvy8nLhClKXkD7uhUnaL3rbPyItrch1sKJjKVhaAJsttW3uLNXnOFz7HT4oJqtw8h5DJ9A8mjiT3PecQ7JA2gGjKt5v9pEVu1uA3BKMzmH7zWtKBrV6r6tk9OgRsl+7IBSLZX/i9bgvpfduYuhCia14eBY+Owtw9TpAY4BTl9xzTZ4yFSvqanDkCDXs/s3AuNkH3GapqRyfcTOw1sugsDAvI++joHwEUA+EBQ021DfjuJk9h8H/8b3NWLabxaMnfYdF356CKaHdeHvijxg76iRoVMo7PLSx9g4HTG/vf3eUTuzyo1Ww2g2ASp2WqDTGQt8PaXIxsrj350+WRrURpLHlBb8+AVHrSLz66qv417/uxxdffILnnntun5iClBk2FXt2b8W6Jj8unDhOsde+y5ADBAGHkUVjOABGClH/Q95TYBrV+MlRiuIcQ1bHhhqTHrsZWs6u5rwpvVdd6xpsO24dVsXlH68yydmHjBBFJbtRg4I8S8Zz9UiFAom9IAs6EU4Ar9Ugt4d8uARkvtDS0oSiVg+Qq4G9fBzQjz7HTGOw54tjQ4c2jkYhKnsbSnXfiPB0Udvli2LkyHLltl1BwMKSKDBeDYYLorGRZu9OnDgeeXk/vzwlQlwzHpMjz4AhEyWchA8+eBvnnfe7Hu+zef1HWDbuRVTyBSif9GTKZcYjRlCX006fFs0GO4J85sZ8bW10PjistATbIGDiMOXObT2xjiGiEjC2kB5P3O7mpJ93oOsK/V5UWrx4sTjw6GyJLygoQGsrDZdMQH5PlLR1938S+E3+RyBlcMOGDeu4TiD/l4PL5RPFi4EIOV+TjVfOe3Du+QT/1b6Ol6wHobVVgVI1Cb9AB2bxoDvtx417mgEzyRexKvoaBwJGowWcn27L/pY6LFKtgTumxv90C2R/FoJKarPKxlBd3ZhU9zhSN0yCuufPJPtqBA1MHnICIbQGM5dDohQWiw3N9bXANCoquVq9cOZae90/grV1IKfIYAwIBOKIxVLb5hzwYiJbheWaUtnflYYLic4qEvCbzH1jLP1uDSquX+wjZMXd7SEDdGD+mGH94jUlSyy2t1zRanXKfu2RmAa8wIBlBFRX7pa/nwoCrCqSC6KCYCzIymfHMAY4zcmfO0aMGI0Va3U4cgSH7au+gaPitANu86N6Pl6JVcBT8wYuLjJm5H0IrAGl6y+DasyhuNHD9vgcpDvcqj3tiMZ5GC0F8C68B9ZPL8PEXU/Bs+V4xAtTz5Tojvd847CRGQ69tQBFvbx/U04RTo7cga38MHzvCkKVoquDHLNJ+PR2PfAX9bVoC8ZwAnjFPv+I2kJOBVi4+58YPbEUf77/dNz47Nf4YtMmsUmKEq6l1cXX4M/fr8Hmljrk5hYp9tpVUu5IjuDF6vpG7NxZBUZrACMJeHwogtb9wrJTGVfJgeMFeAU6UXE1NqT0XmPuBkADtPL9a4w0wqLBY7+eLGaeuFzp52v2Rk6OA431dZiiqYfTHEdjiw9m8EmVAJHj7i43g1jeZAQ1wxDrR59jpomrLKKjkWfUqKqqT3obSnffMPP0eVq8pBLFrNi2S77Ly+2LcdWvjbixtR6VzQH87xQ95g5bifbNP4LLn4KfG/EYXQwTGAas3oKPPvoIu3bVwNZDA6bqbRvE6oUmzgprO3XHp4LBQIU6d3MDkA+0esMZOw5t2bJdLDp4J+8RxKHCx6rXsnLMCxupcFZmoJ/Tpk1be33eTJ870iHx2pKhfxRU98D69evFEO7OTJ06FWvWrBEPBgRyuXr1avHvif+vWrVqH6sq+SF/J6ISyb7o/H9ynfxt/xym3iBPP5B/5L4HSN3f/LxW0dcRAnWYqePBtB9LJU3qeb2zzz/fbP+QQRIXkHJZpLDpdn8UFotF9mPpLXQl3KyKwefzJ3Uf0jqTrOKNLKCD3nYNCXxlBshnZ0f12h+xXD0X4VnXQuClk24v99vTSENuI2oT1GpNys/Pq+ikigREyr0vEWXr4jZEhOT2S6+GBhabNYDAC33+2bd9ehe2jn8c126/GL9dOLXPX4+cH/KdJzorFhYWy76/Vq2GXzr++d0u2fdfU9mIgnwpKyivol+eO8aOnYBl4VKs5Mfg2zZHl7fxh+MdpdWk+1smXrPVmoOQVP7uDcd7vO22Jj9Gxndgnm43CrQxhIYfj/CYX4IReJi//CNi0ajir291MB//0/4a6wyzer3t3EljsSZcjAijx25X6ufNtjaaFeQKCViqX4DPmbnIM+kUe0+2w6/HRsNMcRJaHK/BdN9n+Py0GK797VGioEVcS4sWHYo1a1an9PhE9Lv89fXwHHotVBYnysqU2wfGldOJgTVYK+aduVxtUEkTIp2ahU6t6vJ+mRwbkgxKv0DPFbGgO6XHMMbpGMGNzOxn6fzMKLWj3G7MynORXKU8E4M3cC0ei96EkQ59Uverq6sHWDUe2pIP968/QbRkfp9/btn8yRs5C/OqrsTc9wrFTCU590153+A4WKSGFK2eoJippOR78oMubEcjYbH87ciJuRjN7EGrN/05SX/8IceuMuJwBXDokYvEst4PP/ygx/uE26iDq5mTP5/o/EPK3wizmI1iU6WxgRUZe58kU6nUxsDIRKFHFAVF2RkjqZy0E3Spmh5rydwomfv1Z10hWfq9qLRjxw6MGkWMZHshgd1erxd33XWX6IwglyRn6bjjjhP/f+aZZ+K9994Ts5i2bt2K66+/HocddlhHTSP5//33349ly5aJPw888ADOOeecPnl/AwmNJNgEBa2yjyuVVpmkwU46sEbqwmjWyg/NHeiQMPqTc3Zhre5iXFX3J/Fv7WEBVqt8C69OamltZiIIJNmVaseObeKlS1+Br7hpaDCOw0DKoyLz2ncCMxGccaU4aEyGoJ8KrWHo07LrcyoqLOhZ+UHdH0XmY9hdNWgIJ9cKXGummUUqRgDife8iU9X8KDotjPnDYTUoe2zJBhUVw8XS6/3PU8lAthmfQPN5IgF5HYgIO+pb8QjOwD88i8DmjkF/ZNy4cfhgdQNOi/4Vf/cdjbC0StqZcIRsh4IoKvW0WpoO5HG5MHVAtAd7zk9cU+vBFer38DJzC548bwzuuOM2+A+9E7GiWfhq+E341XNrsLJa2ZDaEE/Pg05z72X406dNR7S5Ury+ucGbVkg3gTTeePncGfjx6kNQ7ki9i+X+2MoPQsEF72Djr5Zj44z7EBl5AuL20bj6H6/h+DvfRv6EubiorBJ1jx6HN/9+iewW5XUkdFrgxO2G87lQXl6u2GtPBHGTYG4S1E3KBI8do8VG3QV4RvMP9BU+np4r+HBq37sN9H5hbeptwX8O5OY6xdbmZL5ExGJG6i7WGw0NdXAsugTsaffhjbWDJ4A3wdgCC0aFdyK47cesBXWT3gWTIs9gfvghNLYF4HQq1x2QnIODoEItFw2iqmoPCqVGMyrbzzeE/fhxTryq/Ru+mfEVJuSxeOutN3q8vTpEu6O3C+mVBJKgbsLBhjqxqdK4WPddYdOFuAon5NEyPc4+EmMKM9/MhKArnozTIrfhTO5O8XcSAD9Y6PeiEilb239STEKin3jiCdFhdOqpp2LdunV48sknOzrxTJs2DXfccQceeeQRUUAipXP33HNPx/0vvPBCHH/88fjDH/6Aq6++GieffDLOO++8rL+3gYZJsp/GeGVa9iZwShNJk5QpkQ4OltoN1eafZyvQniArb8FgEDlMAGrQlX93TJNU6dr+aCy5+G30ZpzivV5ssZoMmzdvEi9XqabhqeK70TriwDKX/gpxKhFc7e3wSa6JZIhJk9RkO691h0kSQxP5SHIIBOgKnsnUex4Ewe7IF0uuCFwoe91buoSLopyl3Ygsk0/EQOTddz/BN9/82NFRSC6hhEgf2LdkOxnqXR68xR+KR9vmQDDJc9pmi4qKEVBHvKKLkpTvbG+h22tnrm26Djt05+A4R53oVMoEZBxxdeF6PK25D3ktP/V427W1bsxkt4rXF1fF8d//Pom2IAf3qW/jq9Bo1HsjeOSH3R1uaSWYZmjEdGY7hkn5Rj0xevQYWNq34271Uzhsye+I5TCl5yRCCeHkiRZod30EVbApI1k2pUUFKJh9JrzHPoH2M7/C2xsascmnhfmkG3HhbDvOm6rB5eaPsf6122U9bt5Pt2Gz7gKcHn1PzMhJ5VzXHYJ+b+t0UiJIwrpzLXqYmTCMrLyuqEqScCoxsdRKxBKdWQUzcRIPXsjxmiMuFUmkY4N00twb9fX10NsLAI0OhhQ66/4cSHR/Ji3bs4E3yiEIPWp5B2xGHTQaZXM6QwLdBgLeVtiMKnHBLSqoYXZQV83PkWMnFsEt0HHnxbOt+OGH79DYSBsddUVi0d+vTq/rZaIiqL6djnXNnAe8gufRA0Uluo8yzjHQqrOzvxY67FgpjEMVZ8dfbrsTN954KwYLA6L8bcGCA9sWT5kyBe+88474f+JImjBh344yRGz69ttvxTK5//znP7Db9+4IJGDspptuwooVK7B06VJcd911GQ8F/Dng4OlBxRA/cFKQDqyRfjc6pO+auDB0JY6L3INY8YFhsD937HYH6tv2rXUO2EakNEkz6PT4iZ+E9aoJ8EuiRW9s2UJFpXnlNjx82mRcOl+59s7ZEJX0ZVOwJmcO7nn5HTBS+WBvxML0syF5RukwbyQd4GuCrbJX6wMBvyxRKTfHinaY0S6Y0S5NKvsK9+YvYVALaI0boJvSc/eR/goZJI0cOTrl+6sYehrWBWmopBwa3fS710th3/0Rcr4dM2YcIg07YEIIVVU7DriNngtCw3AIRGL75CcqCZmIzLS1i1lzlkBVt7cjA1x33WY4GR/CHIuV9ZzYNfa1114W/3/+nDLo1Sx8jTuwdAttZ58upO3wA0Wf423dXzFe15rUZ2pRAyerfsKI2Hao2ran9LyJ7Mkrp8Vg+/RSaOqXIeMwLBaOcuKUyYVQsSwuj/8Rn3M0o2p4/XuyHsro2Q4dE0NjexDl5cMVfZm7AnTiml+QJzqVXC4Xcs30OB9SZ77tfXdUcQX4iZuAunhqjoGEC8Po7Lkb0c+dxCJAK0fPmx8uT84xQdqD/6qoEVt15+OYXXdgsEEWBs7OXYfFl+RCH6DdtTKNNxwTL/lIQBSPlSbMUKHW29aC4QV0325m88Cwyi6g9ydKbAY0Swu/501moGEFvPPOW93e3sFQhyNnSG/xymKxis7uZg/JggRm5sbBk3ZwCkMET6/Xg/G5dHzFOVIfo8nFadKKJdLkbf3yrIsxfvyBHW9/rvR7UWmI/oOGlwQLKcBSKQQjPbkbVKmttiaIcTxc2mJsEcpRVDJwBA0ly9/qWmg5VgJPlBUzleRi0auhbq9CuGodvDKcSkSanTq2Ql4Rbj8pfyMDlke0/8ZToT9CU/tDUvcLxIA9fAHqUxzgJ1AZ6HdkUEOcwCaNIODGYT9i1U2ToDfQ1bbeyLFaMD3wH0yLPIkGru8mR4TmZdRyvYyZ3OEWG2w0akfgva0x1Ht6LsnqipCvDXPZTZiol+9yyibjxo3H6cxX2KC7CNO3/v2A/+dp6QCzvbk5Y04lgi9ORQF1dN/jZGdIRtH4GBXIl9bGkchif+65Z0TBN9ekxR0jtuMT7U2wfX8zOC698xaBOGFy1fT8astPbrI/pSQHy13Una1pWJ7i81JRucBCJ0/v7UHWJjS3HD0Gb104GyWTF+HW+IWiMD9K3wZ14+rkHoSLwhGm4uC6ugjKy5U95wvmEnzEzcLnsWlimQ8R4HJN9HOKavruuPltdCJ+ue04fBGQP1GJxaLIZ+m2n182cMrTMyoqxWm5Z5gEmCdBXWMjyo1hUcw0Sh2nBxNknJ3HtuGQoijU0ew4lYoCW/GS+RGcUfuPjvIpJQkz9Hs0aICKPLo9tKv7p/NXSY47/jfgTIXI0XI4eawab7/dfQlcniRGax3pidHEwEG+w2Y/XQir0IegzkA3VZKnRDhoFF2w3RrPXiQKwzA40bQVt2peBrPzUwwmhkSlIZJGL9DJrsqUWplHdyzGdBwfuRsXtp2b1uPUtdBMEkHgUVH08z8hdOVUathPVGqPpCYqmbRqnNv2GM6pvxuMt67X25Ogv507d6DMxuC0nVfB+ey0ASUsEUGDiEoeyQ7MSKH0vbGRHYfDog/i982np/X8GrMTjX4e7rCAcFhGCVw8jLGqekzXVkNvMCd9whNidF92efu21lvvWideruDGYUx+cq//58bGgtNwyusRLK6TfzouiuzCK9q78I9ceQ6PbDNu3ARsqW4Tu9yVBrcccGwwSAsWxA2SKadSQgQmlKi7z6OxGTQ4p5Ae8xZXxVBQUCiWVVVW7sLixd+Jf587bRo0iGMh9xN2fPe/tF9XW0s9LCwV1vQ5yZUlzZ4yHj9upqWj6paNaWUq5WnoMWdXOLsttAutetywaDQuPmwyPuTn0j8ufyyp+6rclVCDg1cwoLKuGRUVyopKemcZrohdg7uE88Qyxz17KpETo91VSZe5vmIGW4XmN26H3lcr+75+dyv+tdGKV6udqBg5EYMZsghHaAnR427E29Rl3tv+1LaHUMHSUjm1Y/AtXuo1KnhIi2UygYyn3gVMDjnBSsyP/4ijDNvFkG6libJUSDLr1Si304UHv77v9vGswaoQHn+GePWi6VqsW7dGHMfvD1lMeeqnNvx3PTB9Em2Ila67uyVIxwBMyJWx0jfCGDPdRqtV2XVm3jquGRepPhSDyAcTQ6LSEElzT+Q0/MV3Gmr18gNpe8KUk4vNQoVYL50O9TW78Bf1C7hEeBtmE13BHUwQS6lGb0JLgK6cnxS5E3dvHy7aTVPh/BEtuO8oPbR+2vWhJ7Zv3yZ285lYQp+rJqRGo49OkgYCpDyWDwfglto1I5RcvlcwSldbVOh9MNoTb3jGYkbw3zi/5XeynEpcp7BWg4w8ESZOv5s2b3YGhd2VDo6THDb15slQp9gWfaDzlceJ8uvfR7Njkuz76nnqIgwy/VuQGz9+PJZvrkFcYOFEO0JtncomBAFMlIqb3oiQUVEpJk0ay1Xd79/EiTQmurEjT+mQQw7F6aef0eFWIuiHTcOSkgvF69O23APe3fsxsif8Tbvp6xNYWK3JLdpMmzYdO9rosZ5170nZqWTXAzqpfFKT0zcNLo6fUo5ng4eI1601n4MJ9J5vw7ZuES+3C6WIuWoVL3+z6unkkpTAMFqDKCrmCPR4O3xYCfoKk4ke55PNOuxMS7sP171dg8vebk+6XPrn7lRqaqULSNqIC08v7b2cqzUsoJSh2ydvG3yiEsEnnW+0QnbGeKwUR0C6VGai/O1L3TG4MXYRlrY7oNabxHFg1DQ4mv00lP1SvFw0Qo1yG4O33nr9gNs0NTXir9+EcMn7AZSNnpz2c+blFYgh+QQh0AJ3UFrtUZCamipoVcBr8YX4mjsIuaXjkU3UDtoBTuXJkv23nzAkKg2RNO/F5+IFzalQWZQNr3Na6eCGtFVPJ/jU37ATF6o/wYW6LzGYV98a/fQzzGH8CARDKTmVCGGBZkrwod5dO5s300nYrIl0kFXN5SKHeIkHkMtLdCqBbovxQJKh8VwMcXcjNHH5AdudCcd5qK35UJlyZDmVwkE6yfELeuTI+J6v0b6DF5nbkedagr5i7erl+GvdXLweX4jScYMvAy2B2UDzHKIkNVYmVp7umxF1dh0mqTiVAn4/NrbRibqtfUPH//iIH4zYg4kEshJnZebeS1wtrbD34ERkffVQ+WoR54EltRzmzJmH8867SPzfp59+JGaqECqOvwHrMRpmBMF8clXKYdmEsKu6o1w535pcSc3w4SPRxFJXU6RlZ0rPS0q6Sqx0GNgmmJFrVS7oWg4kQNXZvhs/VsexO2oHG+599VrlokHq29oYxD3Nipe/EUcG0ZWsCEBrNKO2tkbsBNe5M1xfMNroRsufzbi79GvZ921ubtonLHcwkxCVPt0WxJYRF2MxPwUvrqjB1qbu3btk4cwHPcokUYmz/ny7g/VEgJHK9Vn5Jdup4HPTz7strstI+VvhhMPxyEebsH7rbvyjchQW8k9i1+hLMRhYH7TjB24iyJre2VO1oqi0/zwsUUpWXFwCtTq9/FCCWP4miUqamBcr9iQXki/XqRTX2XAXfy4ujF2PirzsdH5LwOXQRY4hUWmIQYG6bgnw0XXQ7vgg6ftwKtqlKM+m7Kp4vtWI36vewy3OrxH0ym+rnUAboIP99tjAETMyMVBayU7G99xksasUH/annFHSpqXioScSS7rz2/hS6jZrVeWLg/KBghjkL/Boj0utZYPJiUq/4j/Dh7gaf2RfS+v5E58Vo9YhFEreqRQN0QFwQNDDak5+5XlKYDkO0e1ASVx+CYVSfPHtYryiPw3Xxy/FtNLBmadEOJ7/Gtt05+D+ip47knVFQdNS8dJg6ttsrN4oKRkmlpAt2U3dFZqmNR3/CwXoKjRxMWlMNrBs5oYlcQ0VrNiYv8sFjFZ/BB9X8dh65Mu48jMO/igwe/ZcMWiTiEtkUvnii7TczaDTofaQB8CpDMhvWwHDOupiSoVoOy2383H0+JMM5HOKjzlGvG6KtogZQ6k4lUosVChpEhzIMyf//EpzyNy5OPalIE790ALO2fuqsrZ9m3i5bOUG8dittKhE+Fz9J6zXX4zZZQZxe9nmN6HFOBphQ9+VxuzgC5BrZGGWmkbKYdue3Rg7awFyK7K7at+fy9/eWueG89jboB91hNgN7o7PtiPeTU5aS0szVP5GFDJ0fMDZyjEYCUvHUbNKeYdJV9Q10rF90DkhI+VvC8fkw7/uM8TbauH+9lk8/YsynDSl79yI2WRuhR3PqU7HFdGr8KRrOvbs2Y3Vq1fuc5vdlVsx69eXI//QMxV5TiJqt4UE/Mb7J8wMPwIps1tRiBCmyaWib7FNn/X5yI443U4Zb21K5+aBypCoNEhp3vI9sOIptK99J6nbCxEfjtBuFlse5+Uou5LstFnxJ/UbuMLyLYKu1Ce5lhAdmIdktIT/uUHEkT9XzcMuoRhnqr/GaK0rZadSELTOHLFg0p3fhtnogdunG1jtihMh0e0xugojhJMTlfRRFw4qVKHAmF5Yr51z4XXt/+G9Ue8gFEq+JC0miUp+QSurnCEidTvhQ9kJ2uyKxavWQW0rEF0qk4r7xiHRH9AbTGL5kVUTRzQqb/BhkMrfVJb+vb+RHC8S1r2sljtAVApEeXzGzcDX8SmwpXisShbBQPdzn6CGJ3TgeWLJnnb85bNKXLGYxePLA2Lr7DFjxor/O+88Wu72wgvPIRajk6mZU6cjeMjt4nXT0nuhak/NMcT7qIMkyMgr23bkFcMrGFEnOMGmkE1BMqwSTqVGwY58S9+JSkS0IyLe1roW/LCdTiJ7Ilq6EO1Fh+Gn6ii0Wi0KC5UXeoIsXUDLlT6XOy03Y2bb/+GHGN0m+oK4jq64m6UMLjmMaf8CW49bh99PVraD70B2KhGhmHSJ+vORI2HTq1HdHsKWpq5LC4lLMb+ZdkjktRYI0ncx2Ihr6fu2ajjZ3WpTIREITsrfMuFUssGHI4erMKWAFbuElpYOHgcaCcl2jj8MH/FzULzwt+Lf9g/szm1ejGXjX8Kd5cp0ByXfIVnS2UpEetjhjaQXH9GdU2lcWT55dIzMzX6pb0CTi6CgAwtedD4PFoZEpUHKj2Fa72lrXZVUoHLMVYn/6h7AY9p/ocCh7Kq4xaCBF3QwHWpLrgNHV/B+aqEMSQPBwTpQ4gLtOEq1Cr9S/QBjsB5Wa2oiYBh0IK3iIkk7lRKBr2FjyYDLozIajWiup5MZXaz7IN/OMFLZGyeJNKmiUzGYxW7DTEMdwqHky9/iIfo6AzwRlZLf7mNStxNE+i6oe4Z2F0Y0fIaxOawYDD9Y0VpoRoRVzYldpuRgYqirTWvv//sbcfusaJXEzMZ1EOJUQHOrnLg09idc6Lk4o53fCA260RjteQynRO+EN3KgqLSmlpbFmYI0AJu4lBLOqRNO+IWY59HY2IDPPvuk4z7hib9DpOIouCZeDI8utRyOn3z5uN17Cl5jjpN1v/FlhTgo8iQWhv+JuKkwpaDuz3cDl0WvwVPcCSjoQ6cSEYXK55yA4oufxH8+XwXd5ld7vP13tlPwlO4SrHbpUVZWDpVK+ZVoQSpzs2si+3Tp7MvSbrWB7iMGNi57BdzG08USP9u/nY3ZQKfTie5Jgqu1FUV1n+CTomfwytnTMbm46zFTfX29OFz+st6MaPkRRC3HoMTgFLs1qtQa+HzJjZXSQSuNx1xBPiNOJWHbB/jyHBPuOc6OzVeYYf/4vLTKmQcaJ0ykVQk+awUYrRHvvPMW4vG950d1mGZftjHKiKj5+fT5Ij56PPKEM5GpVI1HZlZhjf4ynKTKfsxDsU2PKoG+T76tEoOFIVFpkPLRks2ICirkcC6wSaioYWnnJ6ui+U5lS1XMOjW8UkCyv50O5lNBkMKVY33Y7revcTgc4AJuMQeC0NrqgtFoSqvNqooP95rLkchqcPB0tZy3DsNAg7iVln71OfYU/xLMpOS6ubmi9BDq1ae3ekbCIcVLRkA0nHwAK8OyaIhb0RSW51RyG2knDE8XE+ts4G/ciccPacWXFc/hsVP7btW/P6A20uOVTR2D292evL271Yv8EiomGZzZ7WySCsSptLUpilfjC3Fn9Ew0SSHxgShdpeSjwYyGdBMsNgdCYSoQ+LoYyO6qrcUd6mcxrfFN8fdZs+buMwn97W/P2SewW4Rh8Ezx3zB/9SF4ZS3tDiaX9V4z/qf9NT5XLZB1v0kjSsHxAgRWjRZi85EBKeci5W/1URPWGOZhvWYqbIa+FXdnVjihjbTjbVwL6zfXQd24utvbPrJ4Nx7dChgqDspI6RthuLR/WcN0XMIm9tU+FJX05r37SCLgPllyGSqa8nplO/gOdLeSr2kXLF9fh+KGzzCqZa9gvD919XXY7jfg342z4Dv6EQxWNNPPwbDvT8B5n7Bob0/+nJUqhjjdbl2BGPLylBeVNrmogDS52IhRNg7B2rVkcIXBwrh8M0Y6dLiEfQ9br7SCCbZ2dDolmKTsxoA6vWZK+4tKR7MrcJv6eTjdtAuwUng8btF9ONZAX7ehYAyyTY5BgxqGLvQEm1NzMA9EBs9eM8Q+5OpYbBRokFisqncV1++mooGH0ym+mmzUquCLUrdU1Jd6YFtURVdZm7T9f4KVyUHSMdbdsDLU7RJTm1POKImyVFTSIZJU6RsZ2K8xzMVX3DSw9oHXFYWEdW9q4bE85xREJvwmuTsJVJSJqtKz16p1e+8fk8K3kyF/8rEovqsWR/9zrSynUlAKdmaTcKFlgrYt34qXlR4WZvvgDo1VG6RSAlVUllNpV30L/qf6Ff7uPx7ashno75Cwbj4exY3Nx+MF7mhsbKGijj9MxBBBDMrPtFOJCMckZ46wv1OpxR9BsW89zlF/gd+ZfupwKnXmnHPOF0v5vv/+G+zatbf1MjnGRuI81tentmrvlQQui06e22bUiBFiSDWhpk1eSVMgEBA7TfJBN149awq+vmKe+N76kvmzZ8G1ZRm+5A8WfzdseLbL25GcimhblbjdRF3VGROVBD3dN0lAd4lNg82WK/C19k+wyfyelIQ0ZPAJhl4D57sSEQsYenzR2gZBu/QkcDrpJLnRxyMw42rxuumnu0Sxbm2tB59soePeBDsb3Si75jVsH/vbtJrKDHQWjs6DZttXiNRuEifwmeZq/d2YH34Ii2uQEVEJWjr+KtXQ/akBGXiOfgw57h87oQhHq1ZhjDmIs6dq9ukC52TpOTNmUKbMPvEdHm6qxAXqT1Hgp508lcxTyjUyyNVEIIDBwVPo+STbn+lzxgsxK/wI1hYkt0j9c2BIVBqkLJg9A8sDdGAR2t27qMT7qf3RExbEUiElYRkGkSAVQZig/FyIBDaWDqo5aSA4WEUltlMnMl6TekbJt+pD8bvoTXipfVJSnd8mTJiEdWOvw1PFd6OgWNn2zlkL6yarYe3tCEutx3tDAzoZ5Jj0VvgtBr0YVNy5pC0ZgsG9E0k5TqUoSyclOiQfCq4kkWoaBFkVHrxZSgmsNrpaTtyFbnfyjQpqWtrEHIRH3PPB59By5v4uKhF8VbTz26ZG6rIoqnoH23Xn4BHHaxl3KlktVjyY8yqe1tyHiM91QOnbTJZ2FPu2Miye56ZOPWif25Ayq0WLjhavP/fcf/f+3W7AKKYWw9sXJ5VBtz8j1M1iXmGxXl4ZQFFRMebGl+Mt7e0o/eFPsu5LXEqEc6cZ4Gj8VpZAkSnmzp0H//rP8WycBpDrdn4IJnDgQhOz/D/4SnUlrle9inh7A8rLM3O+SXR5cxgYFNhNMDNhWJiQWLLfVzgspo64ADlOJU8whHzJqeQoGnjn50w6lci+EDroYsRtw6EKNiPw9b24+LV1uPvzHajz7B1P1bQHYYUfRrXQ5wJsX0Py5ghyS7ZToTHEoA55iEbjshbPkoWVRKUEXqlJzWDi2PH5eBuHi9cvmqbFRx99gFAoRMVoDT2n6RRyRCdysZoCdJytjabeoKm70rcJeXQ8zVvLOioBsg1rL0cz7Gjw9s3ibV8wJCoNUmbOnIUfG2n7EGPTil5vb4nRnd7dUJ2Rk2lEKrXik+y61RXXe3+DYyP3ot4xH4O5/G1X295acIM59VB1j74MP/KTURnpeaK3Zctm8XLChIk4Z1YpHj5tMuYNV8Ymm02Ii8F++AV4frcKH37zNcD3LixpJVGJZ9PLIhmdb0GUp/tVXEb5G3EbEEieCCnPSRZeZRBFLKGPcgNYN60x35izcFCv+BKKpfbeVlL+Jk30k6HeRcVHLT8wOouQ1UmSSRRr2IaxTDVsVR+Lf+fDHmgZEvjKwWbL7IJAjt2OY83bsUi1BnEfXShJsLbOK+aaERZXc5g27eAu96lEYPdrr70kDroJhRY9XtLejXuid0Ploo8hhxtLVuBt3V8xVyvPJk/2eyMfwsHsDji89DgsJ0+J8MBRWtg+uwysv/dw7EwzfPhI2OJurG5msZIfA4aPwbDpxQNuJ7TQle1dRJTmuYw5lda66BC5aPho5OZQIcfLWMSFsL4i12bGGn40foyNQTSe/LGzrX4nWEZAUNAid1j2y0H6s6hEAuuh0sG/4A7x97LKF3FyoRvhOC8KS4lzVHNIwBvaO7BE93uo65djsBIJuPHoESF8d5EDHhkLIaniC9OxWI5Rm5H5B6vfV6gKGVLLxhvIFFr1uOSCayCoDRifx2KKPYTPP/8ELc3NKNTQcWZOsTLHDbPZLIqDTW10rDs1R9lMpZqaqg5RKW4fhb6ixEbntXWZaG/XTxkSlQbxyXS7l7oVuGiAzGR7vD0XaN0nvFlpIlL5EBdKfbW0RZWPrUIZ7AWDu/xt1YYduKZyDk5beRAsltRFpWKLBuHaTR3lFb05lWaPKQATyvwAI5Plb0IshB91V+Gy7ecDSbjmfLweLYKtw/mTDlGellTwMsKzG75/AitvmoRrzjhE1mBrtWYGRkVewNXus9AX5PE0o6RKVTboV3wFnRWb/Dl4b2sMfk/yolK7ux3z2I2YpO17MUCOW8nSvhWf6W7Etd6/i8f7UiMtQ2trbcm8U8lqgztOF1PiUgZfgi01TZjE7BavL66KH1D6luCII44SHUtkhf69994W/0a6piVCOSMu+hjJQronOdV00KmxyF8hj/jpgD+fb5YVLkvcGVoV4DTQCfOnNX0/HCTHArEL3LrP8b84dYTpiajUOZBaEGD20NLDDVKEVaZEpXbjCHzIzcbKSBlyrXTs42eV7X4rlwK7FRfvORq/3HY82nXJj3UC9VR03BmxI79gqPxtf6cSIVZ+OCLDjwEjcPib7gXo1AyWV7vx/kZ6vvIKWpQxzdAhBt40eMu2N7VEcViuC4eWxBGSojEyBRlTPmt6FFfW3wKnMTMOQZVuX1Epbu7/jS8ygc6Ug/CoX4jXL5pOS+Ca9myCnqWiniW/XNFFpsZWOt+rMMh39/bmVJo6nrq3N8b67lhXZNVDo2IQiSvf3a6/0vejiCH6jOElRZgZfhTHBu6EIOURdQcvDcAjTPqT5674l+pcnBC5G6/6U6t9FduaSrk0ZQW0m9JgHiQ99MLneOuj72FJo0X3L4aFcPLWW3FU4KNub0Pa8W7dSleND49/idz/ToF+4wsYiJDyNy4c7CgtwH6Tzq64IXQuZkYew3rVlLSf38/r0BLgEY8kX5JmCFTjYG01ii3yhBmzgaygMIhKJXdZhYuiTEMt80GTcoOUAYtKi/tdR+GU1yNodifvUjME6/Cy9m48k/cyBgokrLu+rgE1fK7ommjbtVzMPSC0NTVmXFQij98eoiJKmX7f/ezRQ2LQMBzqAypUeQTMmTO3W3cQyVYiPPfc0+KlTs2ikaV5E5EWeZ1eSCaJU0Nfi94hfzJjt+chxglQIw7Wn3yjC9JgIXHciAgatHB9UyKwP+RzD2z6Bp/Ep6NJsIvlSLpde89BbKARes4rOi3XVXsyKir5i+bhD7Gr8XRgAewCdQaabH2bt1KUY4Tnrb+g5a074PcnvwBhNDnwj/U2vLKJEx3NQ5BMpX1FJYL/kNvF8bC1aQn+bwr9fP/1XSWafWHY9CwMTBQ8aRQ+SIUHgtVsQkCgc4aYJ/XmOslA9vc50R9wdsEu5CvceTqBWr/vOJm1DbxGM0oRlvJEz5ikxfLFX2Dzls3467cRvFKVh4klys2tSFh3S5Cei1mFF6NJptJ4qRdBnaoMfcXpBxXjh6sPwXVH9J1bKtsMiUqDmGPmTMaa/1wJ/Xf/6tUt8FZgGv7iOw3fGY/MyGtpVeVjk1CBhlBq2TSNzc24XfcyrlC9i+GFynanG0jsP1i0WlNfVc3lm/HEiQZcOrH7Fvd79lSKYa8GgwEWDxWXbhugrnAxxDcSgFswJy0qcdIhlITNp0MoxuGY2H04yPcv7Agl/52p4tSlEOLl7TdWAx0QxpH9wFlV+05oGB4ewYiCktFZf/7+yE/5J6D8+vfR5EveJq2K0Qm1F/1DDEiGsWPHi26a9VFaXsA0rOrIhfFEhCwEdefA1UY/t0m2fS33zrbVHXlK5Hw4Y8asbh/nzDPPhkajwerVq7Bu3Rr6+vV0IiK0y3MqtbU0wqaiThybU37ZRfnwUdjjpg4llXdP8s/b1oYSCz1+NQp2FFiVzUpMFeJU4sM+uN79O7QHnw+B1UDlre74v8pFc6/2CIXwN9eJLcZJOUUmsOrpcZXVW2Bn6HZaUtT3pTGJXBm/P3kRui6oxQ3v1OCtbepB7w7tzqmUyGDxL/g/uE95HQsXHocJhRb4Ixz+9skWVNjp+TJMAotVfZer1dfY9Gq0gwoxnD+1jpfJwobpOMwVFMR9PROorMW4M/Y78ToZl+gcfSdE9DUP7XBiJ18MkwYgjXkfevJZ/N93EbzpnQatWjnJgOQqtQSoqBTzNSPO8Yo6lV4LzxGz+VTDsh/SnYB8Xn1ZKt0XDIlKg5gFh8wHF2gXB8VhKRuiO5ZHy/CC5lRsYTIzCTRq6Kbol9pLy6W+eqfYReAa9VuwWTI7MenvJVydSaf8TWOmj2VU893m3mzeTDu/HTltJHQRFyKCGp6ciRioghzpDOVOTNKTEJWYeBhc0AOjNr0BpoZlEFBbockphDcQSv5+HLUNR2WKSsX6CB5n/4FHrZ1ao2cJF2/B2Z7LcXvsPMwZP+RUIiQGHm2+5G3gJqnNcpDJzIQ6k2HdK9btEi9HRLch6KOuNR+ny3imEpmMt0sGpch+K+wqj1T6Vh0Xmw70JHAR6/5JJ50sXn/uOboPGfJGipe2SJ2s1+RtpM8b54HZ4+QHKJOQ6j1R+rlxrkpZmUolVrrdNcKBPHNmStvlQj57ct5y71iB9dpZcJ2zDEGpMxdBLYlKXl0xIrWbM+ZSIlh1GjDgkWNg4TTQz0qQwrv7klvns2j5sxlF27vujtfdRItASjeHoDiduXszlToRnngWYiXzoGYZ/OWYMeLl0hofRlppqa5gG9yfoVWv6Vh8SyeyIhlCUkOFtpg2Y6KSw5EH4/w/oCL8Mo7WvgDTsPSd5wOVg8ty8D/uaLzDzcfqRmDnTlpqXFqq7Dafn5+PZklUivpa4Aoql6tEjnUf6k/A/8XPRW55z42GhlCWIVFpEDNy5EhUFOfh7V+pkPf8zB5zlchKTWfxR2nGaFrwe9V7OFpPOwPJhXPXiJftcR0JZsBghTiGjEaTIqJSVYSWgdlsFkQiXTsoNm2ieUrHT6YrfhuF4ZhUOjDbsdJ24wF4ZDiVnsEdeNl3MaYa0lutU6tYsKAn2Oq65O3kWp4KUBHIE7UmFRhxrHYtjtBtEzNKssmaXQ1YrFuAd7h5mDhM8igPcp7SPoBtunMwWZ28y6Wkmba91+wXMtqfGTdunHj5+Uaa06ZpWoP6FrrvhB2jM17+xrIs/BwVYMPuvVlx//hqJ/5pvg7XNP0ar26MdVv61pnzzrtYvHz77TfEErYjZ80Qf7eFa2W9prCLnrs8MRXAyD+/jhgxAnX26eL1YDMV65KBuDMSTiVSZpZvpllTfQ0pL5w1a7Z4/YdV68Eb8+AJ7Z1wqNuoqOSO6sD5WjMqKuWoQtipOxubc2+AizOhli1DwNj3LgbNqDnINbII+pIsGxEENDdsx8gZC1BUNtT5bf/yt0RofVeM0Xtw0yw9Dre2oaDpB/FvrD1z29xAgJT7egQ6zmRiezvQZoLGZjoe8uWM7mhFrzQ2gwZnzyzFimsPxYeXzMYwuxSBMAiZWWbHJ/oT8cfYFdhiOhijRw3HrF9fDpednruVLH/b1c7jhOAdOCpyH7xhZUQlci4OQAtWZxTF4LKczES2DNE1Q6LSIIZYoEfNPgYzhudAH3dD3by+29uO5XdgBrMV+frMdIsapXXhes1rOMVKnS9y0fnoQN4ToitJg5nOJXDpZCqpjHTAZWZjHV3Guuv8NkvKwlvFj8FBJbYBm6nER4hTKXlRabzFjwXlahi0qZVtduY6zRt4Vfs3DI8n3z1Ky1MhOAZ5E0K9lAuiImeAePLOKCVYsYM6OTShdhg02S+/64+oWQE6Jg4Vn3z5mzZOM15gHDgZcsSJVFxcgjWNHHhGBTbUCq9gwk/cBNT4aJB2pgmbaPnSsio6kQxGOby9rh5P/lSFL1dthzuMbkO6OzN79hyMHz9B7AD3+uuvgLPRiaYq0Nhr44vORN303OXjUhN1yAryTr8eNXweGv1xWZlKwxy05K1JcCDX1D9EpUQJHOGrtTvx6+dW4s/vb4aqbbsY2hsefQqC03+PHxups6qiInMTfHMn1/M71jNxSPBePBs+FH2Nj5HO61LpaG/wgRbcbXgO249fj+KSwdvIJJnyt85od34Ix0sLcXLVnXj9rstQJJXIcoPcqUTwMVRUUvPKhizvjyA1TGmPqjPmVCKom9ZAU/ND1sdD/Q0Vy+DY8TSEvmzBabh1HrBs/Es4glmq6POQ8jdSmLIj6kQLcuANKzN3q66uwoQRRZjG7MB4uyAu2A6RPYY+7UHOwVOnYCUrlSvVLuv2drfpX8Gbujsw1tD7RDsVWD0dvFnY1Npjxz20A5Kf7x+5EP1hoJSuqKQ305IKExuF3+fpsfNbhYa6DdYxYzGuYOA4J/YvHeQDbrT7pEFFiJbl9ISWlbJMFHCLjGXrMIfdAn20lQbPJ4EBdPLKsfImhEZrLjheCklMcmKiFHNaX8Nh1Y+jnJNXJvRzJszSAboOyYtKBp4KvWrrwOrkRMK6yfhxl0CDbl/2T8NvY7fi+1om404lwtPVwzE2/Bz+Hj9T/H1DvRecABSYNdiyijoRkhGVyKLMuede2FECx+ty4J9zE5oO+7es17PdrcZt3l/iaeZUxFLIldBqtXihdTwWRB/Ci/Fjkr4fmUg/s9OOS6PX4GvtYf1q8D17NhWVNi7/FtXtIZzQ+B84XjkCho3/w0r1dLxoOh+f1mg6yv8yBSlr9rH0HJorZU6RPJm+JijQ4706nlymklfq/FYj5GJ0ed87rfrbWIl0cozHD5zUcrkTIHAx5LavwhHDomgzjkKo4ljE8yZjsBNgLWLcgcBntrMVIy3utUVVohCRKexvnoSc938D45fXYrBz/AT6OY/M1+Gc4VJnRBNtRKGkU4lA8vMIyolK1bhkCo93dLfjKvYtRR5ziOTpP6OIIfqEI+bOwPIgHdzHqpZ0ezsLQyfarCEzg36NibprLKrkJ1WdiUthgSF2YAoamXIqpbPyrzftLZ0Lew+0h5POM1VVe2DTAY4oFQiCedOh6UeTE9nd3wLteP/znxA46FKgjJZgdEc8zkEvGW04TeriXcfjqahNV6cS0NjYkNR9woIGPl4HgZWXh6IxmOAX6CQp6E2+jX26MMEW/Er3A/47+jscnju4VwQ7E1PT7cfAJmcBdwUicJbRDB91Tt8HB6eSq/Sf4NE4N3oDvudpfoUQCYpB2plGo9UiAi2C0hh2dZ0H92sex6P8nTi4kGTOVKAoyTDm008/Qyw33rFjO9748gdMXzwFJ/5QTNoJJf16troZPK89HS/j2JSPnTYVndjtbpHca0lASn52BYxYqZmFkH08+hPTpk2HTqdDS/VOTM3XYR1Pt3XSWfSrLfW4+4sdqNPScUsmy9+IcGiWXJ2Ty6kjMMfQ9wHNic5biUy93gg3UvfrzqgTZWVDolICcrxJhJa3tx+4YOqCHU9tpJ/1oydZcfJf34P/hKcRK1uIwc7yootQ8NUxeHh1Zsd7bIQu7rWFmIw6lRI01tAMocHM6DwzxuQa8KT2nx1/09iVdeclShl/xX6L29TPQ+WijX7SpaamCuNMVKjiHIOn61p/YWDO/oZQjMmTp2JpEx0kmVtXiZ15DoCPw8xSsUcvhTcrTUkhVa2tknglF3eMui6adEO25M6iktmcuthh0BsQE6hqEvIcmBm0dSs9CThyC/BC3rV4PH4ShpcN3KwBkqlEeGdLDI2TrwRGLerx9uFoBCqGbndaY+rZVQnikjBEOsnt3p1c4O7j3mNh/VsLwoK8unG7zQq/JMB6XE3IFupWumK+w8Vj2HBla/QHMpyWbj9GFddtKH5nvlq7A29pT8S9nmOgG9n35ThynUqEb2vU+I6fCh+k/IpYqKOrVSbJMdL9LCqwiPMC1ta04wh2NQ7m14kTTFLWliwks+7002kL5o/eeglRTkCrPyrLcdTioY4zPZP6in+RhZ7DG/0xWaJSpG4LHjoqH0/95iD0J4igNH06zagqCVfjE34WWmCHKtiMRVX3wQ4v2vfQY0lFRWYzggQ9FTofxj34QXcVCoW9WVx9RRhUtNRJmXq9wbduFy+3B0yiaDoERa1WdwjZ+5fAkRzJc8/9La59rwENfgal5jjyd7zUR6+0//GLSfnw/PgKPLV028oUr+deiXnhf+PZ6uKsiEptiUzNQc5xE4vwJrd3bKF1lGbEqfQLwzqxyZLevUOxkO4xOrovzz44+XP5EMowJCoNckhb5JaYWVz5MvIBMbdgfxItnwnWnMzkd0wdRQeGepWAsF9+NwmTQG3gEWnFfzDTufzNak1d7DBq1bgsdg3Oit6E5hDbbee38tETYZx+NrZP+BPmVfR9Z5x0JjKJkPNmV5tYHsYEmoFu7N3R8N5VYr0p/e1OraOTa5OKQ2VlcoG7gQDd7k0meW3ltRoNAjydXPu7EAwzBdNCt5n1zSRgmLoPhgDMNrrPWnhvx3faEx8v34Sv+IPxomc62OJpGEiQHCJC4+alUIETA8pX6S5FiZnJSqvzCfY4HtA8ijvUz6ItEEWocSscjB8hjsXqBi6p0rfOnHceLYH74oM3UMh6cBi7BoFd3yd9fyfXiOnMdhSpUy9DHZ5nxWOaB/GR/iaoG2nuS0+QUh9S8nPlLC3KA6sBLrWy80ySCEtvWvsVrEYDno8dKf5+ZPhz3KB+FeHmPWLpX2FhZss/+U7d3oYxrTB2cvD2FVGGikoGJCcqadvphG2nV7vPotMQXecqkfLzq666DEuW/AhWb0X7rBvFv5uW3w/WS4P1BzuJTp0ejyfpcv1UqPdxqEcuXP5oR7B6JnFph2X8OQYCx43Px+jDL+r4Xa+wqJQoZWwJ03JiJqSMY95VX4kyG52vcPYhp1K2GRKVhsDkYQ6s4enOp65ffsD/mQi11AeiAkYXZeagbrQXgpdW6ANtNB9JDv/wHotjIvdijXEBBjtKZSqRDh9fcdPwAz8ZLV2sgCfylEgL6IWjnLjpqNGYOkBDujuXwBVd+CjOfLsWO1Z/Dftrx8K05O4ubxuNUIcBLzDQK+BUOmQSde4Us66knUqJAPVUHB4+QYe4wCIgtXTPBt49dMK7p/zkjDsMBhJTR1KHpSnU2GUZxv5sa6VZWrNG0NW+gcTo0WNF8ahl2yoczy4TA8qdjA+sOjtB0bkWPX6l+gFHq1ZiWVU7poF2E1taG0ec3xsSnSwTJ07CzJmzRaHmWGYJntXeh5x1TyR9/3PzNuNt3V9xkmYlUmVCWQHsjB95rBdw7+n19m1tbXAaGPz7OD1Kl1yP/kgiV2n50h9x4sRCvMJRUYnQBDtirhqUlZWLHf0yybq2vc0EyLHeZO37jpVhlRkb+QrsjCe3/zsiVAipixizItwOxPGSy7V3UnvXXf+Hd955S3QyPfvsi8g77PfgNXThxvHi/D57rf0Jc6QW75/twDOn5sDrlb8QnCwuPxVOjSpB7AyZKf4cuwQ/chOxvJQuEgx2nCYtZk6ZimvUt+Kc6A2wSyKiUuj1ejGeo6mNzi9HGZWJQ1BL57+Qygah04LAENlhSFQaAosOnoDF3ESxA08bDpwcs1G607vDQsfqhOKwKnijdLATapcvKjUjB9uEMhgUVtMHauC0Ek4lMvjU+xsQrt2MQDDYZec3NQv8pqId6uZ1XZdODsQSOI4KaFx7DdhgM4xrn4Bu6xsH3DYajcIlWNAmmKDTpx8QH8+lDo5phUmWv4Xa8OcRG/DVn6enFBR+ZvsVGBV5ARvYScgWaqlufqvfBINhqNVrAt5aiiX1LLa08nC7exaVKvfsAWfOx3x2Ay6c6Rxw+53RaBQFRSESwJ80e4M0DfrstHFWm6nb1oYAdrYGME9FRaXvdsdEF8fo0WNkP+ZvfvM78XK3j5ahaX3VSd/XpqYuoZgu9QHw5FEV2FYpdVX0VvV6e+LKKLHS862bsWFVfWY7OKXCzJmzRMGIdPOZWwC0woaH46egRbDiE34+hGgoo3lKCXZpx2EbT90LXhhhM/Z9M5CIxomj9pyDm5uP7/3GsRDyeJqJ6GWGJln7k3C/JJxKzz77NB5++EHx+oMP/geHHnoYGQzBffrHiOVNhv/QrheZBhtLdjXjpBFxHDnaILoeM8XN/FP4U8PNqFAdmOupJBOPuhQPl9yPsw/J3niov0NK8b+ITsb3/FTkmZVf9MnPz0d9A41fKNUFFXm9eTnUgV/JDmXH9QVDotIQmDtrFh7aMwKn7jwJm7TTD/g/H2zPuKjU7IvgAvwVxwfvQEtU/qCNU9H7FDsHtlNGCTpbhM3m9Bw0v/a8jJO23AJnaPcBB29S/ja1gMWCwHswv3tmSp2L+htkUsmHqftnV/7RCM68Wrxu+eYGqBv2dRJ4NQU4OPIEDmq5UxGBhAxYebCI8UD1nt7L3+KBdkxT78YsQy0sZvmiEsmTARh4AlkKzOaiyI3R9uk1kaHcgs5Ey4/ABYuLcNs3kV4H6C9++CUm6xrxkvYezF11BQYiibDueuzNyMixZaesSG2htnsDE8UpE+w4zkqPbYur45g1a25KTo4pU6aKl1sa6b5kDtd1Wza7/3HUrpaaUxhTzwshIt22Kpr1w7fuSCpPqcRCh391XE6HS7g/QfKqSOYjoXrTSlwyrxwbR12BmZHH4YrR8302RKX1+afi1tgF4vW4LgdaspLSx8ywhdD4wnUw169K6vbPhw7H39fnINc8JOTvj9OZ2yEqffbZJ7jppuvE32+44RacccZvO27H2UfC/etPEJ50Vp+91v6E2kTHmQ515P/buw/wpur1D+Df7J10pXu3lJYpGxEcgKK4UMDrxH3d47rF9Xdfva6rouKe1604UNyiooJMRUahlLbQPZM0Ozn/53d+HRS6Qpsmbd7P8/TJapsDzUnOeX/vQFNTkIJKgoDJzd/i6qzdSFL1z3Swrhw/MgFPzR8NvSr00x3DBfss/OHKafj28kMRbwhsGExv+yrVNLdMIe6H8jf2OsyN5Z/fVSrqHRcKof90JCHHAkUxG99C1Tu3oXrHxgMe3+Ew4g7rArwoOaVPmS/dYW/k64U8bJHmosbCSzt6ix2Y32L4AlfKPkZmzMCUUAyWTKW+lL8xC5L24IUTNYhzdgxylJfvFd/Ap2fwlflfnFlYWdRz2c5gyFTyt5S1WRwe2CdfD1f2sZD43TB9eRGkVp4NwNhdPMNA8LrEfkx99fwmB45RvI7pv05DUXFxjw2bXQ7eg6UZakTpA8/ykAv8IK3JHtj+drBkDTuhgA8WQQtJS2Nqwv22ux7O4+9DwlkP95ip9P2mnThMyntTeVIOBSSD72O8oIA3615rMffLpMpAqE1msV8aM8y/GzJbObx+CX7fE3g/pVZ5efliVs3O8ka4BRlkghdSW0WvyldjFTyopIs5+N5Aer0etT7+Xu/rRVCJnUCntmQqVQoxiNf3/wlDf/ZV+v33X3HxoRmI07VsZxP/v83ICH4JrVEtR7SEv9eaosOj3JT9vRmbref+a1Bo8MkOKW75uBS5aXxiHjmw/O2nn37EJZecL/YHOuusRbjuuvAsCw0XSj3/f9PLPGiqD04WEevnKgVfrFSawmPfi8TAkkmjgDQIZbOsr1KNkx+/uCzV/dKk+1PHePyfZxGqk+f0wxaSQA2+o1ESFK0Tbzav+QHS5soOj+22q/GG4lR8IDlGbOwdDGziFVuVYGoaez8WWfz+uhpcpvoSNyjeR2Y8pXe3HiSxfgB9zaDxSluyxlwdm8hu3cpPao8ZwQNYa/3DcUiqaYgElfiBusXpEU/YLbP+C29sAaSOWhi/uADw8DRdh8sFn9MmBqFUqr6XRNQ0u7DTKoXCGAe73Y6qqo774f7cDr6f2Pwq6PWBNepm5qnX4RnZIzjE9iMGgjdmOGbZH8Qlnn8hP4GCSvtSSKUQ5CpI1fpuM5X27t2D0t27MM3Ps+bcqdMxmDOV/vfLHqz0TcTct+wwmQbm/SMqOkbMumVk1j1wJ0/Db+WA3dP+ORgo9j6blZUNZ2URqqU8UCbrTRlaTSVilDy4u3DGgVnCgWg286wema098N2V2tpapEbzBZgqITooq9D92Vdp9erfxMvzJqfh2YVj4Nr6vXh7IPqyGdUKxEosBzTtDiU21fW3C7X45LBNnQ5X6exki0lLo5KQro6XfvnlJ/Fz96ijZuHhhx+n3lM90BiixR5jjKsx8JYVveKoFy/Ye7MxLrgN+cnAY+VvDUr+d22o7XkRpielpaXYpp+AV33HQpFN/XVDgYJKRDR58lQsnqHEownvQ732qQ6P1TbxgILUG7yMBhYFP1KyDpfJPoW6gfdd6a2Gvbz/jFeQIMpMK3FsqhZbAZg4cXKfD4yq5Hx1yOaXdjr5bVISPzkr1Y5CnE45pMrfmhwtzcmVOjTNfQV+TSwUtX9D9wfvtzBB2Io3Gi/A4trbxaaDfaWW8yaUphh+UtpTXyVva6aSoIRWG3g52STXOsxVrEeBL7gjgVuxcedF0gz86snDIdm0n+4rWqjHH6pLUZh8Fxoa+IF0Z5Yv/xTev1dgspxnDnrSZgzqoNKWbdvwXv0ofLnTG7x+ffsxGqPQ0PJRdsN3DbhXcysOf6lJDAyNGXPIQf/egoKRcBT9AYfAswZlTT03zLZU8dI7MXGqjwGLpoTJ4qVRsABu/h7WbaZSNH/PapDFQaMIXgPcvmjNHGP9+9g2R2kVmJgehb1b/hiw8rdhjk14UPGSeL3ZGB7ThKoFPRISExGt8kHi6r5JcsVf3yA1IwVxeeORkcEHApDO2wWMGjUGL730etAWT4cSo0aFJvDFLHdj3wMCnXHZeElUozwG0bG8bJkMHew8Ze0eF45xPYSz/f/X599XUloCRQzvf5cdOzA9GklHFFQibUGlygLebNRXwlcFWwl1OzFBsh1JCG6jvJNkv+FmxTtIdGwP6OeERr4izE4UJNLwPDgeSGy8/Nq1f+Gjjz7v8++yS/lBg8zvPiColGaUwCy3ixPEFKl9W2UPq0wlJ89U8vray8/8xlQ0HfsCnLknoXniteJ9grUCR2bKMTFZ3i+rmqwRYoGkBJ+M+ha/nK/tOajk5EElm08h/s0D5ZPzn5F6uz8B7S/bq/j/q6e2DHm5OQPynIOFRmuEWWKBXupCc2PX77OfffYJDkuTQSUT4NMlwheVM2gD3+zEzWaztmU9DlSmEnueJjnPsPQ6GlC0mwd/xo+fKI6oP1gFBTxQtqvB3+tMJXst/55Gt1QcVtHXjIst/gz87s1rG67RbU8lE+8d0qwK35M1s9nc1jh9zZrV4iWbNMWm1zFs+luwKXX8dVktROFmywKEA61GA6uUlztKW6bzdiXhjwewbPQPOGZyLmUqdSI7mwcKU1JS8b//vS9mgZGesZKoBoEvZnktNUF5DqeVfxY2+HVIij/4nnMkPLGeSlaLDYVCGsrcenj72Je1prIUpyhXowC7kWwK/UCFSERBJSJiBxsbnTwNUW/dAck+BypjGr7Eh6q7ca6uY7Cpv9kF/iYg8QR2kuus4+Nymzy0utSKrbqz8re+ckv430SJlmayLdiJ4GHp/CTobyETI9MSMRRER0fDa62D3G3Fv45unwJ139eFuHGdAc/E3Iq1lR7YXF74WoI6Tl//vI2OSDSI/YbGKvdgUooMJbu6zyDytZQkNh9kUEloGZEs8w7M5KdDt9yF80vuhPTvzwckw2Aw0eqMYnCWcds6DypVVlbgj42bMDuHv895WOnbIC3RYMGb3Nxh4vU//lgjXg5Y+VtUFE5dOxFjnM9jvX8YGgp51svBlr7tm6nEvL7Jg/LDH4ct77Qef6bC4sPiqtlY4j0ZhdW96I/TjfwUM+a6H8QZrsViwLE7LOvnzq3ZuMR9LUpNExHOpk6d1tZXiSkp4UHAuDhzW2+hYNKZ+MlsFKwwqcJj0SrWqIO1JSNO0l0AUfAj2sEDl0VN0gHLBhxM2JTBDz74FF9/vRKJiVRi1VtGlRyN0MMlKOBsKcXvb56WTKUGn0bc38nQK39r7WHKWF19a8Ye5y3FE8pncL/y1aD0gCI9o6ASEbFMC4VchWJ/AqQQIK9snyoi87SX2QSTXcL7/8h9gZ3kOltSb20tQSnSf7wy/jdRoT1Tye12Y8eOQkxL56+Hdf48jE0xDpkm581bfoTu70/apoCwhtkrd9bh28IaPPlTMS57/08c9fQqyDa9LD7u0fdPQG14vB57ESeu/illEvgrN3f7/RJBEPspWdwS6HSBn1zVqpLFS98ANHqW2GuQuucT3JG3A/q6bf3S2HwoYa81K/hJotve+QH68uWfwXjoP3DcVB7sdKcNzn5KrfLzebNulq00kI262fNYVfGYKt2KjepLcI3inQ79ew7WiBE8qPTTyJsw7esEFPp6fl8oq3fiDfmpeEm2sM8HwWNy0yAIfggyJepZE5Ju1NbW4e8mDVZLx0MdE94lUa0lcKtX86DS7pbMsoEKTGem8FJdpcQHc5AnUPVWnEkPS8v7RXflb1JrOZRwwyXIYfGqqU9QJ9j/yeGHHylmxZHeY1MQb3ddhITvjsavtcE5/vM284zEeo8ScXF8Sh8ZWuVvLPB9Hj7FXfLX4KjrObu3OwlunmDQqKL2CqFCQSXSZkJGHNYJw8Xrigq+esuofDyS7ERwgzZKGT+J13q77xGwvyorH+Nco6bU7v7mk/MDV42k/SSFBZS8Xi8eKi7A6e7bsVxxDNKjNUMmUwk+D5qLeCNkhhXB3X98Pq6ckYVZeXFINqpwoewLZEt5I+3+6nHLAgsZ0Vps9vOTJZO9+/K3mMnnwHBvDRb8d81BZSrZ5XzVWikJ/omSvI73SdtZ70di2uAs2QomuUwKG1r2oS4yNT///BOoUvJxkecm/Jh/H9zpR2EwGz6cB5VaDVSmEut/5qvcjklSXma9u9omTm6bOHFSn34vaxqt1WrhtfHpfZWWjtmdnamtq4VUw8ttTJq+ZZYOy86Gr6VcpKiyrsfyt+a/v8eto324eTbPGAv3TKVNmzaK0/JaM5UGLNtR3v7ZNqF5JcKBOcrYlqnksnb9t5Y17hQvdwuJQ6LnIQkvM6JsaFr1Trcl233xl/lkTHM+ibuqjuABCDLkyt+YM+U/4Hz5V/DVdX/M2x22+Jvi4UGpzOyOxxZk4PS9PoYMGcdOHoVfftqJBbKfgD2/t92vFXhavk8S3IOSFL0CsAM6X2BBJSULQskBmyT4qfCRZpd2PC6rMaO8rhYvtty3ZQvPoEmN0uLChWeg3u4eMiugrKcSs2+zZJZBMDkjWvxqZWnIBf73lni9orYZ/dW+dUyyEXt25wC+zUiR1ogflF3937ITrFbsZDZgSh3gA3SSnk9+RT43pNa98EcFPnFJqOZ9c/6sV4lTssiBXFKtGMGUOA98/6uursZvq1cjdeq/UAMldONmQ9AM7kaUrc26922gPRDY/nS8+i9cLOfZuD+XejFy5GgYDH1bbWeBqeHD81FmqcHRKQ7EF66HJO0CCKquf6/cugcTDTtRKiTA0JIZ2ZcsyyN9v+NR1QrYf8oEcrrpqWevxTVTlBgu7ABwNMK9ND85OQXl5Xuxbt0fbUGlgZj8Jtrn/Vcn71vPj/4SF2Voy2x0WmrR1bqGrJ79fYEiIRmZZuoVRPpXazlldxNL+6LK5kE54mC3FlL52xDU+jet9WiQJwe8toPvzdXU1Ii8aB9714Muo28LROTgUaYSaTNx7Gj83sA/JFTVG8WTSMbUEuRJkHds1tzfpDo+hUO1X/+enrxsnSZOD/hSOitIWxa5nPp0fOmfgk1uXiq17+S3EfnDcUiqCTPzhs6HPTsxaz1I8vu7PoEwRsfis+yHsOQPN97a2X8ZFnfMycMxh/MMlNFxfjGY0JXmZh7s1Wp14gltoNj4ekYt6d1+rf/5LsS+NQPK4q8Dfi7n3r/Ey+KkY8QmzeRA0S1BDXnzgZN0vvzyc8jNmZDIlTCpWUbb4M8MbC1/G+hMJebQtPb+ez+X+DB1Ki+x6ivWV8lnqcFDihdwePEjkDXwTJGuzNRvE/sVLpJ/A3UfJ7CxYJnPJyBB0gi9Y0+X38cC1QnSejxxrBr5pa8h3LF/1759lUpKigc2qATgSe88bPRnY53xGIQDuUyGUq9JzGptbGmW3BlPDe/LV+RPxLDkofM5TcJDrmQ3Pj0nBvMS9wbl91fWW9omTx/UwhkJa2xYB5u4XNXAj2XjZQffV7CkeCdyY/hnqDxpVL9tIwkMBZVIG9bYudalxGveo/GcsBAQWNQXULcEeWQtQZ9gkel5zbRWxp+3t6q9WnF6gKCnJov9LcGohrtmNzxN1R2adJ9/iAJX5JR26L01ZMrf2LQ3vx9NTd1nzNX5jbjyCyfK3IZ+PYGSJPNJemMTZdi9i680d6bxlyX47sZxWDCHjxIPVLM+BwXOlzGj9rZefb8g5+vhulX3Bvxcipbytz/rZBRU6oI7Jh8rd3tRUW/vdOqbKjkfjyiew2LDl5D2MEZ8MGDlS2ygQCiCSnGG9qBSmUVo69vTV2wCHJuEVCrE92oCnBa8dNsq6Z/3EGlLP65Yoa5tUaizYHS8hn/GFrqiUN7kRLhrDSqtXv3bwJe/AXjcexrmue/D2MzwGUjxckUejt69CH9Fz+3yeyR1PKi5o1mPjAGYlEciS3GjHSdmezEiPjiZ6sdb38N1FYsxxrUhKL+fhEcJXMXecvG6Wcr7Kx6MksINkEsBq18Nfw+DKkjwUFCJdDAmVo7r1iVjWZGqrZeAVsoPTpXG4NY0f+0aLvbouazq1IB+zgV+ghBvCryvDOnezHQFZqy+FrMreFPq1kylM8bpMcHzO3b93V4mORSwqVStTa9bx1Z3xenkJ4Rqdf9mjfhMmdjVrMGKIh8qird0+X0xzTswU1uEZNPBlaUa9AY4oIZP2rufd45aJF7KLKVi4+1e87kRZefZBZv2OttGOJOOLIfejiNfs+PjzVZ4PO09zOrq6rBq1U/IyUwRS5MXWl/v8/j5cCCTyZCXl992eyAnU62oS0OVzY/bvucBlf4LKvFMpRI/7xUha+o+qGSU8c9WZ0t/s76KT0hFs1sQh23IrJ1nK9XW1iLFxEvtdrlN0PYxQ2ogg0pr165BWVnpgAeVPv/nFLx0xiHITwifEjLVqudQ+cYN0LcEJjvjm7EYN/wWhRXLv0Y6BZVIP/OreWa3Ud79YICDVdD0I67O2o0Rct6/kgw9rFdWdTPrXApIHd33AuyOs6al1Jd99g6RdhyDEQWVSAfzp+ShfsVTKFz5Eb9DEPCobQ7+0zQblYq0oD631mjG7/4R+NsdWJT5MsNPuEr2EXKN4dHvYCgxSe14d4EWj81ipRU+cRR1dVUFprQkhb1T1V4WN9SylXoKKrlcPINPre7fSWY3f7YNs6Qv4Ow/RmDL7q4PppQtUxKdwsH1Yokx8HRyNi2qN3xR2fDEHwKJ4IN6x6e9fh5+civAImiwa08Vndx0Yem6OqRd+x4M40/okCW3YsVycd87OosfKDVFj4KgDJ+T2/4ogWNZsgNZ3mBVJiLxURse+NktllElJCT2W1CJZSqVCAm9ylSKkrVmAfOTs77KyspBUQP/HJR2EdBi7+FpMTwQXoOYPjcIHwh5ecPF92WHwyEOiWDB/4Ec/x5vUIn97sKJXm/oMD2xM1ZTPh79uhQ7du5CejoNMiH9S6bj1QVRck+37QIOltzDMy8lLc9DhmZQqdbFFzbc1oPvqfRNUxrOc9+IN92z+3HrSKAoqEQ6mDBhImRSKeI8e+D65Qnxvpe9x2KJ6gIoDMEtf4s18kwjv0wlHjj2BusPcZH2R1yv+AAZUeG/4jrYqI28D4NBJYHd3oytW7dgVLwURoUPVkGDmMyxGGpam3WzDJHusBOcYGQqqRVSCBIplEnDUFzc9TQMhcCf3+0/yKCSUYcH5c/jWd1SSBxdB9DYyGrDV5dBWbQcrrx54n2qwo97/TxuYxbGul/GSe77ECdziyeE5EB+AZCqtGKvq8ZGPkGM+eyzZYBEirlq3iBfknkEhorWZt2s9G0gm/3v2xS8v7KUGDaW3CT3obCcN66VNvFSra6C0jFyvg+fd8SYfnn+9IxMlIAHW4T6XV1OfkuJ4vtgszJ+UAxZYD3jpkzh2UoMC0wfTB+5oWRCkhQ7rtLjqOL7u/ye0lKe1cUCcn1tRE/I/jRRPHgeo3TDYun/kmylny+cSY3U2mIol79ZzYeI12uqDr43106XET/6x6FCP7oft44EKrI/lUmnq1+jR4/BLxfokbrpEcgai+CXq8XH4qKCe1BijjLgTNl3uDp+Hax1vMa2J9amOkRJ+QdPUhqV1fS3Wh/PHlDKJGhuqhcnvx2WxoN3G/y5GJvaPhFtqDXr7ipTiWWNrFz5gzjinVGp+jdTqaClxEKZkIOmvV33VFILvHTHI2nvDxOI3IRoHOv5AXNVm8RpUF1R7loB9c7PoFvzGJzDToYgkUFRvRGyxt6Nfy1rcKDZr8AuVzSyzHRi05UZ9m/wh+pSPDP677agErv86acfAcGPaZqWzJOMGRgqWA+iffe5gRIVFZygElOQnYGNX3/UY/lbXW0VYrU8oKM1908pV052DspNPNBvr+68STgLlqcY+fO6tfykcDBoLYEb6NK3cGVPPxS5MVKo3J0vfsirN2HXb+9h8jEnICVv6C3+kNBTGviiY7TchcaG9oWQfuHzQNsyRMQende/v5uEVabSd3tV4rCl/0TdcdC/p1nGj5sLkgaujJ4ciIJK5ACmw8/FnzJelqAo/gYTVaXIklQgMSa4J4QmjQq3yv+H22K+haOm6xXefVkq+ImtXxAQlxDc8rxIJFG1T5ZxNNWI/ZQOy+YZZRuQh1FJxogpf2P/9rvvvgPjxo3AwoUnY9Wqn8X7x4zp3wP2ggQ9UiU12DbyJSybXQLB33njek1LUMknPbiglsFggMXFa9ldlq6nzLWWurlYQElrhidtRkDZSoU1fKKHp3Y3crKzD2pbI4FaLoFZYoFZ7W0LKq1Y8YWYtXnS1GFQuhsgyDXwJPJG7kPB4YcfiUsuuQJ33HHPgD6v0WjqNFjRX4GyonpeCiKzVwOeAxuvM5bKYkhbs4Q0/RNUY2V8f9lM+N1fgCKHvsugUrKWb58wiBqa7juhbyAnv4WrZgX/nFK3ZKzuT1W0HOc7X8Llk1Qwp9FwBNL/VC3VC0qJH7aG/u17JHHxbE+/IIHOFNwqCRI68fHxqLc6xGFL5e6DK4F3OJ24yrgSc6W/Y0L24PlMG4rCv5ieDLiJ2fH4o3Y4pkq3QrPpRXyoqcJWfxo8scuD+rw6lQwWaMHaB7uaqnr1M7Zq3gC4wSUdEs1rw41eo4ZTUEAt8aC5sUac/HbfTPa24Ua1cSw0g6DJa1/K36qqqvDhh+/j/fffwebNf+7zPVGYN28+Fi48HRMnHtz0ta7kxetRhRhxlY61a9qxeyOisicc8H0a8KCSIOOZhIFSq9WoahkQ5bLUorOiNIm9Foo9v4jXnbknipeOkefAGz0Mrpyupw7ta962f8HkLMcV6wRkn/WPg9rWSCBR80AHK5/a0bLq25oN94+jWEp3FTzJkwFZ/2bGhRLrpXTvvQ8O+PO2ZirFxsYiJ6d/M1xHjBiJBqcEd20bjotueABqaeeZhDUNVty0cxziEhKRUliPY/L7PgiDlbJ92ZSNz/Wn4gSnF3d10VPpvJLpyBs5GqnRwzBYjB49Vuy7ZbfbKVNJzFDl7/saiRs2nweQdXydSep5plqRkIzs+IPLZiWkO3qdAS5BDhaittdX9Ovvbi3JZ+cEibEDm8lKBrb8ze/gfeGanL1re7K/3ds34DrlR7ALStTkXN7PW0gCQUElcoDjp47Bq5/yTCG5nQd3LIIW6dHBHfmsV8nF50mR1MG1zwj77jTX8gk3jW4Fhl7OTOixoJENGqjhgbWxFnt3bYXyGCN8ggfKtIkYimJi+AHMkiVLcM8997Q1oFQoFDj66GPFQNLs2cf0e9nbvv/n6bEGbLOk4xBJEayFKw8MKgkCJOBZRpKW8tSDOQF16livgirU1tbA0MVqN2vM7YkfC7suHeyZ3NlzxK9ePYe9Frq9KzHXBNTtsCA7e/FBbWskkGn4+6tR4UFTU6PYo+LHH78HZHLUyoywCyo0xE3tNPhHAg/8sNf/cced0O89hVimUsyxV+K1zDnQViThgozOT+irGpvxqvMIaBWTcbun82zEgxEl94MVRBXXWLvsqbShToUa/2hcETN4GuCy999Zs44Re4yNHz8Jkc63z/u+xGODINuvFL2Ol07vdMVidCpNxiX9L8mkwdRdl2FX4VY8li7ttvfp19tr8Evxdlw2LQNJxp6PWbx2vrDSIOiRlkCZSkO5/M3vtOEK2TKkOiyQOIdBUAdYwlaxQbzYYTcguWV6MwkNKn8jBxiVk4F11lgx7bSVxa+GXBbcrBSTWiGOQw5kCsCeWr6aUadOCeq2RSqZVIJmgQdPSncXoqrRjgnFl2Oq62mMSB96k99asxeYmpoaMaDEMpEeeugx/PVXIV599S0cf/yJQQso7dtXabOfr8YL5esP/AaJBDdWnwbZPRZoFQe/NtCs4AE0u7Xz/lGqltK3xswTcNzS33H78q1wBnACLK/bKl7uahTQ7OHTqUjnZJqotjHzDQ0N+PrrFXC73cidNBOPeRbgCMkrUE44P9SbOSRMmDAJf/65HQ8//Hi//+68vHz4bbzPTXE1L+FAFxlDMo2x7bOvvyQZeNixsblZ7EvSWVCpadXbON+8FwsOGVzv4Y8//hS+++4XTJ48BZFOo1C2fTazYQod+DxQ2crEq9ub5MjIoImbJDhTERNrN8Cy5iM0NnbyXud1osnhwW3Lt+Hpz1fhiZ3H4o+fe1fxUGMchUOdT+F853VIjue9m8gQzVRyWnGB/EucgRWQ2HrXT3dfujo+xGRPQ0vqPQkZylQinVL4PdgmpGOEhDcatbiDPyFGq5TBY7dBTIewdz/OvZXEWS++ipsEik4HywPOhZDJ5agq4v2rErZ9jFce+BpG9dB8+1iw4HRs3LgeI0cWYO7cecjOHvgG8FMzolHyp0l8beutnTfcbW5uFieG6fqwMsPShRmP/cDJLVJrOZQVq8Xrn3qmwOayobjODpVcKjaOVuxZBdWuFbBN/78DSi9aeSv+Ei//shgglzvEqU2kcwYjD/AZJQ4xU+mzz3jpW/7048H+FwtSYgElvc/1F9Z/KBhYiRYbrpYpqcDI8q+gLNoDdyelomw62xRjNCphhlHTf++lOQkm3Om6G5PV29FYmQVPSsdG5EZXOa6dqsQo7cGPbw4V1guLDRIhgF6tgAU66OCC1G0VS5BaySwlkApeMehUVmdHWhq975Lgtgtg7QHYMYOi/Fco9v4G5d7fUaPJwhl1V6LG5sZkSTVUEg9y9rwHYH6Pv7eu2YNC9oo8AAA4DklEQVQKxMLnkIp9dwiG7iKu2446IQGxEivc1lrIA0ygVTYViZf1ssEzeGKoGppnhaTPRiTo8Ic7DyOkPKjkbGmIHWxuCRvP3gi/o3eTJJbZRuNdzXSYPTV4IuhbF5l+8IyCWxoF19Yf20pHEgxDp69LZx9yzz33EuLiDKittbJKswE3pyAeH3/PSxYSUQU324j9ynSam3kDbJ3u4Esbmv08qORz8d+1L6mjBh7zGAhyNd7czk9ZZg83Y+mvJSits2Jp7ZWQOurgzpgJd+asTn+/o4KvIO2Mnoa0tPViDx3SuZxUPjZZL/OifE8Jvv/+O/G2ISkLqAZGJ3VWoEjCUabZhEnSbbjK9xrcW3Z3GlQa4dmE281b8T/vTJjUx/Tbc49IT4SzkO/XksbdwH5BpTxVNe47Qo1K56/99pxk4BnVChQ6UtEgqLH/wHVZQ3s/JW9TNdLS0kOyjWTou3SKDm8PZxmX7wOvv9/hMUVTPWpcLqRHa3H5uHzgV+AwYQMavU6gh7L9PdU825OVRrUGrsjQI5PJEGMyotrqRp4JkDk6n2bZHb27ClAATj312gs1Kn8jnTpqdA7e8s3Gdi9fzfXIDq4rf6A8cn6CLLgsvfr+SpdCnBpgU+1/WEX6i8Zrg7u2BM6mauy9To+7hm/tcqIR6T+ajMnw+ASY5B5IbR2bYLprduLq4aV45eoZUGsPPnvl39a5GOF8GZ8LRx7wmDd+LBpP+wI/j1+C4no7NAopDs+JxatryvDNjgaUJ8/tcQqcqp6Xv/1ZK0N2NpW+dUdQGlAjTcRPJV78+tO3cLlcyMrOwY2W+/Ct8gbMUPPVOBL+8tMTUSrwVVNpE1+Y2Z/Cw7MDa2GEqR+zPscOz8LfWwrF6/KmA6eomhS8GepqW4w4NZUMTjE6Jc6sPAsLys+H18wa+XceVFJ5rH1aeCCkOxWW9gUpn19Ak34Y7OMuRemsl3Ci5CksPCQFb50zHqPGzWArJFD47FDuWdXj782sWIHrKxZjeu1HkErpVHWol8BV7OFlbxpv7xIKWtmdbiTJ+WepO25UULaP9B7tqaRTR08ahU2//YoNJZYOwZ5ge0p+Ds5w34YPLb1Lcbf7+Es4Vn9wzYpJz052fYPpv12DM+KLkWyQIlaoxc7GfZPtSTCkZefhta0KPLlJ3dKSu52nqQJHKLdihqkKhj6cMLihhB1qWFxdT934YAv/wGbTqXLidJiTz/sbvGrjzXJVxV8B7uYDf9DnQYydT2f8c6+dgko9kcqxPP0uHPGqHfU23hvg+BNPwShhB3Kl5UhLpVW4wWLc8ByU+HlQSWbZA/gP3L9aR8HXC0Yx66S/ZKSlY1clb9Ltqd7e4TGPxwOzjmc87vHFQNrPTcrJwBlpVqH8xcugXP/OAY85C07Hl+ar8Z8P1iPBtTck20ciwzLtQtztOQf/2n04kt/Pw8gnqlCc+09o8ufgtfOPwE2zcqFmU4IlUiD/ePFnlOyYoQeJ9b/iqqzdmC7fNgD/ChJKZrMZ1XZ+lMuy3wNRsWeHOJ3aKchhzqKgUqhRUIl0SqtSYJSsAh/XpOM/TbOxVjt9QJ63XJqE3/wjscveuyDR6do1uFr2EQp0nZzUkn4xJ6YE7y3U4j9H87/J7748aJTBbdpOgGWlUtyX+zruqjsGte6O5YYeBw/2NvtV0OsPPlNJ1fIJYNtvlKusbiskbisaHR58V8h7r5wyhmcDnjMpTbx8qSQWTkMmJF4HVMUrDvjdUnsNqiVx4kTHHSWVFFTqAcsa+e8uE9KufRfSlgbOM0aYoZD4UCFNhDI2K9SbSHpp7KgR2NssgUtQQCJ4Ie2k+ahB6hQvXTIjlKxPWT9hJaZNcj5sQKjvmN1WX1+PFCN/73ZqqP/EYKbX83JYm+3AKX+CNg5r67X4bVsVMpOoHw0JHkFnxiu+4/BtxhXQnHw36rVpuOCCs8UhE1HajsFyXx4PKvkLv4Tg62F8fEtfVa+C5jpHQqZSrYtn63pttQH97Ba7CTNdj+CcsvlITc8O0haS3qKgEunS5MlT8bU9D0tUF2CbIn9AnlOr4Cuntm4yJ1o5nU6coVuD6xQfINfoGoCti0x+OetzBeiU/G+zXTECyb0YCUv6Jj+Jj5hXJg5DcXHHk0Ofi59INPsVfSptmKgqwcPypZgrfN9+pyDA+NXliH35EPz523K4fQLyzDqMSODBq9w4HaZnx0CABD8qedmcuvCjA363U5OIGa7HMcn1DNzVu2nyWw9Y1ojDB0hVOvErPT0Ts0w8GGFLOizUm0cCkJGRBce2n1Hi4O+Tsk5K4Ewynql05gQepO1P1qSp4qXKUSnuz/tOfkvR89t+HQWVBjO2mHDLdCW+PrEGmg1LD3i8rKxUvKR+SiSYWrMsrS4fNHIJtHoj1qz5Hbfccj2E/cprPWmHiotMOm8Dqgu77+nW2gLDp6X3qaHObI6HI+so8XpdTWDT3/6usGCXkIzvSiVITe3/z1ISGAoqkS6NnTgV+rFzxOv6AcpMyVQ04WzZNzhKw3tCdOfnZS8iV1EDvyBB7tgjBmT7IlGFtONKp908ARIqmwi6ggS+Em1OSoJj2zcdHvM6eR8Dm69vQaVDZUU4Tb5SbJ7ZSla/DfKGHeLJ6Jjx03H14Vk4b0p6h7/5OZNSxctHKseKl4qynyFpru7wu9mkONZjweF0w2etoUylXnhCsQR/qC7DSXkynHjiyTBV/ybenzBydqg3jQTYfDS1di0K9/DVdtl+vY28Xi9iVLyEWGvu/8lcTbF8v9TCAYmrfdR3fX0dkjUefsOQ3O/PSwaOXKVFysRjkG0SIFjaT8Qkjnp4fngA0WoHDBNOpImbJKhy4ni/1fGpJrx7/iQ8fdPFYg+kN998DS+//HyH71WrNdikmQKHoET57i3d/l6pny8UV0eNCOLWk3DJVPq4KhlHux7G52k3B/Sz2yv455vK1QCNhi+Ak9ChoBLpUkbeaMh0fOqCVs2nyQTbMEUN7lO8gkWxf/b4vZL1L4qX2/wZSEyltMdgsUvby6vsggpxWeNCuj2RgmUEaQQHNkXdiFObnoXE3j4C3N8yrc3qk0OnO/jyN6mKB64UQnumn7qQj7J3ZxyFqKg4sdzt6OG8j1KrcSkmcRrZTl8CyrQj4TNmQGbd0+F7Cmv4NrqrdkGhUNAqUi+YpA6YJU2INSgxf87hkNfxfhLuVMpUGmwKCkaiqMHfaVCpvq4WcVoepNXH939ZY3JcDFb4JuFD1xRIfLw/F9NYU44oBQ8qKU0UVBrMok1G2LR8kIrPyfveMfK6rUje8gwujfoV2vzpSE+nTCUSPOdNTsfbiybg2dPGIMmoxqxZx+COO+4RH7v99lvw0098anCrrQXXYZxrKV51dP+ZZpC0DINR8YxtMnTFx8ej2ubBDiEV1d7AhkKdYH0bl8s+wUg9tUAJBxRUIl3KSYhqu+4eoLpmqZo/p0HWfiDcmV2FmzEnnp9kqw+7akC2LVL5Fe1v8pv8ORidxvt1kOBifVY0UgHFAu9lJK/Z3PaY0BJUsnllfcpUkmr4AZta0rK/CQJUOz8Vr7qGzevy51jW0vlT0nHKmETUHvsyGs76Cd7E8R2+55w/z8CqqHtgWPsCMjIyxV4vpHsuOQ/yTTlkFFIVvLeA1VQAQRMT4i0jBxNUWrpZjWt3TIN9wpUdHmuoq8GNmzLwmGUWXtnMy+D6U35qPC71/AvXu//Zocytpr4Jc3YvwsXu6xAVRa+pwcxoMMDi5aVH3maeEcfIGovaJr95G6uQlkaZSiR4ZFIJcs26Dk3/L7/8KixceDp8Ph8uvvhcFBfvantsdN4wOKHC+rImuL1dDHwRBJhagkoKfVzw/xEk5OVvfgdv6WBxtmTS9oLV4cHZki9xk+JdDIumcEY4oL8C6fbEcUQMf4lcOJOn0webXM8PdI09BJW2fPQgYjQSVLpUMIxbMCDbFrEU7UGLNRgpZtCQgZFplGGzwKd+KfYJKrHm2IzNI+lTUKlOzjOQlHJ+QCiv2gCZpRR+uRaXbUjAl1uruhw7PiMnFouPzkNqUgp7s+jwmMRRB0XDDiQ7t6OipJhK33rJI+dZZ+NGDsMflmi87T0KX4CylAYjb+JI2M96C8s0x0NQ84zfVrUNjXi5bjSeVF7YNvWmPx0yrCWQoNCg0d7+WVpbX491NXL84h6ORBP1xRvMWKmHxc2PzwRHe6aSrGHnPkGlSsoQJSE5d3j00ScxfvwENDQ04Nxzz2hrKM+OH2O0Cji9fmwpq+j8F3jsUEp84lVdFDWaj4TyN7nbIg5dOqniv51OS+2M0lEBvcQJj18CVRwdY4YDCiqRbj1/zjR8cP5EHJo7MM3y0pJ4VoZB5oHf23lgiU2VGO1dL17/SHkSIKVJZMEkU/MstY3+bKxPu0BcmSIDY0xqNDb7eXmMvPavtvs1h98A+T0W/GvpSmi1Bx9Usit5EFcr5atDrVlKhabpWFXmxFtr96LXf22vE7J63gtNXsv7JdR49Wj2gJp095JXwTOVJC4Lvrak4Vbvxdg7/MJQbxY5COOG85Jsr8qA5uaOqfmsYbZUzf/WRnX/Z/DlZWfCa6uHEh7s2rm1w/PWffEEjvOvw/jU9kxkMjhP3C3elteOmzc1ZuQN7ZlKWsFBfUZISKjVarz66v+QkJCIbdu24rLLLobf7xdft/MTa/GV8iaM+uHszn9YocHU+nsw1/UAzGZe4kmGLlb+5nY041r5h5jd/KnYF643jDVrxMu/qrxISeOLryS0wjqoxIIHd999NyZNmoRp06bhsccea5smsGXLFixcuBBjx47F/PnzsXlz+yo+8/nnn2P27Nni41dccYU4SrcV+x2PPPIIpk6dismTJ+Phhx8W3+zIgVRyKTJiAqtx7Yvpo4a1XXc0Vnb6Pd+s+BRbfSloErSwDj9jwLYtYqVMwE2ei/FU4+H4z7xRod6aiDJ7dBZW/1UsXpdUbmq7n52k+thboUwJpfLg+52pdLz8TS91i6tDqqLl4u3XrRPFS1be1tqgmx0YPvHEI7DbW3odtCistuGFDz+C6cWxMC0/X0xdd5bzANjfTp66TplKvaNq6WFnQDP+LOcniqOTaKTyYJSfzk+GzjJtgv+rmyFxNrQ95qzZhWlmG1JQE5SgEgskzBFWYZvqPBSsua7t/iTXDlw7VYnR0TQtdSiwefiCmtzDs0A6ZCr5k2HW0oIbCZ3ExCS8+upbUKlUWLHiC9x5553i/VmZuciV7EWSYweklo69GBk2WbZcnoItQiZSE6j8baiLioqG4HagATxTW+qs69XPKUt+EC+/3Oml3nFhIqyDSvfddx9+/fVXvPTSS3j00Ufx3nvv4d133xVPav75z39i4sSJ+OijjzBu3DhccsklbSc7f/75J2677TZceeWV4vdbLBbceuutbb/3lVdeEYNOTz/9NJ588kl89tln4n0k9NRaA5rdPHDYXNd5auwrb72N25Q3YrLrGUwZlT/AWxh5TKkj8Z7vKJSmn0xT3wbY8JRYFG7aKF5XNu+FxMknXbRmPvSl9I3RtJSbMhKPHY3zP8G2UbfgA8twqOVSzMnnqeeFhdtx8snH4oEH7sFjjz3c4Xf8vrsBb+zWi/0TZJYSyKvWw9USVNqAPPGSgkq9M6mlbCnduh7pjq1QSv3ITzj4RuwkdKK1CjFQe7ViGbIrlkHW2N5XJMmyHp8P/xx3KV6HScP74vQ3i1cFqUSA0dX+OXqIqgyPz1FjrIqPmyeDW5NPiVK/GfXSlj6HHjtktr1tmUrp0QO3IEhIZyZMmCSWwjH3338/vv56BSbn58JqniDepypeccDPVNfVQarkGXYZSVT+NtSxaYFGqQc1Dh6SkPYmU8nvg6SYB5W+2Omj3nFhImyDSo2Njfjwww9x7733YsyYMTj00ENxwQUXYNOmTfjiiy/EyPdNN92EnJwcMYDETq5WrOBvTm+++SaOO+44zJs3D/n5+WIm0sqVK1FWViY+/vrrr+Pqq68Wg1IsW+mGG27AW2+9FeJ/MWnV2ifA0XBgUKmkZDfWljWJHzh6jQYFibyEgAQPW0mP1ythNlAPjlCITcnBrpYpUvLav8VL5+9L8NG1k3DY1L5N4jOZojDF+TRGNDwGQWmAX5+M/zbPhhsKMaCkV8lRVlaKhQtPFnsjMC+99Lw4mrzVqWOTIFXq8KWPZzepCz+CuoFPLdtUzQPEWVk0nbE3/PpEeGMLoGwuxzLVnVhs/BpqBWUbDEasca1WcKHUn3DABDjWc4ypE4wwBSFTidG3BKCjYBGDDYxBxR9b66E+O0PBHocKh1bcgOeN14u35S2ByzrBgAavCtnJHad2EhIKp512Bi6++FLx+h133AqNTIB8+PHibWXxVwd8v6toFa6vWIy5ZU8h1kiB0Uhg1khRWc0/F6UOPqSkO/aSNVB5LWgUdFhTRdOFw0XYBpXWrVsHvV4vlqe1YtlJDz74oBhYmjBhQlvWBLscP348Nm7kK/rscRYwapWUlITk5GTx/qqqKlRUVIglda3Y79q7dy+qq6sH9N9IDrSzphk3Km/BaU3XoMp74Ar96nf/jWnjcsXrs/MTOkycIMExOtmI5ZdMxX9PHR3qTYlIadnD8dj2RLziPg6+aJ7xk968EaeYtiMxum+ZSrEmA6oQg2Z5NFj4p8nhwXeFfKriKWOTUFtbi9NOm4eKinLk5Q0XJ1o1N9vw3HNL2n4HCzzNH5uMZT7eUFq1/WOYHbwEY0OpTSzPS0lJ7dN2Rgp35mw0nLYCdikPljcnTg31JpE+iNVIUSLwlXZZU0nb/Qo3b6xcByOM6uBkKqWkZaHewYO6LIOQiVPyPoU1UiopGQoSSn9E+YuXIUvGA4gsIF139ir83+ZMlL90OTLSafWehIfFi+8Qe+fs2lWE119/Ga7sOeL9ivLVB/TQUVStw1VZu3Gyah0d40cI9tqoaRY6LLp0p7psO+yCCj86chBjMlLvuDARtjOeWVZRSkoKli1bhueeew4ejwennnoqLrvsMtTU1CA3lwcWWsXGxmLHjh3idRYcYi/Q/R+vrKwUf5bZ9/G4OH6AxR7f/+e6M5jf61q3Pdz+DXqVDL/4R0OQeXCy3d1h+7weD450foErRvhxnTsVM/OuDLvtJ0NDOO0f0oxx+Dz1VKxsrsSJ+kSxcbbKz6e/uSHv0zbGR/N+PUeotsHwyUtYq5sLty8TefE6ZOgFnHLKfBQV7RRXgd5/fxk2btyAc889Ey++uBSXX34loqN5+dyZE5Ixb/1o1AhGmFuaxu7yJ2JnSQUyM7Mgl1O2TW/8UlSHZV+vwKt+KyyCBjHZE8PiNRiu+0a4S4s1oLS6JVPJUtL2f6bx8x44VhjEMrlg/F+y/a5otR8xKTLIm3bDF5uPBHVLLyVDMv39hsC+wRZeGRboF59TJoMQlYGvN9eKk99YnxH6O5NwYDAYcM899+DSSy/FI4/8G7NPmA+HMgep7iIod38D94h/tH2vp6lKvHRKtPT6jaAJcLW2lgUWe12Pf/cvZUfgbFcqZNu/xOTJsYP+dSIJ4+OqQLYpbINKrD9SSUkJ3nnnHTE7iQWDWJM3Fo10OBwHNKdlt1ljb8bpdHb5OHus9fa+jzGtP99bsbGDv/Qq3P4NCh0vsZLIFXB5rYiLa9++n//3KGbE+uEUFFivnoz/jE2lSWRkyO8fhw5Pwm9bAJsiCjExekilEtSAv4/5ZaoO+0igdEY1/il7EIsVbwN7gTHDsjAlazyOHWHGRRctwqZNG2A2m/Hdd98iLy8Po0bl4dFH/y32rXvjjZfEg0SGbcO88Rn4bMM0XCBfgU99h+Jqz1VwVZ+F/NlH9GkbI4mhxo4C5waAtePJmI65E7NhYr15wlA47Bvhbv60PCx/pBQoAKSNu9r2A52EB4VPKIjB6OHBmaw6auwolG7MwSTshtZdDkHhQ5SSB5WiU3NpnxwC+0ZsbDS+OUeLCbYnEI3jgbhccRANK1lmDjlkJP2dSdi48MILxT62bNDS/15fikz1IbhGVgRZ8VeIO/yitu/b7eKZdx5NHL1+I0RGRiqcvrmsQxJ8jroe/+476hxwQYm60j248uIThszrJHaQH1eFbVBJLpfDZrOJDbpZxhJTXl6Ot99+GxkZGQcEgNhtNsKSYf2WOnucBaT2DSCx72u9zgSaPldXZ2WDjgYlFnlkL95w+zf4/ALGSwpRIC2Fu1SK2tp5bY+5Vj0LmIE/XFm4ceEkNNTbQrqtZOgKp/2jICUB/k1VGKssR9mKJ6Ebdzo0ghMsZckrKFBb2z7552AcV/8O0HJeqx8zH/815+PCi87F999/D73egLff/gAxMUltz3PttTfiggvOwRNP/BeLFl0kTu5gFoxOwL1rDxODSkdL18Hkb0KJvQmpqRl93sZIIbfW4GbFO/x6xnQ47U7U2nkAMVyE074R7o7IisPru7aKQSV/XVHbfmCU8WMOn8octH3DHBOPNYaR7BQN1UV/oQobME4qwCdIoNAn0D45BPaNen02hsWaES3Uo7FiD5S/vojfSpuRdfTZKFr7MzSaKPo7k7DaN+688x6cfvoCPPPkE7jw3mfxgbUGGvVxmLLP67TZZhUXVvboCuj1GyH0+ii8UToey6VH4rbMGRjdzd/d73ViQynv8emuKMS4cVMG/etEEsbHVa3bNqiDSmx1nAV9WgNKTFZWltgPifVZYr0+9sVut5auJSQkdPo4+53sMYZlPqWm8j4frSVx7PFAsD98uP3xAxVu/wZWP32q9Cecrfgey12Htm1bVfEWTI9mPa8kMB1+DfLTo8Nqu8nQFA77R252DjzVq/FC9lNIXNOAhtTR0LRmKklVfd6+cQntv8AdPw43XH8Nvlj+mRiAf/31tzFmzLgOzzF37okoKBiBrVu3YOnSZ3HTTYvF+zOitRg2+jA8V3IB3rGMhMfKVxuzsnJC/n84WKjV7X3kPElTwvr/LRz2jcFAncwCO6ug9lpgc9ngV+gQrWKN9yXQmjOC9n9oNEbhD2s8PpTPgNadBmMV73NWCxPMBh397YbAviFX62GTshLmBnEyqGbTy5jlcyF2xONorNwLpbLvnw+E9KeZM4/GEUcchZUrf8CG7WX4wnwpjrDEYvI+r1OVn0+3dUrpfSqSyt8qttWiPioVdT5tt393+fKrsEzYhLv9p+NbqQs5OcOGzOtEGOTHVWHbqHvs2LFwuVwoLi5uu2/Xrl1ikIk9tmHDBjHNl2GX69evF+9v/VnW6LsVC0SxL3Y/Cyqxpt37Ps6us/sC6adEgqcZPONM2jKxhin55G6o5RJst6iRNPGkEG4dIQOL9c2QNe3FZn+meFtRsQYySUtDQ0XfJ6Nsa+D9jnYphuOO+x/EW2+9Lo54Xbr0FUyffvgB388eu+66m8Trzz//LCwW3nSYufnoPMy/6B68duUCuH55RbwvO5s3Fyc90+p0eNwzH0u8J6FQwv/eZPBixybxeRNx5LJoXF50DASFDk1NjbhnrR6PNM7Eku3BbS662p6K6z2X4RPrCJQ5NDih8SZc47kSZn3H9gBkcGJDEiwCH9Ygq9sKic8FtyDHHsGMRH14ls2SyMYGK911133i5fov3hTvW1vWCK+PT7hldODH/l4l7/lIhj6zOR5+B68+sTg9XX+j3wt9xc/IlZajrq4e0w87rG1oFwm9sA0qZWdn48gjj8Stt96Kbdu24eeff8bzzz+PM844A8ceeywsFgvuv/9+7Ny5U7xkfZaOO+448WfZ93zyySd4//33xZ+96aabxN+VlpbW9vgjjzyC1atXi1+sxG7RokUh/heTVg7wA21FSzNiv8+LAvtv4vXPjadh3Z72k1hCIkGM1I6/hay2aSmMn5WxqPp+UnpDzTw87z0eJ1tvxKeNieJ9jz32FI4//sQuf+bEE+dh+PB8MaD0wgvPHfC4UiqgdBufxklBpcBOEv/rm4//eE9Hg8Mb6s0hfeTy+vGZbAp2H7sEq7btFfPI6+pq8WpZMp5WX4RtNl6CHyzRCn6itrvOhqp6G9ZWSbDOnoR4Q3CflwwMo0YJa+vxUuV68XKXkAQ/pMgY5L05yNA1atRonHHG2XBX7YLE04wszw7Yf3wI8PFggrGl55yg5qX1JDIylQyeOlwj+xBjCh/v8vsUleug9NrQ4FFg5Q8rMWPGEQO6nWSQBpUYFvhh0ytYEOjmm2/GWWedhXPOOUdcuV+6dKmYYcQmwm3atEkMOGm1fNV+3LhxYgPZJUuWiD9rMpnEZt/7NoubO3currzySlxzzTU4+eSTcd5554XwX0r2pZTzFTaNj0et13z7ARTwwupX4SXnkbA46WSLRJYso6wtU0lqq8TFpQsQ+7AF8f2Q6FAYNQUPeM+CFVrY/voGd955L84885xuf2bfbKWlS5fAauUT31qxRrFer1fsc5eUlNz3jYwQOqUMOXFapJjUGJ1Eq7SDnVohg1HJD7N2VdbD5/OhtrYOMjU/4Teqg9uBINmoEj87NY5K2OorUPXObRhXvhxRGspiGQpMWhUs4JlKiqoN4mWRkASfvQlZafS+S8LXLbfcDq1GDXvRWryqfBg525a0LZgtLPsHjnM9iOaoglBvJhkgrP2M1G3BvxQfYmLFW2JGUmeUJd+Ll19sc8BevKnTbHoSOmHbU6l1BOXDDz/c6WNjxozBxx9/3OXPsmAT++qMTCYTM6DYFwk/w6IUQBOg9fET1effXY7v1sZjxnk3wC/X4tBMWr0gkWVEahw++vZbYC4gbyiEw56ORqcAva69B8/B8ujap0+dNX0Errzyml793EknnSKOBt6xoxAvvrgU//rXjW2PFRcXiZdZWdliAIr0DkvjfuPs8awHO+Qy+n8bCpKjNMis+RNHTnXC/vOTsFmTMD3FB7ukCiZ1YH0cA5WTYMIDlttQoC7D785c1E1VwhQf1od9JABReg2sTXxlQergvUF3CinwNlYhfUJGiLeOkK4lJibhiiuuwbNfrcO3w/NxumYNVMVfwpM2HXt8UagWMnBUrCnUm0kGiNFoQqPdL2bgSyUCJI56CLoDW9IoS34QL7/Y4UZmZhbS0tJDsLWkK3TUSsKOXB8rXmokblRXV2PFiuVQDZuG7UI6pmXFiKu/hESSnOxsFP7xCxpcMkj8XpgFfgKh0/FV6r5Qg6ecq7zNuOeOO3v9cyw43xpIeu65p2FjE1ta7NrVGlSi0rdAKWRSCigNIYlGNYZLy3DdyAaoipZDW7MeK6Ztwn8VS2DSBDfAMzIzBWUCPzCfqt6Jx+aoMTLaFdTnJAMnRq9BlRCNMn9c231F/mR4myqRnk5BJRLeLr/8auiq/sSbH/8o3lbu+goOux2CnAdKk+NoATmSFtTUjhrUe3m/P6mz7oDvkdoqIK/bIgaeVnpHUelbGKIjVxJ2FAZ+EKyT+/Dtu09D8HkRM2ameN9Rw9oPngiJFCzjh9lYxXukPF6wDvdcNKtfgkqPLBiPcXFSvH/pkQE3PDzllAXIyclFQ0MDXn75hU6CSny7CYnkoFKJn2cDqu3l8FurxOu1ghGmIJe/TczPwua1azrc97dieFCfkwycGIMeTzdNx4za2+DT8tdYkZAsZirRCj4Jd+z45dZb78C3u7ywuQFZcyU8fy/DYv9SnF3+EPJT6Hg/kpi9Nahu5E3apY76Ax5XlvLg40YhB+4RJ1HpWxiioBIJO59b0nGJ+1+4ds8snOZ4FSXXmZBrcEIhk2B6dkyoN4+QASeWkWlNeHjPGKzUzhHvmxXfBF0/lL9Nzo7H8+dOR5Ip8AZN+2YrPfPMk7DZbB2CStSkm0S6RIMKJQI/4Y+SWCB3VovX6wUjjOrg9jZKiI9HmbVjoNiq5M34yeAXF2XA3iWLYP/gVtQv+h3bj3kf3z1+PaxrPkRyckqoN4+QHv3jH2ciZ/gofLmT99CJ3vYGrsqtwMVxfyIjpu/TbcngatZd3cwnG0sdB2YqeWPy8LP2aCzzHQZ3RSEOO4yCSuGGgkok7CgMcfjKPwlaqRdJOj9USgWKhSRMTo8WpyMREmkMBiNikzKwdfzN+LKJT7G0+RX9kqnUV6eeulAMetXX1+OVV14U76OgEiFcolGFakTB4ZdDJgEShUrx/iboEK0NblCJZR42qzoGkVRR1MB5qGBDa5jmZhsgU2BngxQulxvJ5hgoFNSMnYQ/tjB1/o3346fMy8XbMZa/xEurj5dBkcgRZ05ArZOf40k6CyolTsC11kV43TcHCXIn4uMP7LlEQouCSiTsxBj4ifJ5iTvFyx9cozAqNQ6z8igVlkQutmrnd9mhg1O8bfPKwyKoJJfL27KVnn32STQ1NYrT3xgKKpFIN8ysx8kFsdht4yf5OSqe1j/CCMwd0d4kP1ia0o5su24T1DCb6UB8qNDrDTgkUYofz5TA8NkilJbuFu+n0jcymMyfPQMrJRPgEdr7pdqloT+2IQNLbU6HI3e2eF3qqD3gcYvTgzoPf41My+eLqyS8UFCJhJ1Ygxb/lH2GWUZ+Ypp4xD+x9B9jceIoStsnkSs7KwvuqiLMk/0i3o6TWPql/K0/LFjwD2RkZKK2thb33vt/4uh0rVYrTnghJJKlR2tw29zRKKnjweBopU+8lBoH5vOsOSa/7bofEmQlUAn5UMpUip1+JialyKAu/R5bykoQM+cKpFGTbjKIsAqEhJhozHPfi7t2jRLvq9TlhXqzyABjjdmf856IWc6HYRt7SYfHFGU/oXzbr5DAD099OY46bFrItpN0jYJKJCw/YBYr3hav1zhkGD71+FBvEiEhx7J+3JU7MVrKV6NHKCvDIlNp/2yl119/WbzMzMwOuPE3IUOVRWbucFsVNTA9b9LiolEnGMTrewUzshL5dFUy+KlUKtgN7VlJJokV6sxxSKdMJTLIHJWfgr+FTMTE84Uoi0D9lCJNanws9sKMIqTCKnTs8an/5W7MWHUWjpOugbuyENOmHRay7SRdo6ASCcugUpUQJV5foZ2HBgcfeU5IJGN9i1yVO/CBjzcnfKZmXNhkKjELF57eYYw1lb4RwjXY3fhRNxfpzwCLljnxn8aZeKosCTYXb04bTAVp8TjC9ThOdN2HG1wXw2xQB/05ycCxedsP43cLiTT5jQxKkzP4MX+smk+4dUnofSrSJCUkiC0eGIuz/bNRat0Lef12+CDBKv8omKXNMJn464WEFwoqkbCjV8mwyH0LrnBfjTscp2Luc7+j0U6BJRLZWFCJZSot9lyIBa478UzFyLDJVGJYY9hrrrm+7TYFlQjhFn++FV/ojkG9eTze2q7CEvVF+LHOBLU8+Idg44ZlwAYt/hKysaVJDbmUsgeHkmZP+99zt5AAb1OlWIpMyGAyMskInVKGUdJi8bZJS426Iw1rvG1278W18g8QveG/bfcrS34QL3fYo7DluWswI51n3pLwQ0ElEnaitUrEZo7Fcv9UNgMAIxINiArylBxCBkNQydtQAafLjbVCPhrXfAy1Wh1244FTU3kDxWHDqCcCIUyCke+nMqMZUg0/IGYnUHJZ8A/BhmWmwe+wiNfNu74K+vORASYAN3suxl2ec7FHiIe3sZIylcigw4LdE9OicLvnAvzPexRWxp8b6k0iA4wNkTB4anCt/CMk73yj7X5lKQ8qfbLFDm9TFWbPoH5K4YqCSiTsxOmU+O+pozEpnac3HjWMpr4RwtJ9Y2NjUPnmTSh9bAGU9tqw61mkVCrx6qtv4eqrr8PJJ58a6s0hJCwkGlTi5UNjSlB2kR2TJNtgUvPRyQMxslvmaBCva8w0MWeoUUj8eNd3FF7zzRFvC9ZqJCUlh3qzCAnYSaMTsVOWi8Xei6HQUnlTJA4eqHPysITK0wT4vYDPBWXZz+J976yrEzPiJ09mCQckHA3MUQ0hAWp0eLC+rFG8TkElQtDW/Hrduj/E69qY8DzoGjPmEPGLEMIlGXlQ6dKkbeLl+7gHx6o/HbDn18OFRnsTNIboAXtOMjDUUgF8riAXo+SBREIGm8NzYjFvTBLeWb8XxgEKupPw0lxZBn8WwKq0Jc4GyOu2QeK1owFG7Jl6OUa5ysTJwiQ8UaYSCUs/FdXBJwC5cTqkRnWcAkBIJJfAtQqnfkqEkK4ldtIc26QZuJOmmboK7HnqLExNpBO1oUYj88PXUt7IpNDxEhnEJqdH4ZyJqRiXagr1ppAQiGvchjo7b9YuddRBWfaTeP0HVz50w6cjd9SEEG8h6Q4dYZCwdO9XhR1WeAkh+weVwmfyGyGkawktn2NeQQq5hB8wG9UD1yfwmquuwZhRozB7Ni+RIkNHhrsUvz/5b2i1Ojg8Xhx22j9CvUmEHLQZObHiF4lM8fEJqLFvhlnHg0rNU2+GPW0mlr6zVUyDOXrCiFBvIukGZSqRsHTMcLPYyPTaI2mCFCGdTVSjTCVCBldPpTd8R4uXfwgFiBnA4RMGgxEnnXQKlQ0M0T4kjN3eDMHjQjo16SaEDFJxZjNqnLx8lwWVIJXji71abJfmwu9qxrGHUaZSOKNMJRKW7j0+H06PH1ol9QYgpBWVvxEy+KgVMpw/JQ121WLUqI9GVu7RuEFDq/Gk7/T6juO109MzQrYthBDSF83xo9GsLAGwBhIWVALw9dotrLAXOmct1CqqXglnFFQiYUkqkVBAiZD9UPkbIYPT5dOzWq7lsCnwhPQLvy4WGTd/Ll6v+eTfSEujoBIhZHCKM+nxkPV0LLEciQ+rNkC+cieaqnRAVApyYyigFO6o/I0QQgaJ6OgYREfzCU6UqUQIIZFNo23/HJBpo5CRQUElQsjglBhtRImQiCJfPFS7voBm82twSnip+OEjM0O9eaQHFFQihJBBmK1EQSVCBg+r04u/K6048/V1uOy9TdhSaQ31JpEhIEq3z2TB5jqx0S0hhAxGKWa+aDpJVQKJ1wmHIhZ/2QwQfB4cN2V0qDeP9ICCSoQQMohkZrYGlaj8jZDB4pPNlTjvrQ3YUdOMtWVN8Pj4FDhC+iLG0L64EKdTQiqlw3pCyOCUnhCHDEkl3jA8Jd7e7EpG1Zs3oWDLK0gwakK9eaQH1FOJEEIGkdNOOwN///0X5sw5LtSbQggJcAJcK5Nm4Ka/kaEr2qCHu7oYUqUGuSZ6TRFCBq/M5AQkS3iDbuaTv5vFyyNnzAjhVpHeoqASIYQMIjNnzha/CCGDR6Jxv6CSmg6/SN8ZDHpUvHYtAAlmnnVOqDeHEEIOWpRBhzqvBlDy289/VyheTp9+RGg3jPQKHdUQQgghhAxgppJBTVklpO/0egPg94nXqUk3IWQwk0gk2FFcjgcNo5GakATl6ccizeeC2pwe6k0jvUBBJUIIIYSQIIrRtSy9tpBLJSHbFjJ06PXtvfXS0ujEixAyuCXt/RmLf1uFnFETID8+ERD8MOs7LsqQ8EQd/QghhBBCgkgqoSAS6X8UVCKEDCWtEyzLXTybN07hhVYpC/FWkd6goBIhhBBCCCGDDJsCqlQqxbKR1smghBAyWMWa4yHVGKHJPES8PTY1OtSbRHqJyt8IIYQQQoLs3rn5qLe7MS0zJtSbQoYIhUKBJ598Fi6XC3FxcaHeHEII6ZPiuClIu/qEttuThyWHdHtI71FQiRBCCCEkyI4tiA/1JpAh6NRTF4Z6EwghpF+YNErA3n57VKIhlJtDAkDlb4QQQgghhBBCCAmZWIOm7bpC4kd2nC6k20N6j4JKhBBCCCGEEEIICZmEqPbMpGkZJpqUOohQUIkQQgghhBBCCCEhk5uRKl4a7BV4ZP64UG8OCQAFlQghhBBCCCGEEBLy8rfEjNxQbwoJEAWVCCGEEEIIIYQQEjJahUy8bHJ4Qr0pJEAUVCKEEEIIIYQQQkjIpEbxTCWL0xvqTSEBkgf6A4QQQgghhBBCCCH9JVanxLvnTYBBRSGKwYb+YoQQQgghhBBCCAmp7FhdqDeBHAQqfyOEEEIIIYQQQgghAaOgEiGEEEIIIYQQQggJGAWVCCGEEEIIIYQQQkjAKKhECCGEEEIIIYQQQgJGQSVCCCGEEEIIIYQQEjAKKhFCCCGEEEIIIYSQgFFQiRBCCCGEEEIIIYQEjIJKhBBCCCGEEEIIISRgFFQihBBCCCGEEEIIIQGjoBIhhBBCCCGEEEIICRgFlQghhBBCCCGEEEJIwCioRAghhBBCCCGEEEICRkElQgghhBBCCCGEEBIwCioRQgghhBBCCCGEkIBRUIkQQgghhBBCCCGEBIyCSoQQQgghhBBCCCEkYBRUIoQQQgghhBBCCCEBkwf+I6SVRIJBv+2D+d9ASLDQ/kFI52jfIKRztG8Q0jnaNwgZnPtHINskEQRBCObGEEIIIYQQQgghhJChh8rfCCGEEEIIIYQQQkjAKKhECCGEEEIIIYQQQgJGQSVCCCGEEEIIIYQQEjAKKhFCCCGEEEIIIYSQgFFQiRBCCCGEEEIIIYQEjIJKhBBCCCGEEEIIISRgFFQihBBCCCGEEEIIIQGjoBIhhBBCCCGEEEIICRgFlQghhBBCCCGEEEJIwCioFIFcLhcWL16MiRMnYvr06Xj55ZdDvUmEhERVVRWuvvpqTJ48GTNmzMCDDz4o7h9MWVkZzjvvPBxyyCGYO3cufvnll1BvLiEh8c9//hO33HJL2+0tW7Zg4cKFGDt2LObPn4/NmzeHdPsIGWhutxt33303Jk2ahGnTpuGxxx6DIAjiY7R/kEhWUVGBSy65BOPHj8fMmTPx6quvtj1G+waJ5M+ME044AatXr267r6fzjF9//VX8Gba/LFq0SPz+cEZBpQj08MMPi2/kr732Gu666y48/fTTWLFiRag3i5ABxU4AWEDJ4XDgrbfewuOPP44ffvgBTzzxhPjYFVdcgbi4OHz44Yc4+eSTceWVV6K8vDzUm03IgFq+fDlWrlzZdttut4tBJrYo8dFHH2HcuHHiCQS7n5BIcd9994kH/C+99BIeffRRvPfee3j33Xdp/yAR79prr4VWqxVf/2wBmx1TffPNN7RvkIjlcrlw3XXXYceOHW339XSewS7Z46eeeio++OADxMTE4PLLL29bvAhH8lBvABlY7M37/fffxwsvvICRI0eKX+xFzk6qjz322FBvHiEDZteuXdi4cSNWrVolvqkzLMj00EMP4fDDDxdXBN555x3x4CgnJwe//fab+MZ/1VVXhXrTCRkQjY2N4iLE6NGj2+774osvoFKpcNNNN0EikeC2227DTz/9JC5MsIMfQiJhv2CfBa+88grGjBkj3nfBBRdg06ZNkMvltH+QiNXU1CQeV917773IzMwUv1gWODt+Yo/RvkEizc6dO3H99dcfEAz6/fffuz3PYOfqo0aNEj9bGFZJcdhhh2HNmjWYMmUKwhFlKkWYbdu2wev1iisErSZMmCAeDPn9/pBuGyEDyWw248UXX2wLKLWy2Wzi/jBixAjxjX7f/YQdLBESKViAla2e5ebmtt3H9g22L7CTAoZdsjIH2jdIpFi3bh30er1YNt2KZWCwg37aP0gkU6vV0Gg0YiaSx+MRF+/Wr1+PgoIC2jdIRFrTEgRimaz76uk8gz3Osvpasf2KJYKE8/5CQaUIU1NTg+joaCiVyrb72Ek1S81jq2+ERAqj0SiuoLViQdU333wTU6dOFfeT+Pj4Dt8fGxuLysrKEGwpIQOPrZitXbtWTLfeF+0bJNKx1eWUlBQsW7ZMzPCeNWsWlixZIn6G0P5BIhnLRLrzzjvFE2jWB+a4444TM79ZHyXaN0gkOvPMM8UyUBYU2ldP+8Ng3F+o/C3CsP4x+waUmNbbrIkYIZHqP//5j9hEktUus8aSne0ntI+QSMAWGVi/PXZywFaee/MZQvsGiaQ2AiUlJWLZAstOYgf/bF9hJw20f5BIV1RUhKOOOgrnn3++2F6DlcIdeuihtG8Qso+e9ofBuL9QUCkCVxH2f0G23t7/5IGQSAooscb1rFl3Xl6euJ/sn7nH9hPaR0gkYMMbWC3/vpl8PX2G0L5BIgXrm8TKpFmDbpax1NpU9e2330ZGRgbtHySiM1zZwhwb7sBe86wfH5uy++yzzyItLY32DUJa9HSe0dWxFquyCFdU/hZhEhIS0NDQIPZVasVW2diLOJxfqIQEC1tFYw1XWWBpzpw5bftJbW1th+9jt/dPRSVkqE58+/bbb8Xee+zrs88+E7/Yddo3SKRj/fjYAX9rQInJysoSR6nT/kEiGZsszQKr+waKWN8YFnSlfYOQdj3tD109zj5/whUFlSIMa5bHVtn2bfTFmk6y1QSplF4OJPIyMlgJw2OPPYbjjz++7X7WC+Dvv/+G0+nssJ+w+wkZ6t544w0xiMR6xrCvmTNnil/sOtsHNmzY0DbJhF2yRqy0b5BIwV7rrES0uLi47T7WkJgFmWj/IJGMnRCz0tB9MyzYvpGamkr7BiH76Ok8g12y261YORxr0RHO+wtFESIMq/mfN28e/u///g9//vmnuBr98ssvY9GiRaHeNEIGvO7/mWeewcUXXyxOXGAZe61fbKpPUlISbr31VrEnwPPPPy/uLwsWLAj1ZhMSdOzkmK02t37pdDrxi11njYktFgvuv/9+cVQuu2QHO6whKyGRIDs7G0ceeaT4+cAm6v7888/iZ8QZZ5xB+weJaGzxQaFQ4PbbbxeDrt9//z2ee+45nHPOObRvELKPns4z5s+fLwZd2f3scfZ9LDjLJsmFK4nQGjImEYO9ibOg0tdffy2Oxb3wwgtx3nnnhXqzCBlQ7I2a9cTozPbt28XVtttuu00c68lOptn0hmnTpg34dhISarfccot4+e9//1u8ZAc+rJE3C8wOHz4cd999t1jiQEiksFqtYun0N998Iy7WsQk/V1xxhTgmnfYPEslaA0ZsP4iJicFZZ52Fc889l/YNEvGGDx+O119/vS0w1NN5ButN9sADD4gT31j7AfaZw3qThSsKKhFCCCGEEEIIIYSQgFH5GyGEEEIIIYQQQggJGAWVCCGEEEIIIYQQQkjAKKhECCGEEEIIIYQQQgJGQSVCCCGEEEIIIYQQEjAKKhFCCCGEEEIIIYSQgFFQiRBCCCGEEEIIIYQEjIJKhBBCCCGEEEIIISRgFFQihBBCCCGEEEIIIQGjoBIhhBBCCCGEEEIICRgFlQghhBBCCCGEEEJIwCioRAghhBBCCCGEEEICRkElQgghhBBCCCGEEIJA/T90KWmWzMFJkAAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Improvement Over Original Forecast:\n", + "- MSE: 1.13%\n", + "\n", + "- MAPE: 1.15%\n", + "\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABIUAAAGHCAYAAAAuvOIBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAmmJJREFUeJzt3Qd8m9XV+PFjy7Ik753EmZBACJCEkDTsMkrLLhCgpQNKaRlt6Pi/belLaUtZpYUOXlbL6KK07E2BMlr2TkhCFmQv2/GKtzUs6f85V3oUKbEdD9lavy8fofFI1vPcaByde+69WcFgMCgAAAAAAADIKNmJ3gEAAAAAAACMPpJCAAAAAAAAGYikEAAAAAAAQAYiKQQAAAAAAJCBSAoBAAAAAABkIJJCAAAAAAAAGYikEAAAAAAAQAYiKQQAAAAAAJCBSAoBwAAEg8FE7wIAAAAAxBVJISAOFi1aJN/5znfkiCOOkJkzZ8pnPvMZ+elPfyrr1q0b0OMfe+wxmT59umzdunXAzzmUxwzEu+++a/6unvflf//3f819+jo9//zzkk5efvll+fGPf9zvfW699dZe2+Kggw6SE044QX7/+99LT0/PqP9bKb2P7l+8nXfeeeYEAEA60e82/e4899xz+7zP//t//8/cR2Oi0fo+H+xjrOOIPu23335y8MEHy4IFC+TJJ5+UeLPioeHu+1CNVMwDpLOcRO8AkOruuusu+d3vfidHHnmk/OQnP5HKykrZtGmT3H///XLmmWfKDTfcIKecckq/f+OYY46RBx98UKqqqgb8vEN5TDzpcd522229bpsyZYqkk7/+9a8Dvq/+m0TbsWOHPPPMM/LHP/7RJIV+9KMfxW2/DjjgAPN806ZNi9vfBAAAItnZ2bJkyRKpq6uTsWPHxmzr6uqS//73v5IK9t9/f7nqqqsi1/1+vzkmjW0uv/xyKSkpkaOPPjpuz3fOOefIUUcdFbe/B2DkkRQChkEDgt/+9remSuiyyy6L3D5//nw544wz5Ac/+IHpQdp3331ln3326fPvlJWVmdNgDOUx8ZSbm2uqYBCrtzY59thjTUWXVnfFMylUUFDAvwEAACOUTFm7dq2pfr7gggt2i/9cLpcUFRVJsusrVvj0pz8thx12mIlN4pkU0gTarkk0AMmN4WPAMGilzN577y0LFy7cbZvdbpdrrrlGbDab3H333TFlrfo4LdudNWuWudzbULDHH39cTj75ZDMc7fOf/7y8/fbbJkDR+6pdH6PJJw1aHn30UTNc6cADD5TTTz9dXnvttZj9ev/99+Ub3/iGfOpTnzL3Oe6440yZbSAQGJE2evbZZ82xzpkzxwyv+/nPfy6tra2R7frcn/3sZ007aDJNK66s7Q8//LCpstL91Moova/2cEV79dVXTXm3Bjz6WP37bW1tgzpereTRNtZ/j0MPPVR++MMfyvbt2yOl1++99545DafUWYOyrKysmNteeukl0zb6b6xtc91115neR4vb7ZZf/OIXJnDTfT/xxBPlT3/6U7/l17qfX/ziF2X27NnmdfDWW28NqGR716Fg+tya8Pzc5z5nnltLzb/+9a/LqlWr+jzGN998U77whS+Yf2tt729961sDHkIJAEAyycvLM8mS3obEa2yj37E5ObH96x6PR26//Xbzfa3f7fodqhXlu8ZYDzzwgHm8xh1f/epXpaamZrfn0Nv+53/+x8RG+p3+ta99TVauXBm343M4HKaDLzo20f3U/dW4TL/7dR///ve/xzxu8+bNcumll8ohhxxi9ktjDo3F+hs+tqfj7WvI2a5DwTTm1eomjfe0WlqTWnpdq7L78re//S3y76EVTBpXdXR0DLK1gPRGUggYoubmZlm+fLmpAtn1x75FS3IPP/xwMydNNB1KdNppp8ktt9xiviR39cQTT5gkj/4Qv+OOO8x9vv3tb++WENmV7o8mDb773e+aoEQTUlrFZCVZVq9ebRJHul86x80f/vAHmTdvnknIPPfcc4NuAx0OtespekJm3XcNaDRho8eqybN///vfJvmgSQeLBgcaUOg+XXHFFVJcXCx33nmn/OxnPzNf+NpeX/nKV0xyTW+L7qm75JJLpLy8XG6++WaTzNFEi47zH+jx6nxQGlBo4KZ/X5//nXfeMVVeSkuuNRmnJx2qpUHIQNvE6/Wa5JL+XU2YaJLO8vTTT5v20KSi/ltppdlTTz1l/p2tNvzlL39pkno6n5H+u+pcVTfeeKNJ/PVmxYoVcuGFF0phYaFp7/PPP9+0/1Bom+jzXHzxxfLnP//ZtMuaNWtMu/Q26faWLVvMvmsQqe18/fXXy4YNG8zjRyrhCADASNLOOWsImUUTCvrdfOqpp8bcV78bNVlyzz33mCFUGrtoMkLjk+jhW/fdd5+5rgknjZM0sRId21gxpnZ46fe6btNOGv0u1VhosJ0tul/RsYkmrtavX2++1zs7O2NiE02YaPygHWXW/mssonGK0n3QuKu7u9vEI7r/GmNpJ5BOndCbgRzvQOhzalyjx69/T+Mivf6vf/3LxHi90U6/m266ybSb3l/jLp1H6dprrx308wPpjOFjwBBt27bNnI8fP77f+02ePNkkhTQxo8kOpYkJrbqwfPTRRzGP+b//+z+TbNLKEaU9G1p5pEFBf9rb200F0aRJkyK9XNojo0kOTSxpkkSTVPoFqWPllVao/Oc//zGVI3ua+2jX4+8tQaJJA00E6PFqckArR7R6x6JD6fTLWRMOeq40SNHEh7aLdRwaOGjvk07YrbRXSAMPva5tp8PxtPdoxowZJsljJea010vbr7GxcUDHq0khp9Np9lkfq/R59N9EAymdr0erfNRAhmr11ibV1dUmOafPofTv/uY3vzH/rnoePReTJrE0QaaVUVr1o/tr/btor5z+m2oSrDeaSNNt2u76elGlpaWRJNlAaTJLA0Vtaw2IlfZUaiD8q1/9yrStzikVbdmyZSbRp8HimDFjzG1aPq6vfa1+stoQAIBUod/FOkwsegjZiy++aL5r586dG3NfTRRpda7OM2l9b+t3uMYYGpdoAkNjCo1v9LtV56G04hv9ftVqmujqlpaWFjM/pRVnatWwPk7/liZuBkorpneNTTRm0njMijeVduQ89NBDpjPJild03/S+Gl98+ctfNvGaJpS0E8gacmZVvWvssCuNdwZyvAOxceNGE1f8+te/lokTJ5rbtLp76dKlJl7qjd4+YcIEE29qHKixjMZR0RXrAEgKAUNmVUtYP777otU60fdXmsjoi/a0aOXM9773vZjbNcDYU1JI5xiyEkLKGtOtvStK5znSk/YS6Ze/PpcOB9IKJJ/PJ4OhSQFNPuzKek7tWdMAYdeeNE38aICjX9RWUmjXNvnwww9NgkGHekWv2KXXlVbdaECgZdSabImu1NLAw0pkDOR4dZiT9jDpfmriTIMcDViGOr7+kUceibS5TuKoySdNrmiVj0UDKu111ARK9PHpvmjyRI9PA1FNAmnQpPfV/dFTb0MVLZrg0uAu+jWpFVDWa3CgNDlmDVPTSidtOw3GrEk1ewv8tOdPS9HPPvts07OowavuvwaLAACkIk3oaOwRnRTSypSTTjpptypxjWt0OJl+B0bTqhtNvuh2TUw0NTVFEjEW/XvRSRKdMkDjIu1kseIEfax+t2pV8WBoQujqq682l+vr603lksZAeq7VyhbtQNRYtbfYS+M9jTE0ltHEllb6vPHGGyZe0n3SqqPeaLwzkOMdCG2Pf/7zn6ZaSWMSjel0zid9jr5Wd9WkkVZ561D9448/3sRRWqnfV4U/kKlICgFDZPXcWBVDfdFhNfn5+ab6xKK9FH3RkmG1azVIRUXFHvdJe7OiWV961vAdTbRoyayWzuoXqPae6PwvGsT0NiRoT4kDHZ/dF6sXprf91tu0GiiatpFFe8eU1VO1Kw1q9O/rPvdVNTPQ49XrOn5eEzh/+ctfzGXdPy0BH8py69FtogkwDSI1wad/36qEso5PgzQrUNv1+NSVV15pkmwaAOpx6En3V8u7dUnZXWmbaGVQND3WXW8biNdff92UjGuwpf82+nzW67a314q2rZaIa/tpYuzee+81E3Bqz+L3v/99AjAAQErSBIYO8dYOGu380ISNfq/19R28a0eMVVmrcY8VG+36vbxr9a3GCZr06GvIutXZNxD6HR4dm2gnjiaqdLi5Vpdbi5ZYsUlfVePaSaTf5TqkXJNEWjGl0x1oR5QmXDSesSrio9tkIMc7UBqn6bA23VeN1XTIusa+u8aUFu0k1BhYk0lasaQV5hq/63QDVgciAJJCwJBpMkKHE+kcOfqj3xqeFE3LY7Xqw6pwGQir0kZ7VqLten0odJ4X3V/tHdJhVdaPfJ23J96swECHGkX3RKmGhoZI6W9vrNU8dGhVb8vbayBgTdxsJdEsWhWkvV0a9Ghl1UCOV4dx6UmDLH2sJjR06J7+jeFUuuhr4oYbbjABls4Rpb2LGlBax6fz9mgpc19tp4k3HaevJ60e00odDWp0iJ7+rV1p4lHbO5omcKLLpHdNFFp0uJiVmNNJJLUiSYM8LRnXfyt93D/+8Q+TLOpLdAm59ihq75wGb5pQ0qAaAIBUo5Uw+v2o1UIaR2gniCYjevvu1gmPtRo5OjFkdfRoYsRKjuwa01kJGYvODajxgcYJvbGGuw+FxlA6rF9jV40LrSp0KzbRoWvRHXXRQ+GVVi9p55TO66PD9LVddO5EPbbouZOsYx7I8VqxSXTbaVwSTedi1CHsuoqrVv5YySw9jl2nYYimleB60sSRVjfpvurf0OF/1nB3INMx0TQwDNpzpENrdPz4rvSLTb8ctVrlm9/85qCSQjoETHtgor3wwgvD3l/9oa5DevTHvpUg0cmpNbES78mANaGiQYtO8hftgw8+MAkOnUS7v8dqz5P2SmnvlnXSqhdta119QgMWLSW2hjRFj+nXCiMNwgZyvDo2/ayzzjLJE+1t0hJnnd9IWatj9JbwGyjtkdKx91oxZq1Cp0kyTSrqcUQfnwYnGpzpsDh93ehwNu2Rs4IxHW6nCabeVimxkl16/NE9iJrEiR4aaM3tEz1ppiaNoieu1DbS5Jq2o74WrWDNSgj1VimklVDadpoQ0n933RdrIse+9hcAgGSn32kaR2gnky5S0VcljSZxtCp519XKrOFemoTQjq5x48btdp9dYxn9Wxpf7rXXXjFxglY+azXuYIeF70qHuGlnmMZo1nw8VjWzJrain1NjJh3+pokcHd6vnWw6j6DGBhqH6byFOj9Rb9/1Az3e3mITjeGi6XVNXGlMbSWENHGkt/cVw2pFlzXsXhNt2kGlMZn+O1nJOgBUCgHDol+oWgGiKzDoXDWaXKiqqjI/9nVyQL1Ne2F6G+rTF/2S1dXDtLRVk0q6LKj2xFgrPwwnQaGVHBrQ6L5NnTrV/F0tAdbnHEwp8kBo1YomFXS/NcGjCQNtFw0sdDz6mWee2edjtWdJv/T1vlptpYkdTRDpdd1Xqz21nbSKRidF1LmDtEpGk0YavGmAMpDj1fHmWo6s/45aTq0JFF05RPdftykNQjQQ0pJxXYVs1/LoPdEhZBrEaVJIj1sTRRpEaU+dBnbaNm1tbaYKSI9Ty8V1HgM918obbT9dllUDxMcff7zXFeuUBj66+to3vvEN034ayGmVVPQcQ/p3NEDTfxer2kqrgaKHHurzagJOJ+jW8nJN9GiJ+SuvvGK268TRu9K20sou3Qed3FyPS+cL0GB617kEAABIJTrUSOcB1BjMWgBjV9Zcerpdv8s1VtGEi/Xdr7GP0vhOK371fpqc0TkYNU7ZNW7QBJCe6/ewxkXPPvusmQi6r/l7Bksnfta4RyujNbbQ+ECv63xBOjWCVkNp3KHzLmp1lCZ4NJmi8YlWMOmcjlp1pJNra7yrE2nvSmOMgRyvzvWjldUaF2kMU1tba+KU6Ioljen0cVotpHGFJnV0/kON/fqKyzQ20VhaOwD130djLY2r9FgGE5sD6Y6kEDBMuhKWzvOi5bb6paM/xHWstK44oQkhKwgYDJ0ET39465edrtKlK23p/DJ66m8+oj3RxIc1uaD+0NcveU2q6ER9uiLXnpa8HywrYNC5ZnQokSZaNCDQnps9HYfeR9tRx4Frkka/8LX6RBNA2tujNCjQ4Un6Ba/JCO050rbT5x3o8WogoskMrcjRyi8NYLQ3T4eQWfNAaYWOVs9cdNFFJmjR5xgMTYxo8KUBpb5GdNUQXa5Wgx09Nm0bbQ+tntJ9sYbWXXPNNWbfdd90yJ1WF+lEzrtOQm7RIEfbWgMmTTrp/bXqSa9bNFmjz6/zBWlb6r/P1772NTN3kAZ/1op5WrGk7artpW2vQyX//ve/m3mWtNpLg8doGlzpv4UGcfp3tW01oNR933X4IAAAqUSrY7SDSDtVtJOpN1Yni37HavWsxoMad+h3YvSKszqUSZNL2hGkiR/txNLve72fRSuHtWNFv4t1qJZW7+p3vMaVGgfEg34363e6fk9rskU7dDTG0WOwFrnQOEITYhqTafygJ72/7pfuiyZZdL90/3VIV28GcrxaEaXxkXbcaYeitrE1l6JFE2vauahxscaG2kYaw+nchZrI0ornXf9tzj33XBMH6vHoYzShpbGkDh/b00IxQCbJCg52dlkAI07LebUiJfrHtFZpaFJBv1Dp3QAAAAAADBdJISAJaS+J9nhoz4z2SukKFNrzpPO7aLUGAAAAAADDRVIISEI6yZ+W5uqkwVp+rEN8dB4ZnUOntxUhAAAAAAAYLJJCAAAAAAAAGYgl6QEAAAAAADJQQpNCutygTpyrK+4cd9xxZqZ+y8qVK83qPLNnzzbLfOvKP7tOxKvLTut2XXVIh9gAAAAAAAAgBZJC1rLUjz32mFmuWZdefvHFF81S3DrR7rx588w2Xe5bk0d6u1q2bJlZmluXj9alnHU5xCuuuCKRhwIAAAAAAJBSEpYUam1tlSVLlsi3vvUtmTJliqn6Oeqoo+Ttt9+WZ599VhwOh1x++eUydepUkwDSyXWff/5589j77rtPTjrpJDnjjDPM0tw33nijvPrqq7Jly5ZEHQ4AAMCooNIaAACkfFLI6XSKy+UylUA+n0/Wr18vixcvlhkzZsjSpUtl7ty5kpWVZe6r5xr4aBJJ6XatIrLokt3V1dXmdgAAgHRGpTUAAIiXHEkQrQT6+c9/Ltdee63ce++94vf7ZcGCBaZ36+WXX5Zp06bF3L+8vFzWrFljLtfX10tVVdVu2+vq6ga9H83N7cL6aztpHq6srJB26Qdt1D/aZ89ooz2jjfqXae1jHS92Vlpr/KSV1nqyKq11m1VprR1qmgB67bXXTKW1xljRldZKK62PPfZYU2k9ceLEAe9DU1NmvO52fQ2Wlxdm5LH3hvbYibbYibbYibaIRXskpi2s50rapJBat26dCUa+/vWvm4SPBjiHHXaYdHd3S25ubsx99brX6zWX3W53v9sHgyCzd7TLntFG/aN99ow22jPaqH+0T+aJrrT+wQ9+YBI6Wmmt1UP9VVprUki3X3TRRb1WWg8mKaRBbKYG9Zl87L2hPXaiLXaiLXaiLWLRHsnZFglLCmmP1iOPPGLmAtIAZ+bMmbJ9+3b5wx/+YAKTXRM8el3vp7QXrLftGiQNFtnKWGRx94w26h/ts2e00Z7RRv3LtPYZaE9XJkiGSutwzimjWMecicfeG9pjJ9piJ9piJ9oiFu2RmLYY6HMkLCmkEx9Onjw5kuhR+++/v/zxj380Y+EbGxtj7q/XrUBmzJgxvW6vrKxM6QxdMqFd9ow26h/ts2e00Z7RRv2jfTJToiutMzlBl8nH3hvaYyfaYifaYifaIhbtkZxtkbCkkCZ4Nm3aZAIRK0DRyaYnTJhgVsS4++67JRgMmtJnPdfS6EsvvdTcT7cvWrTI9IxZq3DoSW8HAABIV8lQaZ0pFWqZXJ23J7THTrTFTrTFTrRFLNojuecUStjqY7qEqt1ul5/+9KeyYcMG+c9//mOqhM477zw58cQTzYoY119/vaxdu9aca++XTo6ovvSlL8mTTz4pDz/8sKxevdpMqHjMMccMajw8AABAqumr0rqmpqbPSup4V1pbFWqZdsrkY6c9aAvagragPSQl22IgEpYUKiwslL/+9a/S0NAgZ599ttxwww3yrW99S774xS9KQUGB3HnnnZFqIJ0A8a677jLLrypdYvWaa66R22+/3SSIiouLzeMBAADSWXSltSW60vrDDz80FdbKqrS2KqmtSmsLldYAACChq4/pZIh/+ctfet02a9Ysefzxx/t8rCaLrOFjAAAAmUArrW+66SZTaa2daVptrZXW/+///T9Taf3b3/7WVFife+658sADD+xWaa0V2QcddJAZdqb3o9IaAIDMlrBKIQAAAAwOldYAACBtKoUAAAAwOFRaAwCAeKFSCAAAAAAAIAORFAIAAAAAAMhADB8DEDfZ2VmSlZXovQAAAIhfbGMJBAa4vjMApBCSQgDiFjQ9u6pe2rq9UlWaL5/Zu1T8foInAACQurHN0yu2S7vbJ4VOu5x2wBgSQwDSDkkhAHGjCaGWLp+4XD2J3hUAAIBh04RQW7cv0bsBACOGOYUAAAAAAAAyEJVCAIaEMfYAAAAAkNpICgEY1vxBRa5cOXlGVaJ3CQAAYFSxwAaAdEBSCMCw5g8CAADIpCpprZCOnoS6siRPPjetjAU2AKQkkkIA4k5DJg2WguHYiOFlAAAglRU4c+Sp5XUmttFVyKInoWaBDQCpjKQQgLgrcOTIMyvqpbXLExleRmIIAACksnZ3jwStHi8ASBMkhQCMCIaXAQAAAEByY0l6AMPm8wdk9fZ2WdPQSUUQAABIazpEPnoVVgBIZVQKARiWtzY0yy2vbhBPT8Bcn1aRJ9PHlyR6twAAAOIu32GTJz6qlQKHXbJYegxAGiApBGDI6trc8ub6HeZygcMmnV6/rG3skjfXNcn0yrxE7x4AAEDcbGjqlP980ig52Vny9UMnJXp3ACAuGD4GYEh0mNibG0IJoYMnlsjVp8yQY6ZVmOtPL6uRLTu6E7yHAAAAwxcIBuXJZXXy6JJa2dHlk4YOrzy6pMbcDgCpjqQQgCFZsq1Nmrt84rJny+F7lUqXt0fmTSqWqeV5otMKvfxxQ6J3EQAAYNg+qe+UJVtbzeWDJhSbSqF1pjK6OdG7BgDDRlIIwKB1envkjfWhQOj46ZWSl2szl3VsvSaIbNlZUt/hlcYOb4L3FAAAYHhW13eY86Omlsnps8bKCTOqzPV3N+2Q+nZPgvcOAIaHpBCAQXtjXbOZWLrIkSPzJsdOKu202+TA6iJz+eNwEAUAAJCKGjo8ZriYLjZmxTczxhbKAeMKzWWtGAKAVEZSCMCgl2D9z5pGc33vijzJ7mXljflTysz52sZOs1w9AABAKvqopt2c7zemQPJzd67RMzOcINrQ3CVB5hYCkMJYfQzAgGgy6NlV9dLU4ZbX1oaSQlPKel9hbFpVgRQ5c6TN3SNrGjpHeU8BAACGz+3zy8q6UFJozsTYyuipFXlit2WZlVdrWtwJ2kMAGD4qhQAMWFu3V5bXtIvXH5RiZ45U5Nt7vZ9WD1k9aB/VtI3yXgIAAAyfVkbrcPmCXJupjo6WY8uWqRX55vKqOmIdAKmLpBCAQdnQFBo7v29VgZlYui8Hhsfab97hNsu3AgAApJI31jWZ830q83sdLr9PZYE5X13bzhAyACmLpBCAAc8l1BMIyuaWbnPb9DGhQKgvxS67lOWFKone37xjVPYTAAAgHjTJ82F4GfrqYmev99mrIs+suNrS7ZPV21lcA0BqIikEYEBzCb2ytkk27+gWnz9o5gsaV+TY42PHh4OodzaSFAIAAKnTEVbX7pH68KpjlQW5vd4315YtE0tCsc4r4UU4ACDVJCwp9Nhjj8n06dN3O+23335m+8qVK+Wcc86R2bNny1lnnSXLly+Pefwzzzwjxx9/vNm+cOFCaW5uTtCRAJkxl1CHxydbw1VCWkbd39Axy/iSnUkhyqoBAEAy02TQ0yu2m9PS8JyIVYUOsduy99gB9lFtaEJqAEg1CUsKnXzyyfLGG29ETq+88opMnjxZzj//fOnq6pKLL75Y5s2bZ5JHc+bMkUsuucTcrpYtWyZXXnmlXHbZZfLggw9KW1ubXHHFFYk6FCBjbAuvrjGpzDWg+48rdIgtS2R7u0c27QgllAAAQ0enGjCy2t0+c1oSHjpmJX36okkjtby2TQJ0gAFIQQlLCjmdTqmsrIycnnrqKVNJ8MMf/lCeffZZcTgccvnll8vUqVNNAig/P1+ef/5589j77rtPTjrpJDnjjDNMEHTjjTfKq6++Klu2bEnU4QBpzx8ISl2bx1yeWDqwpJCuzDG+JHTfdxlCBgDDRqcaMDqWbGuLqXruS5nLLjnZWdLh8Zth9gCQapJiTqGWlha5++675Qc/+IHk5ubK0qVLZe7cuZHhKXp+8MEHy5IlS8x13a4Bj2XcuHFSXV1tbh8sfQpOsSfahTbq7Xhr29xmomlHTrZU5Pc+tt5ql+jzKeGqonc370j4cSTTKdNeQ7QR7ROP4wWdasBocPv8sq6xc0CVQjrkbGz4PivrGEIGIPXkSBK4//77paqqSk488URzvaGhQaZNmxZzn/LyclmzZo25XF9fb+6/6/a6urpBP3d5eWjZbMSiXfYsk9rI5XLIxw2h3q9xxU5xOR3Sk9UjecEscTpyxSfZ4nTkmHNXMPTrzeHIlbxAlkyvtslr65pl8ZZWKS7N73dcfqbJpNfQUNFG/aN9MpvVqXbdddftsVNtwYIFZvtFF13Ua6faxIkTE3gkQHKpaQ0Nl59c6pK83Bxp6/b1e3+tit66o1uW17bLyfuPGaW9BIA0SQpp79bDDz8s3/zmNyO3dXd3m+Amml73er3mstvt7nf7YDQ1tQvDf3fSOFJ/ZNAufcu0NtIesO5uj6xvCPV+VeTZxe3xitvdI11dXnG7bOayXQLmvLvbK1LiEo/Ha7YXu3KkxGU3y7X++l8rpdSVI0WuXDll/yoJBDKgAXuRaa+hoaCN+pdp7WMdL5KjUy0TK7eiq/OQvu1hHc+2cFLooAnFA3pMdXio/Iq69rRrk8FI19fFUNAWsWiPxLTFQJ8j4Umhjz76SLZv3y6nnHJK5DYtfd41waPXtWS6v+0u18DmOYmmwXQmBNSDRbvsWaa0kXWM1jj5MeEJFfd0f+tce6pnjy+SV9c2ydqGDtmnIj+j2q8/tMGe0Ub9o30yVyI71TI5QZfJx54p7eFy5Upte2gOxSOmV5kqoZ6sbHE67eLXc4ct5twnWVLtCJj7r2nokILiPHHabZLJ0vF1MVS0RSzaIznbIuFJoddff93MD1RcvDMTP2bMGGlsbIy5n163erf62q5j6wHEX5u7x5w021xZ0Pd8Qn05cFwoKVTb6o4khQAAqdmplikVaplcnZep7aHV0V1dHtneGkoK7V2cK283d0pXt0/cjmxxe/xiCwZizrVCurIkT0rz7LKjyyfvrK4zcU8mStfXxVDQFrFoj8S0xUArrROeFNKVMHS8ezRdJlXHyGsvmFYZ6PnixYvl0ksvjWxftGiRGR+vamtrzUlvBzByY+urChxDmhPowHGhD6Pa8OplAIDU7VTL5Aq1TD72TGgPPZZ2d494/QGxZYmsaeyKzNHV32P0PhrrvL6uWT6qaZcDxmZmUihdXxfDQVvEoj2Ssy0SPuOrjnPfdfy7jo3XZVKvv/56Wbt2rTnXkmhdMUN96UtfkieffNKUTa9evdqssnHMMccwSSIwwkmhPa3A0RsNpQ6sLjLnWm3U5fWPwB4CQGbpq1Ptww8/NJ1pyupUszrNrE41C51qwO4aOkPVdKV5uWYVsoHId9gkJ9xptmp7u6k40hMApIKEJ4W0h6qoKDabXlBQIHfeeWekGkhXxbjrrrskLy/PbJ8zZ45cc801cvvtt5sEkfaS3XDDDQk6AiD91YfH1o8t6n8+od4Uuuzy6rpmqQrPRdTQQbUQAAwXnWrAyGjsCCWFKgY5XL4sz27O39/cIv9ctFWeXrGdxBCAlJAUw8d6M2vWLHn88cf7fJwmi6zhYwBGjvY0N3YOLUCytHV7pbrYKdvbPdIQDrYAACPTqXbVVVfJQw89JNOnT++1U+2WW26R1tZWOeKII+Taa69N0BEAycmKeQY7h2J5fuj+TZ1eaQn/DQBIBQlPCgFIbk2dPun2BczwLw14Otw9Q/o7E0qd8uHWVqknKQQAw0anGjDSlUKDq44udORIri1LvP6gtLp7pCScJAKAZJfw4WMAktvaxg5zXpafO6RJpi0TSlyR4WPWfBcAAACJZs0B5A8GpbkrnBQaZFJHJ5u2Ekkt3b4R2U8AGAlUCgHo17rGLnM+pnB4PV5jCh2Skx3qQdMlWwEAABJNk0E6/0+722fWbw4ERXJzsqXIOfifSTrkTBfn2EFSCEAKoVIIQL/WNnRGkjrDYcvOivyNGpamBwAASUITQm3dPtnYFIp5qgpy97gUfW+suRepFAKQSkgKAejX2sZwUmgIK4/tylq9zFrNDAAAIFnoghjKWjF1sCqt4WNURANIISSFAPQpEAzKunBSaKgBUrSdy9Iz2TQAAEguVqfVUGOenZVCPSaGAoBUQFIIQJ+2tbjF0xMwcwFZS60OhxUsNXZQKQQAAJJLfbt3WEPmS112M1xeJ6xuG+JqrQAw2kgKAdjj0LHyfLtkD2Fs/a6slTy6fAFp6qRaCAAAJAefPxCZC6hqiItr6KTVpXl2c7mZOAdAiiApBKBP1tCxwS7L2hdd0t5azcOawBoAACDRWsOVPS67TfJyh75Ac3leKGai8wtAqiApBGDPSaHwxInxUBbuQbOqkAAAABKtNVwlZFX6DJU13L6JyaYBpAiSQgD6LIFe19RlLleG5wKKa1KISiEAAJBklULxSgoxfAxAqiApBKDXhNAzK7fL5uZQUigek0xbysJl1VoppM8DAACQLJVCVufVUJXlhx7f1OkTnY6RWAdAsiMpBKBXW3d0iT8oZhWNwvA8QPFg9cCtqe8wiSeCJQAAkGjWamGl4c6robIe7/UH5J+LtsnTK4h1ACQ3kkIAerWja2ePWTxWHrPoRNP27CzxBYKypZkhZAAAILGCwWBk5TFdVn44crKzpCDXZi5va+mWdjdzCwFIbiSFAPSbFIrn0DGlCabKwtDE1fUdjLcHAACJ1e0LiFfLo0WkZJjDx5RVYd3aHao+AoBkRlIIQK+sHrN4J4XUmHBSqJGkEAAASJKYx1Qz24b/80j/TvTfBYBkRlIIwB4qhYbfY7arMUWhpFADSSEAAJBgO7q8ce0IK3SQFAKQOkgKARjV4WPRlUIs1woAAJJmHsU4dYRZlULWimYAkMxICgHYTU8gKK3ukUsKVYT/5o5un/gDoTH8AAAA6dARxvAxAKmEpBCA3dS2ukVzNbas+C5Hbyly5ZjVOfQ5alrdcf/7AAAAA6WdVCMxfKzT6xefPxCXvwkAI4WkEIDdbN7RFenpiudy9Bb9m6Xh1T02hZ8LAABgtAWCwbhXCjlyss1JsQIZgGRHUgjAbjbv6Dbnxa74VwlZysJJoc3NoecCAAAYbbrohQ6b1y6wEld85hTKiur8sobjA0CyIikEYDdbwkkha0z8SCjNC/XGbWymUggAACTGpnAcosPlbdlZce/8YrJpAMmOpBCAviuFnPFfjn7XYGlT+LkAAABG29YWK+aJb0dYSbjzq4XhYwCSHEkhAP0khUZw+Fh43P5mKoUAAECCbG1xj0h1NJVCAFIFSSEAMXSVjNq2cIA0CnMK1Xd4pcvrH7HnAQAA2FOlULyTQtacQixLDyDZJTQp5PV65eqrr5ZPfepTcvjhh8vvfvc7CQaDZtvKlSvlnHPOkdmzZ8tZZ50ly5cvj3nsM888I8cff7zZvnDhQmlubk7QUQDpRZeI16Xidcn4PLttxJ7HabdJnj07ZrUzAACARFQKFcZ5yPzOiaZ7zApnAJCsEpoUuu666+Stt96SP/3pT/Lb3/5WHnroIXnwwQelq6tLLr74Ypk3b5489thjMmfOHLnkkkvM7WrZsmVy5ZVXymWXXWbu39bWJldccUUiDwVIG1aVkK48pqtnjCRrsmlruBoAYM/oVAPiQ983kUohR3wrhXReRg2j/IGgNHZ44/q3ASAtkkItLS3y6KOPyrXXXiuzZs2Sww47TC688EJZunSpPPvss+JwOOTyyy+XqVOnmgRQfn6+PP/88+ax9913n5x00klyxhlnyH777Sc33nijvPrqq7Jly5ZEHQ6QNmrbPCM+yfRuk02zLD0ADBidakB8tHb3SGd4CHuhI77V0dnZWZEhaVbiCQCS0chNGLIHixYtkoKCApk/f37kNg1k1M9+9jOZO3dupEpBzw8++GBZsmSJLFiwwCSOLrroosjjxo0bJ9XV1eb2iRMnDmo/RrgQIuVY7UG7ZGYbaQDz+vpQr3GJyz7i7VOab61A1pWW7ZmJr6F4oY36l2ntkynHOZhOtb/85S+mU01ZnWo5OTmRTjWNnTQB9Nprr5lONY2fojvVlHaqHXvssaZTbbDxE5AOtraGkjUFDpvk2OLfV64dbJp40qH5B40vjvvfB4CUTgppADJ+/Hh54okn5I9//KP4fD4TsHzrW9+ShoYGmTZtWsz9y8vLZc2aNeZyfX29VFVV7ba9rq5u0PtRXl44zCNJT7RL5rZRU1doQsTyIqfk5TnE6cgVn2SL05FjzvOCWZHboi9b213B0K83hyNX8gK7b49+zNiSPBFplpp2r1RUpGd7ZuJrKJ5oo/7RPpknWTrVgHSaT2ioHWF7YlUK1bWHqrABIBklLCmkpcybNm2SBx54QG644QaTCPr5z38uLpdLuru7JTc3NNeIRa/rGHrldrv73T4YTU3twtxvO2kcqT8yaJfMbCOtFGruDAUu+TlZ0tXlEbfLJm53j9glYM67uryR26IvW9u7u70aXYnH4+11e/RthTmhHy7r6jvSsj0z8TUUL7RR/zKtfazjRXJ0qmVi5VamVedlSntsC1cKFQ8jKdRfWxSFh+LXtXlSvq0y6XURD7RFLNojMW0x0OdIWFJIS5w7OjrMWHgNblRNTY3cf//9Mnny5N0SPHrd6XSay1oa3dt2TSgNlgbTmRBQDxbtkpltpMfTFl46dagBktUmA2mbCaUu0c+qLp9fHlpSK184qFoCuvRZhkjH11C80Ub9o30yTzJ0qmVygi6Tjz0d26OhOzSfUGVhuDraaRd/llY322LOfZLV7zblcNh3u395ocNsa+z2ZVRFdKq/LuKJtohFeyRnWyQsKVRZWWmSO1ZCSO21115SW1trSqIbGxtj7q/Xrd6tMWPG9Lpd/yaAoevy+qXLFzCXi5050h2efHGk6Pj9QmeOtLl7ZAvL0gNASnSqZUqFWiZX52VKe6zb3mbO8+3h6mhHtrg9frEFAzHnpsq5j22mQrrYIR6Pb7f7u2yhhNGWpi5pbGyXdJcur4t4oC1i0R6JaYuBVlonLCmkS6F6PB7ZsGGDSQap9evXmwBHt919991mmUgdD6/nixcvlksvvTTyWB1Tr+XSShNJetLbAQydToSonPZscdptI54UsiqSNCnUEp7LCACQ3J1qmVyhlsnHno7tEY85hfqrkLaGj9W2uU0ltDXfV7pL9ddFPNEWsWiP5GyLhC1Jv/fee8sxxxxjlkJdvXq1vP7663LXXXfJl770JTnxxBPNMqnXX3+9rF271pxrSbSumKH0Pk8++aQ8/PDD5rG6yob+LSZJBIanJjy2vnSEJlzsTYkrlJtuDQ9bAwAMrFPNEt2p9uGHH5rONGV1qlmdZlanmoVONWQyt88vjZ3eYc8p1J+C8ETTnp6AWYUMAJJRwpJC6je/+Y1MmjTJJHl+/OMfy1e+8hU577zzzKoad955Z6QaSFfF0IRRXp6uVCQyZ84cueaaa+T22283jy0uLjbj6gEMz7ZwpVBp3mgmhULP1eImWAKAPaFTDYiPreGYp9CRIy67bUSeIyc7S/JzQ3+7tj30fACQbBI2fEwVFhbKjTfe2Ou2WbNmyeOPP97nYzVZZA0fAxAftZGkUOxEpKOSFGL4GAAMuFPt2muvNUkenQ/I6lTToSnaqXbVVVfJQw89JNOnT++1U+2WW26R1tZWOeKII8zfATLRtpZQdfSEktCcWyNFl6Xv9PrNCmQzxiTPxLIAkBRJIQDJJZGVQq1ukkIAMBB0qgHxm09oQsngVy8eDF1Qo7bNY+YVAoBklNDhYwCSc6Lp0Z1TKPRcHR6/Gd8PAAAwWh1ho1EppLa3e0b0eQBgqEgKAYhMSGoFSCWjWCmkK53lhpdstZJSAAAAI2lbeHGNka4U2rkCGUkhAMmJpBAAQ5eF1zHvo50U0jkwtLRabQ2P7wcAABhJ28LDx6qLR7ZSSCeyVnUMHwOQpEgKATBqwsFKXq5Ncm2j+9FQFA6YrJVAAAAARkogGIzEPSNfKWQlhagUApCcSAoBiFl5rDgcvIymnZVCJIUAAMDIaujwis8fFFt2llQVOkYlKbSj28fciQCSEkkhAEZNuAereBQnmd41YLKWhwUAABjp+YTGFTkkN2dkfw45crIlP9dmLtcx2TSAJERSCEDMWHcrQTOaihyhRJQ10TUAAMBIsRa20ITQS580mvkNR4r+7bFFoWqk7QwhA5CESAoBiFkVoyiBw8e0UkjH+QMAAIz0JNMFuTnS4fGN+PONLQpNZl3LZNMAkhBJIQAxgUoi5hQqyLWJdtJ5/UFp7PCO+vMDAIDMYVUmF7tGJ+YZG563qJbhYwCSEEkhADGrYhQ5R39OoezsrEiF0tbwOH8AAICRTQqNTswzLrzs/XaSQgCSEEkhANLh6ZF2T0/Cho+pknBgZpV0AwAAjGRSqGQUOsIKnDmROYzqSQoBSEIkhQDEDB0b6VU49pQU2spk0wAAYITosvBNnd5RHT5mt4ViKyqFACQjkkIAIpNMW+XNiWAFZixLDwAARrpKqNCRI057aKn40VpQQ5NCQRbUAJBkSAoBiCxHPy68OkYiK4WsEmsAAIB4s+KM8SWjF/MUOEJJIU9PQFq7Q8P1ASBZkBQCIHXhcubqBFYKRYaPMacQAAAY4Uqh8aMY8+RkZ0l+bqgqiSFkAJINSSEgw+nKX4u3tprLnV6/ZOna8AlQEh4+tqPbJ51eetEAAEB6JIWs4WqqvtNjYi8ASBYkhQBIc3jCRZc9cR8Jjhxb1LxCVAsBAID4s+YuHF/iGtXnLc0LVUQ/s2K7PL1iO4khAEmDpBAAaXWHKnNKwgFLokwodsX04gEAAKRDpVBReJh8Y4dH2t2+UX1uAOgPSSEgw+nSrF1ef8y8PokyITzp41ZWIAMAAHGmK39ZSaEJo1wpVBRegazTE4q5ACBZkBQCMpw1ybROgpjI4WPRpdxUCgEAgHhr6vKZFcB05Fb1KK4+pqwh8h3hjjgASBYkhYAMVxtOwOhyqYmaZFplRfXasSw9AACINyu+KHTmyKvrmkc17ilyhqqxWUwDQLIhKQRkuNq2UKVQoSO0VGqiFLrssjUcrH1c38EEjAAAIG40rqhpC8UZRY4c6fCM7rw+xdbwMa9fAsHgqD43APQn9OkEIGPVte2sFEq03HBeqqXbJ/5A0FQPAQAADDchpCt+vbBqu7lenIA5FAucWpGt8xpJZC5HAEgGVAoBGa42khRKbKVQaB9yzDj/QFBke3iuIwAAgOHSFb8aO7wJW1gjOytLCnJDHXDt4VVfASAZkBQCMlxkfH0SVAppwGTtByuQAQCAeGoLJ2MSUSlkzWWk2j0khQAkj4QmhV588UWZPn16zOm73/2u2bZy5Uo555xzZPbs2XLWWWfJ8uXLYx77zDPPyPHHH2+2L1y4UJqbmxN0FEB6zCmUDMPHopds3drCZNMAACB+rGRMIiqFlNXxRaUQgGSS0KTQ2rVr5dhjj5U33ngjcrruuuukq6tLLr74Ypk3b5489thjMmfOHLnkkkvM7WrZsmVy5ZVXymWXXSYPPvigtLW1yRVXXJHIQwFSks8fkIYOa6Lp5EgKUSkEAP2jUw0YvJ5A0EzyrKgUAoAkSQqtW7dO9t13X6msrIycioqK5NlnnxWHwyGXX365TJ061SSA8vPz5fnnnzePu+++++Skk06SM844Q/bbbz+58cYb5dVXX5UtW7Yk8nCAlKPz9uj8PTnZWeKyZydZpRBJIQDoDZ1qwOC1uUOrjeXaEhfzWDGONYwNAJJBwpNCU6ZM2e32pUuXyty5cyVLp+gXMecHH3ywLFmyJLJdAx7LuHHjpLq62twOYPCTTGuQYr3fEo3hYwDQPzrVgMFr7Q4PHcuzJyzmoVIIQDJK2HiRYDAoGzZsML1bd955p/j9fjnxxBNN+XNDQ4NMmzYt5v7l5eWyZs0ac7m+vl6qqqp2215XVzfo/UiS38FJw2oP2iUz2siaT8hKxCRD+1gB0zZTKRRMmmRVPKXTa2ik0Eb9y7T2yZTjHExS6PDDDx9Up9qCBQvM9osuuqjXTrWJEyeO6jEAo621O1QpVJqXm7B9YE4hAMkoYUmhmpoa6e7ultzcXLn55ptl69atpvTZ7XZHbo+m173e0DKSep/+tg9GeXnhMI8kPdEumdFGrT215ryswCF5eQ5xOnLFJ9nidOSY87xgVr+39bbdFQz9GHE4ciUv0P/je7ttbGm+eXyH1y+2PKeU5ScueBtp6fAaGmm0Uf9on8yTDJ1qmZiky7REbLq1h+5ni5UUivN8QoNpC6vjS+c26gkExJYqDZimr4uRRFvEoj0S0xYDfY6EJYXGjx8v7777rhQXF5uerBkzZkggEJAf/ehHMn/+/N0SPHrd6XSay1oa3dt2l8s16P1oamqXYHCYB5NG9IWjPzJol8xoo3W1reY8354tXV0ecbts4nb3iF0C5ryry9vvbb1t7+726rIe4vF49/j43m7zenxS4LBJh8cvy9Y3yIHjiiTdpNNraKTQRv3LtPaxjhfJ0amWyf8WmXzsqd4eHd6AOa8sdorTaRenwyb+rOzIuU+yzO3Rtw10m3I47Hu8f5lkiS07S/yBoHhtOTKxLE/SUSq9LkYabRGL9kjOtkjockMlJSUx13X8u8fjMWPjGxsbY7bpdat3a8yYMb1u18cNlgbTmRBQDxbtkhltVBMePlYcx+FjVpsMp210qVhNCm3Z4ZYDxqZfUiidXkMjjTbqH+2TeZKhUy1TkpGZnIhNt/bIzs6S5s7waqv2bHG7fWILBsTt8UfOTQeVIzvmtoFsM51hxQ7xeHx7vL/H45d8u03aPD3y8ZZmcQVCq6Gli1R7XYwk2iIW7ZGYthhop1rCJpp+/fXX5ZBDDjG9WpZVq1aZRJGOh//www9NibTS88WLF5vlU5WeL1q0KPK42tpac7K2AxiY2lZrounELM3aX1JIsQIZAOxOY6Xo+dZGu1PNSkZm2imTjz3V2yMQCEbNKRTfmCe6LQYi32Ez53VtnoS3S6a/LmgL2iMT2mIgEpYU0mVStcfqpz/9qaxfv96sfqGrYHzzm980Y+N1mdTrr7/eLLuq55o80hUz1Je+9CV58skn5eGHH5bVq1ebVTaOOeYYJkkEBkGLqBs6wpVCroQWDfadFAonrQAAIXSqAYPXqsPT/aH3RXGc5xQarPzcUFJoe3soBgOAREtYUqigoED+9Kc/SXNzs5x11llm2dQvfvGLJimk23TyRA1crNUy7rrrLsnLy4sklK655hq5/fbbTYJIS6hvuOGGRB0KkHK0jPrBD7eJxkc6tr0gvBpGsigJJ6m27qBSCACi0akGDL0yOs9uE7stYT9/DCvmIikEIFkk9JfgPvvsI3/5y1963TZr1ix5/PHH+3ysJov0BGBoQku+hxIwybbse0m4tJtKIQDovVPtl7/8pelUy8/Pl3PPPdckhfSzXDvVrrrqKnnooYdk+vTpvXaq3XLLLdLa2ipHHHGEXHvttYk+JGDE1YTjCV3IItGoFAKQbJKrPADAqGkLj623hmolE2ufmjq90u3zi8ue+CAOAJIFnWrA4Kqja8MJmMIkqIy2KoV0TiEASAaJrZ8EkNDx9cmaFNIkkBW4bWuhWggAAAwtIfT0iu3y3qYd5noyDJenUghAsiEpBGSoNispFOdVOOJlYmloiWRWIAMAAEPV7vZFFtZIhuFjVmKqpdsnbl96LUkPIDWRFAIyVGRp1iSsFFITSpzmnHmFAADAcGgCJlmGj+XassRuC83lSLUQgGRAUgjIUMleKTShhEohAAAwPMFgUFq6QzFPoTPxSSGdEN5KTpEUApAMSAoBGagnEJR2T/LOKRRTKURSCAAADJGnJyDenoC5XBCezyfRrOQUSSEAyYCkEJCBdGx9ICiSnZUcvWb9Vwq5zUSRegIAABjKwhp5dpvk2JLjpw+VQgCSSXJ8MgIYVbXheXoKcnMkOysrqZNCtW1u+ccHW+TZVfUkhgAAwKC0uUPzCRW5kqcTjEohAMkkeT4dAYyaGisp5EyOMureVBbkmskYvf6gbGvpFhsJIQAAMMQ5FIudyTNcnkohAMmESiEgA9W2eZJmFY7eaPpHS7ytaiEroAMAABiMtvAk01QKAUDvSAoBGUiHZKmCJE0KFbrs8q+V9eIKTwhJUggAAAxFqzV8jEohAOgVSSEgg4ePJWulkGrr9kpRuCeNpBAAABje8LHkiXms+KbT65eO8GqwAJAoJIWATJ5TyJG8cwqpsrxcc05SCAAADIUVQyRTpZDdlh1JDFEtBCDtkkLNzc3x/pMA4sgfCEpdOABJ5kohVZYfCuBICgFId8RPQPy1u3vE0xNIujmF1JhChzknKQQgJZNCM2bM6DV42bZtm3zmM5+Jx34BGCGNnV6TGNLFvPLCc/Yke6VQu6dHgsFgoncHAIaF+AlIzByKGu/k2rKTMilkddQBQKIMOGX+xBNPyGOPPWYu64+zhQsXit0eW4ZZX18vlZWV8d9LAHFTGzWfUHZWci/zXpJnNyuR9QSCZtw9AKQa4icg8cPlS1zJM3TMMrbIac6pFAKQMkmhz372s7J161Zz+b333pODDjpI8vPzY+6Tl5dn7gcgedWEe82Kk6yMujc52VlmzH2ru0daukOrhwBAKiF+AhJfKVSShDHP2CKGjwFIDgP+hNQA5rLLLjOXx48fLyeffLI4HKEPMwCpFyAl04SLe6oWIikEIFURPwGJr44uTrJKoQJnjmzY1mYu15MUApBgQ0qbn3nmmbJp0yZZvny5+Hy7/1A744wz4rFvAEZAbVso+LBWvUh2WvK9SbqlpYvJpgGkNuInYHTVhGOeZBw+piuQKSqFACTakH4V3nPPPfKb3/xGiouLdyuBzsrKIqgBUqLXLHWSQopKIQCpjvgJGF3JPKdQYdSS9DrfmH4GAEAiDOlX4Z///Gf50Y9+JN/4xjfiv0cARmX4WHGqDB8jKQQgTRA/AaOrzppTKC/5Yp4CR+hnmKcnIK3dPUm5jwAyw5DWZvR4PPK5z30u/nsDYEQFgsHI0qcpM3wsHCSRFAKQ6oifgNHT4ekxcxIm45xC1mIaebk2c5khZABSLil02mmnyT//+U9T6gggdTR2eMXnD4otK7QkfSpVCnX7AtIeDu4AIBURPwGjpy48n5AzJ1scOUP6yTPirFjM6rADgEQY0q/Cjo4OeeSRR+SZZ56RCRMmiN0em32/995747V/AOJoW3hs/dgip2Rnp8bYdQ3k8uw26fL5ZfOOLpkxpjDRuwQAQ0L8BCRgtdUknkNR5xXSKiEqhQAk0pA+JadMmSKXXnpp/PcGwIja1tptzscXOyWV6KTYmhTa1NxNUghAyiJ+AkZPKsyhaFUKkRQCkHJJocsuuyz+ewJgxG1rCQVIE0pckkqKnTlS2+aRTTu6Er0rADBkxE/A6KlpTf45FHeuQBaKzwAgEYb0KXnFFVf0u/2GG24Y9N+8+OKLpaysTH71q1+Z6ytXrpSrrrpKPvnkE5k2bZpcffXVcuCBB0bur6XXN998szQ0NMiRRx4p1157rXk8gL5tDQ8fG1+SapVCoV6+zTtClU4AkIpGIn4CsIfhY8mcFKJSCEASiMusaz09PbJhwwZ59tlnh5SY+de//iWvvvpq5HpXV5dJEs2bN08ee+wxmTNnjlxyySXmdrVs2TK58sorTY/bgw8+KG1tbXsMtACkdqWQ0uFjAJAuhhs/KY2X/vd//zdyXTvVzjnnHJk9e7acddZZsnz58pj7a6fa8ccfb7YvXLhQmpubh30cQHInhewpUClEUghA4gwpdd5XT9Y999xjKnsGo6WlRW688UaZOXNm5DYNjhwOh1x++eWSlZVlEkCvvfaaPP/887JgwQK577775KSTTpIzzjjD3F8ff+yxx8qWLVtk4sSJQzkkILPmFCpxSmOnJwUrhbrMqj36uQAAqSae8VN0p9qZZ54Z06mmq5xp5fX9999vOtVefPFFycvLi3SqafX1fvvtJ9dff73pVLvzzjuHfWxAstFh59a8hMmqKFwpVN/hFX8gKLYUWQQEQHqJ6/qMJ554ogk8BuPXv/61nH766WaImGXp0qUyd+7cyA8/PT/44INlyZIlke1aRWQZN26cVFdXm9sB9K7L65fmLl9KVgppebV+HOiy9I2d3kTvDgBIouOnPXWqTZ061SSA8vPzTaeaiu5U06SQPl6TStqpBqRbzNPS7Uv64WP5DpvYssQkhJq7iG8AJEbckkLaO/XQQw9JaWnpgB/z9ttvywcffCDf/va3Y27XeYKqqqpibisvL5e6ujpzub6+vt/tg6E/NDnFnmiX9GyjmrbuyFCskryRK6WObp940Z4za/UQnVco0W2Zqa8h2ii5TpnWPulqKPGTolMN2PPQMe1UcuTYJFllZ2VJRYHDXGYIGYBEGVLqXHuXehu+ob1T11133YD+hsfjMRNJ//znPxenM3bS2+7ubsnNzY25Ta97vaEMutvt7nf7YJSXs7x1b2iX9GujRds7zXmhyy5vbW4Vl8shXskWpyNXfJItecGsyGWnI2dAt/W23RUMfTY4HLmSF4jP39TbKgodptev2ReUiorUavt0eQ0lAm3UP9ontcQjforuVHv66aflF7/4RUynWnSSyOo0W7NmTVw71dI5SdeX6EQskr896sIJlupiZ9K3xbgih0kI6WlmtaS0ZH9djCbaIhbtkZi2GOhzDCkpdO+99+7yZFlit9tNIFJQUDCgv3HbbbeZ1cSOOuqoXoOjXRM8et1KHvW13eUa/JCYpqZ2CQYH/bC0pS8c/ZFBu6RfG63assOcF9ht0tTWLW53j3R1ecXtsu122S6BAd3W2/bubq9IiUs8Hm/c/qbeVuwI9fSt2NwsjVMH16OebFL1NTSaaKP+ZVr7WMeb6uIRPyVDp1o6/FsMVSYfeyq1R/uaJnM+qSJfXK5ccTps4s/KFp9kidNpN5et2+KxTTkc9kH/LTWlskCWbGuTlh46vdIRbRGL9kjOthhSUmj+/PnmfOPGjbJu3ToJBAKy1157DTigsSZHbGxsNCuLKSsg+fe//y2nnnqq2RZNr1u9W2PGjOl1e2Vl5aCPRYPpTAioB4t2Sb822hpezn2kJ1y02iTebVMaHvK2aUd3SrV7Or2GEoE26h/tk1riET8lQ6dapiQjMzkRm+rt8UlNqzmvcOWYzipbMEfcHn+o48mRbS7bgoGY86FuM51hxQ7xeHyD/lu6eEZlOC7TfW5sbJdUluyvi9FEW8SiPRLTFgPtVBvSr0NrCfiXX35ZiouLxe/3S2dnp3zqU5+S22+/XQoL9/zEf//7381SrJbf/OY35vyHP/yhvP/++3L33XdHVhnS88WLF8ull15q7qPLqC5atMisRKZqa2vNSW8H0LttraHx9SM5n9BIKsvbOacQAKSieMRPydCplsnJyEw+9lRqj9pwzFNdNPLDx4bbGTY+vPhHTYs7KdsynV4XiUBbxKI9krMthjTRtI571/HnusrFu+++GxnXrpMl9rXc6q7Gjx8vkydPjpx0dQw96WVdhUMDJ10qde3ateZcS6J1xQz1pS99SZ588kl5+OGHZfXq1WaVjWOOOYbl6IGBJIXCEzanmrK83Mhx9PgDid4dAEhI/KSdavqYJ554wpyOO+44c9LL2jn24Ycfms40ZXWqWZ1mVqeahU41pPty9ONGYU6h4Shw5sjGHV0xcRoApERS6D//+Y+Z2HDvvfeO3Kbj4XV8u/Z+DZeWUd95552RaiBdFeOuu+6SvLw8s117x6655hrTq6YJIu1tG2gwBWQiXeq0JlIplLxLs/anwGETpz07dCzhYA8AUkk84ic61YCBrz6mkzgnu1xbdmSfNcYBgNE2pF+HOiY9O3v3fJIO9dJS6KH41a9+FXN91qxZ8vjjj/d5f00WWcPHAPSvvsMjPYGg5GRnSYEjNZNC+vkyqcQlnzR0ysbmLplUOviJ5QEgkUYifuqtU00notZl7qdPn95rp9ott9wira2tcsQRR8i111477OcFkonb55fmLl9k9bHFW0PzCyUrjcuys8TEaQ0dHhk7CkPeACDakH4dapny1VdfbeYBmjRpUmTSRC2LPvroo4fyJwGMoG0toR6z8cVOyU7htSCnlOeZpNCm5i6RqeWJ3h0ASHj8RKcaEKsuXE2cn2uTwhToCNO4rMhpl5ZunxlCRlIIQEoMH/vRj35kertOOOEEOeSQQ8xJS5Z1GNfPfvaz+O8lgGHZ2hKanHl8SWoHGnuVhXq7NzSFxt8DQCohfgJGXk146JhWCWkVXioodoaSV8wrBCARBp0+37Rpk1RXV5uJDj/++GOzpKoGOFOmTJGpU6eOzF4CGJYt4aTQxPAKF6lcKaQ2NrMCGYDUQvwEjO58QmMLk38+IUuxyy6yozsy/yMAJGWlkK5goeXNOlmhrmyhdKz6ySefLI8++qhZBlVLmK0VLwAkD2sZ94kpPg+PVSmkcwrxWQMgFRA/AaOrpjU0fKy6xCXZOllPCih2USkEIAWSQvfee69ZQlVX/Jo/f37MtjvuuMPcrmPY77///pHYTwBxqBRK9cmZJ5XlmckY2z090hSeRBIAkhnxEzC66sKVQju6fPLSJ40pMYSs2Gk351QKAUjqpJCuYqHj3Y899tg+J0/84Q9/SFADJJlAMChbwxNNTyoNVdqkKkdOtpkjQG1kXiEAKYD4CUjM8DFnTpZ0eHwpVSlEUghAUieFtm3bZla06M+hhx4qW7Zsicd+AYiT+naPeHoCYsvOknHhhEoqm2JNNq0rkAFAkiN+AkaPDherCa8+pit6pdScQiLS2OkVt8+f6N0BkGEGnBQqLy83gU1/6urqpKSkJB77BSDOQ8cmFDslN2dICw4mZVLILEsPAEmO+AkYvYTQY8tqpanTa64X56VOUsiZky35uTZzuTac1AKA0TLgX4if/exn5dZbbxWfr/cyzJ6eHrntttvkyCOPjOf+ARimLeGhY1op9MrappQYW98flqUHkEqIn4DRU9Ma6gjLtWWZREuq0NhsfLiae1v4GAAg6Zak//a3vy1nn322LFiwQM477zw58MADpbCwUFpbW2XFihVy3333SWdnp9x4440ju8cAhrTyWF6uLWXG1g9sWXqSQgCSH/ETMHraunsiw7FSrRNM50z8pKGTeYUAJG9SqKioyEyW+Jvf/MYsndrdHfqhqUuoanCjS6t+5zvfkYqKipHcXwCDtCWcFCp2DvjtnpSywqXhe1eEkkL1HV7p8PRIgSO1jwtAeiN+AkZPmzuUFCoJz9GTSsaXhFaItRYHAYDRMqhfUzre/brrrpOf//znZkLEtrY2c9ukSZPEZguNgwWQnJVCRSmeFCp02eVfK+ultcsjBQ6tevLLph3dcsDYwkTvGgD0i/gJGB2t7lBFdEkKzSdkmVTqionbAGC0DOlXYm5urkydOjX+ewMgroJRY9OLU2gVjr60dXulpcsnpS67SQrpsvQkhQCkCuInYGSlcqXQ5LJQUmjTDobHAxhdqTMDG4BB0aFWD3y4TXz+oNiyRPId6dMbXZafa87XM9k0AADYJSlkLfGeSiaXhobH65xCPn8g0bsDIIOQFALS2ObwZMxaRp2dYhMu9qcikhTqTPSuAACAJNHaHR4+5kq9IfOVBbmSZ7dJIMi8QgBGF0khII3t6AoFR6WuUBIlXWjgpNY3khQCAAAibp9fOr1+c7k0L/XiHpstWyZZQ8hYYRXAKCIpBKSxlnCPWWkKTrjYn/JwpVBNm0c6vaFScQAAkLlq20LVNXZblrjsqfUTp8CZI08tr4tc39zCZNMARk9qfWICGJTmLm9aJoXyc22RIWQbmFcIAICMZw25KnLkSFYKDplvd/eY4WNqUzNJIQCjh6QQkMaaO30xlTXpQpentyaRZLJpAACwrTWUFCp0pt58QhZrLiRWIAMwmkgKAWmq2+eX1vAqHOmWFIoOnNYxrxAAABmvJjzkqtCRukkhq8OLSiEAo4mkEJCmNocDCh1Xn2pj6weissBhzkkKAQCAreFKoaIUrhQqDu+7zglpraQGACMt/X4pAjA2hleu0ORJKo6t35OK8ApkaxspsQYAINNtC88plMqVQnZbthQ4wvMK7aBaCMDoICkEpClrAmZr+fZ0Yw2Ja+r0SksXvWkAAGSqYDAoNWkwp5AqywvFNyxLD2C0kBQC0rxSyKqoSTeOnOxImfW6JoaQAQCQqXS4VZfPby4XpHClUPSKsZupFAIwSkgKARkwfCxdWQmvdQwhAwBAMn3lMR16lZOd2kPmy8JJIYaPARgtJIWANOQPBCNlx+k6fExVhIeQradSCAAAyfT5hIqdoYRKKisNDx/bGJ4GAADSOim0adMm+cY3viFz5syRY445Ru65557Iti1btsgFF1wgBx10kJx88snyxhtvxDz2rbfeklNPPVVmz54t559/vrk/gJDaNrd4/UGxZWdJSbjHKZ0rhdY0kBQCACBT1bSFk0Ku1B46Fh3bbN7RJZ6eQKJ3B0AGSFhSKBAIyMUXXyylpaXy+OOPy9VXXy1/+MMf5OmnnzaTxS1cuFAqKirk0UcfldNPP10uu+wyqampMY/Vc92+YMECeeSRR6SsrEy+/e1vm8cB0MkJuyPj0rPTcOUxi1UFtbahUwK8/wFkCDrVgD4qhVyp3xFWkGszcyb6g1QLAUjzpFBjY6PMmDFDfvGLX8iUKVPk6KOPlsMOO0wWLVok77zzjglSrrnmGpk6dapccsklJrjRBJF6+OGH5cADD5QLL7xQ9tlnH7nhhhtk27Zt8t577yXqcICksiE8dKw8jauEVHlerplwWieX3BoOCAEgndGpBuxuW2uoM6wkDYaPFbrskSFkaxkeDyCdk0JVVVVy8803S0FBgQlGNBn0/vvvy/z582Xp0qWy//77S15eXuT+c+fOlSVLlpjLun3evHmRbS6XSw444IDIdiDTWZNMl4Xn3ElX2dlZMq0i31z+uL4j0bsDACOOTjVgd9Zy9EVpMHwserJphscDGA1J8cl53HHHmd6rY489Vk444QT55S9/aZJG0crLy6Wurs5cbmho6Hf7YKTxyJohsdqDdkntNtqUwEqh0W6f6WMKZEVdu3xS3yGf269SUkEqvIYSjTbqX6a1T6Yc52A61ZR2qi1evNh0ql111VXD6lQ75JBDEnA0wPD1+ANS1+4xl0tcdgkEUr/yrbIwtHLs2gY6vABkSFLolltuMT1f2uulvVbd3d2Smxtb4aDXvV6vubyn7YNRXl44zL1PT7RL6raR/kjYGF7GtLqsQJyOXPFJtuQFsyKXnY6c3W7b0/aBPsYVDP16czhyJS8Qn7/Z13Z9poP3csljS2tlfYtbKiqS898k1V5DyYQ26h/tk9kS1amWiUm6TEvEplJ71LZ7RPNAOpw8P9cm7e6elG+L6IU0kqGNU/F1kWi0RSzaIzFtMdDnSIqk0MyZM825x+ORH/7wh3LWWWeZxE80Tfg4nU5z2eFw7JYA0utFRUWDfu6mpnZhKH3sC0d/ZNAuqdtGDR0eaenyiS1LJN8WFLfHK253j3R1ecXtspnLdgnsdtuetg/0Md3dXu2qE4/HG7e/2df2QmeObGkMVUUt3tQszc0dKdFDmOyvoWRAG/Uv09rHOl4kR6daJv9bZPKxJ2t7LAvHAXtV5EtenkP82TZxOu3iz9LOJVvMuU+yRmSbcjjscXueCeWhofHNXT4JOnIjlUOpIhleF8mCtohFeyRnWyQsKaRBjJYrH3/88ZHbpk2bJj6fTyorK2X9+vW73d/q3RozZoy53tsY+8HSYDoTAurBol1St42s8ecTS/Mkxzb604ZZbTJabZNnD1UMdXj8Ut/uMZNPp4pkfQ0lE9qof7RPZktUp1qmJCMzORGbSu2xfNMOc56bnSVuty/UeeTIFrfHL7ZgIOZ8JLaZzrBih3g8vrg9j9/nNyvI7ujyybsfb5dDp5RKKkim10Wi0RaxaI/EtMVAO9USNtH01q1bzYoY27dvj9y2fPlysxKGjn9fsWKFuN07VxPSCRR1+VSl53rdogHQypUrI9uBTKbLs6tplaFepnRnt2VLcXhiSSabBpDutBPspZdeirktulOtt06zPXWq6eOGkozMtFMmH3syt4c1j2KiJpmObot4qggvFqKdfYlu41R8XSTDibagPYJJ0BYDkZ3I3i2d3PAnP/mJrF27Vl599VW56aab5NJLLzUrkI0bN06uuOIKWbNmjdx1112ybNkyOfvss81jtSdMJ1bU23W73m/ChAlMkgiIyLrGUFJoasXOiUbTXXk4cPp4O0khAOmNTjUg1qbwPIpWEiVdVIbnFWKyaQAjLWFJIZvNJnfccYdZ+eKLX/yiXHnllXLeeefJ+eefH9mmEyIuWLBAnnrqKbn99tulurraPFYTQLfeeqtZYlUTRS0tLWZ7FjNXAbI2PLZ+WkWBZAorEKRSCEC6o1MNiLXZWnE1zZJCFQWheYRYlh7ASEvoRNNaxnzbbbf1um3y5Mly33339fnYo48+2pwA7OQPBGVDUyh42KcyX+o7dvYWZ0SlEEkhAGnO6ji79tprTaeadq5ZnWraOabbtKNNO9U0luqtU01XKdPb58yZQ6caUlqX1y/1Hd5ILJACa00MulJoQ3OX9PgDCZknEkBmSIrVxwDEx5aWbvH6g+LMyZbxJaGJRTNBeZ7dnG9tcUub2ydFztB1AEhHdKoBO+Me5bJniyvXJp0ev6SLYmeO5Osxef2yvqlL9q3KnApwAKOLlDOQhvMJ7V2RL9kZ1PPrtNukxBVKBK2oa0/07gAAgFFgTTJdmkIrjw6UVvDNGBtaNWglsQ2AEURSCEjDlcemlmfOJNOW6uLQ2PvlNQROAABkgs3hSaZLwx1D6eYAKym0ndgGwMghKQSkkbWNmbUcfbRxRaHhch/VtiV6VwAAwGgmhcLDyNPN/pFKIeZMBDBySAoBabkcfeYlhaqLnZHhY4FgGs00CQAA+k0KleWld6WQdvp5egKJ3h0AaYqkEJAmun1+M9GympaBSSFdpcORky1t7p5IkAgAANJTMBiUTTusOYXSMyk0tshhhsbp6rJrGqgWAjAySAoBaVQlpPUxGjyka49Zf2zZWZEy649qGEIGAEA629Htkw6PX3RZjeI0nVNIJ5veOYSMeYUAjAySQkCaWLU91IO035gCE0RkopnjQoHT8loCJwAA0tnm5lBV8Lhip9ht6fuTZv+xoaXoV27vkOzszIzvAIys9P0EBTLM6vDKFDPGhIKHTDSzusicM9k0AADpbUN4OfpJpS5JZ/uPC8U2b29olqdXbCcxBCDuSAoBaWJ1uFJo+phQtUwmJ4V0KF2X15/o3QEAACNkbUNocY190njFVU0AWbFNc5dPmjo8id4lAGmIpBCQBnRFinVNoR6zTK4UqixwyJhChwSCugoZ1UIAAKSrNeEVV/epTM+4p8CZI08tr5MPt7VJoSPH3La9naQQgPgjKQSkAV2qVFemKHbmyNhCh2Syg8aHetQWbWlN9K4AAIARWnlMq4LTfcXVdnePdHh8Mq44FNvVtIZWmQWAeCIpBKTVfEKFGTvJtOVTk0rM+QebWxK9KwAAYATUd3ilzd0jtiyRvcrzJN2NLw7Nm0RSCMBIICkEpIGP60O9ZTPGFpjx59YpE80LJ4WW17WLuyeQ6N0BAAAjNJ/Q5LI8yc1J/58z1SXOSFIoEAwmencApJn0/xQF0pwmf97dtMNcDkiWPLe6QR5YvFVeWduUUVVDWeG2mFyeLyUuuxlOd/fbmzI2OQYAQLpa09CR9kPHolUVOMSWnWU6uzaG55AEgHghKQSkOG9PQOraQuXE5fk50tbtlZYunxmDnkkKXXb518p6kwybGF6edlVdaFgdAABIr7kU1bQ0XnksmiaEqgpyzeUl21hIA0B8kRQCUty6pk6z2pbDlm0qZDKZJsQ0GTa5LJQU2ryjO9G7BAAARigplM7L0e9KV1dVS7exkAaA+CIpBKS4lbWhapiKgtyMGi7Wn0nhSiFdurXNnVkVUwAApDOtkN7Y3J1Rw8eik0LLaqgUAhBfJIWAFGcFB1ZZMUQKHDlS4soxlz/YTI8aAADpYnNLt5k3sMBhiyRKMoF1rFoF3dzlTfTuAEgjJIWANEkKZVJgNBDVRaGVOt7Z2JzoXQEAAHGgi0c8tKTGXC7LyxWbLXN+yjhysqU8PzRNwDLmFQIQR5nzSQqkoR1d3si8OVUkhWJYk02/sb5ZgizfCgBAWtgSjntK8zJvHsXxxaHYZilDyADEEUkhIIV9FJ5PSHuOtAcJO1UXO8WenWXmFfqkITQhJQAASG3bwyuuameYVg7pKZNiG8W8QgDiiV+RQAr7qDYUFIwPBwnYKSc7K7IK2evrmhK9OwAAYJh0LqG6dk9kOfqnltfJS580ZsxCG/tUhSbWXlHXLj5dehYA4oCkEJCitGfslTWhZMf4klDyA7GmhpeqfX098woBAJDq1jd2is8fNJXAuupqu7tHOjyZs8qoDpnLy7WZ5NjKulC1OAAMF0khIEX5/AHZ2tIdU06MWHuX55lzDZwaO0I9iwAAILWHzWtCKDtDqoOiaUWUVR2+dBurqwKID5JCQIr6pL5DegJBcdmzpSwDJ1sc6NL0B4wtjEw4DQAAUtfy8LD5TF5x1eoIXMoKZADihKQQkKKslSd0la1MGUs/FEdNLTfnrzGvEAAAaZEUqizIlUxlTRmwtKaV1VUBpH5SaPv27fLd735X5s+fL0cddZTccMMN4vGEhnhs2bJFLrjgAjnooIPk5JNPljfeeCPmsW+99ZaceuqpMnv2bDn//PPN/YFMsmRrqGx4UnjpdfTumGmhpNC7m3ZIh6cn0bsDAMNG/IRMpN/h6xu7zOWqgsytFNIqKV1Mo7W7RzY1h6YRAICUTAppZlsDmu7ubvnHP/4hv//97+W///2v3HzzzWbbwoULpaKiQh599FE5/fTT5bLLLpOamhrzWD3X7QsWLJBHHnlEysrK5Nvf/jbZcmSMQDAoi7aEkkJ7hefNQe90dZLJpS7x+oMMIQOQ8oifkKlWbW8XfaUWOXPMZMuZypadFRk+x9L0AFI6KbR+/XpZsmSJ6d3aZ599ZN68eSbIeeaZZ+Sdd94xPVfXXHONTJ06VS655BLT46UBjnr44YflwAMPlAsvvNA8Vv/Gtm3b5L333kvU4QCjal1jp7R0+8Ruy2LlsT3QoXXHT680l1/6uCHRuwMAw0L8hEy1PDzJ9NiizK0SsowvCc8rVMNk0wCGL0cSpLKyUu655x7TmxWto6NDli5dKvvvv7/k5e2sgJg7d64JgpRu1yDI4nK55IADDjDbDznkkEHtB1Ox9N4etEtyt5FVJaQrUGiPUTJJhvax6C7YbFnyuRmV8qd3NstbG5ulu8cvefbE9jAmUxslK9qof5nWPplynKkUPwGJSgpVF7HiqjXZ9BImmwaQykmhoqIiMw7eEggE5L777pNDDz1UGhoapKqqKub+5eXlUldXZy7vaftglJeHViZCLNoludtoaV2HOZ9aVShOR674JFvyglm9XnY6cgZ0W7we4wqGfr05HLmSF4j/fgxmPytLXPLi2mZp6/bJmCKHbG/zyB1vbZZfnTVLkgHvsz2jjfpH+2SeZIifMjFJl2mJ2GRrDx02bw2VGhdOiGTya0M7BfXpNu/olqZOj1QkyRxLvE92oi1i0R6JaYuBPkfCkkK7uummm2TlypVmjPtf//pXyc2NXVVAr3u9XnNZx9H3t30wmprahaH0sS8c/ZFBuyRvG/kDQXlnfaO5PK7QLm6PV9zuHunq8orbZdvtsl0CA7otXo/p7vaKlLjE4/GOyH4M9m+2u3tkR6dXZowpMEmh9zc0SXNzhwQCwYx9DaUC2qh/mdY+1vEiOeKnTP63yORjT2R7rKxpM8PmdS6hKVWF0uX1i9NpF3+WdgbZYs59kjXq25TDYR+9fXDaZca4IllZ2yZrWr2y35TYysFE432yE20Ri/ZIzrbISZaA5m9/+5uZLHHfffcVh8MhLS0tMffRgMXpDPUM6PZdAxi9rr1ng6XBdCYE1INFuyRvG63e3iEdHr8UOGwyJkl6hqJZbZJsr58DxxXJK2uaZGNTl7R2+6QgN/Eff7zP9ow26h/tk9kSFT9lSjIykxOxydYeLyzbZs7nTCgWr8cnXd0+cTuyxe3xiy0YiDk3nUKjuM10hhU7xOPxjdo+5DtsUuQMDYV/dVWdHDY+OX5c8j7ZibaIRXskpi0G2qmW8F9F1157rdx///0msDnhhBPMbWPGjJG1a9fG3K+xsTFS8qzb9fqu22fMmDGKew4kxqItoYD/4Aklkp1k8wkls6rCXKnIz5XGTq+8/EmjnH7g2ETvEgCkZPyUycnITD72RLbHB5tDsc/8yaWSbBLVGWatQKbzTCbba5L3yU60RSzaIznbImGrj6nbbrtNHnjgAfnd734np5xySuT22bNny4oVK8TtdkduW7Rokbnd2q7XLVoOraXT1nYgnb0fDow+Nakk0buScquQ7T8ulCl/buX2RO8OAAwZ8RMySY8/IIvDC2wEw9/nEJkQXn12Q1OXNHcNfgoNAEh4UmjdunVyxx13yEUXXWRWxtDJD63T/PnzZdy4cXLFFVfImjVr5K677pJly5bJ2WefbR571llnyeLFi83tul3vN2HCBFbOQNrz9gTkw62hwGgeSaFB239sQaRXra5t548mAEgVxE/INCu3d0iXzy9Oe3ZkyBREXHabVBaE5gizkmYAkFJJoZdffln8fr/84Q9/kCOPPDLmZLPZTMCjAc6CBQvkqaeekttvv12qq6vNYzWAufXWW+XRRx81gY6On9ft9Bwg3WlCyN0TMMOg9qnMT/TupJwip10mloTm1nhhdUOidwcABo34CZnm/c07zPmkEhev1T6qhRaHOwwBYCgSNqfQxRdfbE59mTx5sllitS9HH320OQGZ5K2Nzeb88L1KCYyGaMbYQtnS4pbnVtXL+fMnJnp3AGBQiJ+Qaaz5hCaWhhIg2GlCidN0GC7eGjvBPACkzJxCAAbnzfVWUqgs0buSsqZX5YvdliVrGztlTUNHoncHAAD0we3zy7KaNnN5UhlJob4qhdY1dskO5hUCMEQkhYAUsbWlWzbt6BZblsj8Scm3+kaqcNpt8ump5ebyMyuYcBoAgGT13uYW8fqDMrbIIaUue6J3J+nk5dpk3/B0AtZCJAAwWCSFgBTx1obQmPpZ44ul0JmwkZ9p4dTwcvTPraw3q5oAAIDk89raJnN+9LQKhs334ZApoY7CdzaG4kQAGCySQkAKyM7OkrfD8wkdsXeZua4nDJ622pFTy6Q8P1d2dPvkjfCQPAAAkDz8gaC8vj6UFDpmWqjCF7uzphR4d9MOCQaDid4dACmIpBCQ5DT58+TyukgPUFCy5IHFW+WVtU30mg1Bocsu/17dKHuX55nrz6xkCBkAAMlmeW2bNHf5pMBhkzkTihO9O0mpwJkj29rcYsvOkvoOr5lmAAAGi6QQkAKW17RKTyAoRc4cybNnSUuXTzo8vkTvVspq6/bK5PAqJm+sa5KmTiZnBAAgmby2LlQldMReZWK38ZOlL25fQMYWOszldzYxhAzA4PEJC6SANfWd5nzG2AKqg+KkNM8u44oc4g+KPEu1EAAASeXV8HxCx+xTwZD5ASxNr5hXCMBQkBQCkpxWCOny6Wr/sYWJ3p20MrO6yJw/vqxWAozDBwAgKWxs7jJDoXKys6TV3SMvfdJIp1g/xheHkkKLtrSIt4cFNAAMDkkhIMl9uLVFun0BceRky+Sy0Dw4iI8ZYwokP9cmW1rc8v4mlnIFACAZvPRxgzn/1KQS8fkDDJnfg7I8u1meXoeSLa1pTfTuAEgxJIWAJPffNaHyaZ0DRycSRPzk5mTLqQeMMZcfWVqT6N0BACDj6Qpaz6+uN5dP3D/0HY3+aRXVXuGOw9fWsaoqgMEhKQQkMR3S9N9PQr1lU8KrZSG+zjqoOjKh5fZ2T6J3BwCAjPZxQ6dsag4NHdO5hBg2NjDTKvPN+StrGlmaHsCgkBQCktiqunazxKjdlhUZL4740TBzn6oCOXhCsQSCIk8sq030LgEAkNGeCy/+MKnUJT0B5scZqMllLnHas6Wu3SOr6zsSvTsAUghJISCJvRAeUz+1PN/0mCG+Cl12+dfKehkfXrXjsWW14vb5E71bAABk7OIa/14VGjo2rYIK6cGw27LliL3KzOX/rmlM9O4ASCEkhYAk5dfAaHVDZCl6jIy2bq9U5edKkTNHmrt88i+WpwcAICE+2LxDmrp8puJlQokr0buTco7dp8KcvxKejxIABoKkEJCkPtjcIk2dXil25shezCc0onTOgnmTis3l+z7YahJyAABgdD2zItQxM72qgMU1huDIqeWmsnxDc5dsaOpK9O4ASBEkhYAk9dyqUGD0uRlVBEajYGZ1kRS7cmRri5uyawAARllrty/y/XvAuMJE705KKnTkyKcmlZjLxDIABoqkEJCEun3+yFL044qdrLwxCnJt2fLFOePN5Xvf38LKHQAAjKLnVteL1x+UcUVOc8LQKp8/t1+Vufz0ijqzii0A7AlJISAJvba2Sbp8fjN0rCLfnujdyQiadjt37ngzj8Gq7R3yylrG4wMAMBq0I8ZaAfSgCUV0hg1BgTNHnlpeJ75AUPJzbabyWaciAIA9ISkEJCH9Ulf7jy0kMBrFlcje2tgih04pNddvf2ODsBAuAAAj76PadlnX2GXmw5k5vijRu5Oy2t09Ys/JMvGjevyjUDwJAP0hKQQkmY3NXfLe5hZTuTKzmjH1o70S2RF7l0lerk02NXfL9S98YkqxAQDAyHkynLzYtypfXHZboncn5c0IJ4V0XiFdtAQA+kNSCEgyjy4NlU8fObVMil0MHRttTrtNDgtXC738cYN0ef2J3iUAANJWu6dHnl0ZWlxj1vjQSqAYnqpCh4wrcpjVVK0V3QCgLySFgCSiE0w/syLUW/bFg0OTHmP0HTSh2Kzg0en1y51vbkz07gAAkNZVQj2BoIwpdMj4YiaYjueqqurhJTXi7WFAPIC+kRQCksi/VzdIh8cvJa4c8fiDzCeUILbsLDl8r1C10D8XbZWVde2J3iUAANKOVrJo0kLNn1JC3BNH+40pkKqCXNne7pFHw5N4A0BvSAoBSbTyxkMfbjOXp1cVSJe3J9G7lNEmlbpkxpgCCQRFrnvhE+nx08sGAEA8vbG+SWpa3eLMyY5UtiA+7LZsuejwKebyn9/ZLJ3ElQD6QFIISBKvr2+WNQ2dYrdlyb6V+YneHYjIsfuWS7Ezx/y7/OW9LYneHQAA0soDH4aqhDQhpEkMxNfnZ441nVwt3T7556JQxyMA7IpPXyBJqoTueXuTuTxnQrGZ7BiJl5+bIz/8zDRzWf99Fm1pSfQuAQCQFtY1dsoHm1tEF/k8iGXoR0ROdpZ864hQtdB97281Q8kAICmTQl6vV0499VR59913I7dt2bJFLrjgAjnooIPk5JNPljfeeCPmMW+99ZZ5zOzZs+X888839wdS1Vsbd8iq7R3itGfLvEmsvJEsdGaDUw8cK6cdOMYMI7vyX6tZ2hVA0iB+Qiqz5hI6elqFFLHa6og5bt8KmV1dJF0+vyx8ZJkEE71DAJJOwpNCHo9H/ud//kfWrFkTUzWxcOFCqaiokEcffVROP/10ueyyy6SmJvTloee6fcGCBfLII49IWVmZfPvb3zaPA1K5Sujs2dWmOgXJodBll3+trJc5E0pk7/I8kxD66bOrmV8IQMIRPyGVtbl98q/wUunnstrqiMrOypKrT97PTE+wqblb/sZweADJlBRau3atfOELX5DNmzfH3P7OO++YnqtrrrlGpk6dKpdcconp8dIARz388MNy4IEHyoUXXij77LOP3HDDDbJt2zZ57733EnQkwND9Z02jLK9tF0dOtpz3qYmJ3h3soq3bK/acLDl15jgTUGmp+69eXsuPKAAJQ/yEVPf08u3i7gnI1Io8mTuRCumRkp2dZU6Ty/PkuH0qzG1/fHOjLN3WmuhdA5BEEpoU0iDkkEMOkQcffDDm9qVLl8r+++8veXl5kdvmzp0rS5YsiWyfN29eZJvL5ZIDDjggsn0wdOVLTrEn2mX02qjL1yO//e868/fOmzdBKgtzJdVFt086ceZkmYBKD+vJj+rk7x9sTYrXUDqfaCPaZ9fjRfLET0A8lqE/9+AJYmOC6RFR4MyRp5bXyT8XbZWXPmmUA6uLZN+qfNP+P3hihWxq7kr0LgJIEgkdp/LlL3+519sbGhqkqqoq5rby8nKpq6sb0PbBKC8vHPRjMgHtMjptdMfTK6Whw2t6cH54yv5mgmmXyyFeyRanI1d85jzHnOcFsyK3RV8eyvaRfIwrGPr15nDkSl4g/vsxEsc20L+53/gSybLZ5PkVdXLraxtk8pgiOXvuhIS+htIdbdQ/2iczJTp+ysQkXXQiFsNrjzc3NMs2XYbeni3tHp9JWGSlcMMm82uj3d0jbd0+kyDSNj5xRpXYsxtlRV27fO+x5fKXLx8kZfm5GdEWo422iEV7JKYtBvocSTl5SXd3t+Tmxn5A6XWdUHEg2wejqaldGAUS+8LRHxm0y8i30cf1HfLXtzaYyz88Zm/pau8Wd3aWdHd7pKvLJ26XTdzuHrFLwJx3dXkjt0VfHsr2kXxMd7dXpMQlHo93RPYj0X9z1th8qcibYFbxuPyRpeL3eOUz+1aa8mxLQGelHoXXUDqjjfqXae1jHS+SI37K5H+LTD72eLSHDr3+x0PLzOWDJ5VKVk6O9GSJOJ12cTps4s/Kjpz7JMvcHn1bsm5TDoc9afcvsk3s8tdvHCIL/vCmbGnulu8/sVLuv+hQKc6L70TfvE92oi1i0R7J2RZJmRRyOBzS0hK79LMGLE6nM7J91wBGrxcVDX45Sw2mMyGgHizaZWTbKBAMyq9eXGNWtDp+eqUcOa1CnlmxXQocoZ6cVGa1Sbq+fnTy6emuXJlZXSgf1bTLlc+slrwFOdJieuO8UuTKlZNnVO0xMaR4n+0ZbdQ/2geJiJ8yJRmZyYnYkWqPD7e2yuLNLZJry5KZYwukq8sjbke2uD1+sQUDMeemcyYFtpnOsGKHeDy+pNy/6G2alLP7e+SWBQfKN/65RFbWtslX7n5bbj9nZlwWOuF9shNtEYv2SExbDLRTLSmTQmPGjDGTKEZrbGyMlDzrdr2+6/YZM2aM6n4CQ6EVJU8srZWPwpNLT6vIk1fWNkm726c/8RK9exgA/beaP6lEvD0B+bi+U374xAo5c9ZYKXQk5UcqgAwxWvFTJicjM/nY49Eef303tPLVaTPHmo4wHdqU6lKpM0yHkem8iHm5OXLu3PHy57c3m8VOvv/YCrn17JniiNP8TrxPdqItYtEeydkWSTmz2+zZs2XFihXidrsjty1atMjcbm3X6xYth165cmVkO5DMCaGHltTIb/4Tmlz6iL3LzIR/HZ7UD4oycYnXUw4YY/4NPT0BeWxprTR0eBK9WwAyGPETkjn+WdfYaeYT0nroaZUFKV8Znap0niGNOzUxpHMMaQelVnCdf99i8UswZjg8gMyQlEmh+fPny7hx4+SKK66QNWvWyF133SXLli2Ts88+22w/66yzZPHixeZ23a73mzBhglmJA0h2z3xUa5IIY4sccvAElmFNZbbsLLnx8/ub5XS9/qA8t7JBtreTGAKQGMRPSEaaZHh6xXb5+XOrzfV9qwrMJNNIvMqCXPnKpyaI3ZYl6xu75Py/L5bHP6ojMQRkmKT8RLbZbHLHHXeYVTIWLFggTz31lNx+++1SXV1ttmsAc+utt8qjjz5qAh0dP6/b6XFAslu0pUVW1HWYy5+fOZYv3RSn/3p5jhz5v7NmSrXOJ+APyMMf1sjahs5E7xqADET8hGS1vrFDPqkPfTceOqU00buDKBNLXXLm7HGSk51l/o0eWrRVegYwLyKA9JE0E2B8/PHHMdcnT54s9913X5/3P/roo80JSBU+f0BueHGNuTxjTIH5EtYSXqT2pNP/Wlkv+bk2OWdOtfzj/W3S2OmVbz20VP74hdmyV3leoncRQJojfkIqeHN9szk/YFyhVBY6Er072MWk0jz54tzx8sAHW+WThk65+rnVctWJ081QeQDpLykrhYB0dN8HW2VDU5fk2W3yqUklid4dxImuOKZj8x05Njl5/yqpKsiV5i6ffOvhZbKpuSvRuwcAQEKtqG2TdY1dprr2mH3KE7076MO0ynwzAbgWsT+7st50ZOpqZQDSH0khYBRsa+2WP72z2VzWgEgn9UP60X/XLxxcLdMq8qWp0yvffniZbG3pTvRuAQCQMHe8sTGSdKgooEoomekE4CftP8Ykhp74qE5++991JIaADMAvU2AE6ZxBWnmrX6o6ufS8SSWy/9iCRO8WRpDLbpM/fGGWGTpW3+E1FUN1TD4NAMjQYWPvbNxhkgwsrpEa9htTIFedtJ+5/OCHNXLzq+tJDAFpjqQQMIIJoWdX1ct1L3wir69rFluWyPHTKyU7m7ddOtPy+IpCh5lTaHKZS+raPGaZ13qWqwcAZNhcir97ZZ25fPDEEilyJs1UpuhHgf47mZi1wlz/56Jt8ksdShaObVkkBUg//DoFRlBju1teXN1gLn9qcqnkO2yJ3iWM0uTTL31SLxccOllKXHbZ0eWTSx9aJk1dXoIpAEBGeHhJjWze0S1leXZWHEsxuhDKXmV58rn9KiNDyb7+zw/l3vc2y9MrthPLAGmGpBAwgt7c0CydXr+U5tnlsL0IiDJp8umWLp/o1FHnzq02iaEtO7rly39bJA8tqSGYAgCkNV2J8+63N5nLl316b+ZSTFEzq4vkl6ftL7bsLFlR2y4PLK6R7W3uRO8WgDjjExoYIau2t8uiza3m8mkHjhG7jbdbJipy2uXCwyZKoSPHrEr257c3mR44AADS1U0vr5UOj1/GFTkl35kjWSxtnrJOmFEld3xhlknsbW/3yP2Ltson9R2J3i0AccSvVGAE+ANBuf7fn5jx11PL82SfKiaXzmSlebmmYshlz5aGDq98/7Hl0u31J3q3AACIu/+saTQnrS7ReWm6vHSEpPL8Qk8tr5M2j1++NHeCFDhs0tLdIxf840NzO4D0QFIIGAE6RGjV9g7Tq8I4eliJoZNmVIkzJ1s+3NoqC/7wpvhZzQMAkEba3D658eW15vLXD5koVYUsQZ/qtLq5w+OTioJcOXPmWLO6qq6oe+2/P5H/fXqlNHd5E72LAIaJpBAQZ3VtbvnjGxvN5aOnlUteLpNLI6Q8P1e+On+C5GRnyaradrn6uY8lQGIIAJAGdISYJoSaOr3m+27vygKGjaUZp90mZ84aKwuP2stUgr38SaN88a+LTNWQVskDSE0khYA4++1/10mXzy+zxxfJrOrCRO8Okszksjw5fdZYEzw/u7JefvffdRIkMQQASGG6gML1L66Rf69uMKtVnTijSjw9DJNO11VWq4oc8s3DJsu+VfnS0u0zVUPn37dY3t+8I9G7B2AISAoBcfTquiZ5ZW2TCYhOmDFGsrN5i2F3Uyvy5fTZ1ebygx/WyA0vraGHDQCQsra1dMu/wnPMHL1PhYwrdiZ6lzDCQ8rGFjvki3MnyGf3qzRzDX3S0Cnfeugj+ebfPpCNzV2J3kUAg8AvViCOw8au/ffH5vKs6iIpdDJsDH2bNaFEfnbCvqKF9Y8vq5Of/muVGaMPAEAq8fYE5IqnV4nXH5QxhQ45cmpZoncJo0QXzTh87zL59qf3koMmFJkq6JdWbZdz/7rIVM63eXpMFZl1ApCcchK9A0A66PEH5Mp/rZbW7h4ZU5grB08oTvQuIclpaLRgTrUUOHLkymdWyUufNMqWliXyq9NmyIQSV6J3DwCAAfn9q+tlRV27WUjh2H3KJZt5hDJOICDyqYklcsTeZfKfT5pkVV27PLB4mzz5Ua18bkaVWYm3yJUrpx0wRgJURgNJh0ohIA5ue32jLKtpk/xcm5x24Fgz+R7QH00GPbOiXho7PXLe/IlS4rLLx/Ud8tW/L5ZnV2438wzRuwYASGbPr6qXR5bUmI6Okw8YI4UO+pszWUWBQ86aO0HOmz9BKgtypdsXkCeX1cnDH9ZITWt3oncPQB9ICgHD9Nd3N8s/Fm01l686cbqU5tkTvUtIEW3dXmnp8klFvl3++bW5Mru6SDq9frnquY/l+48vN6+rBxZvlWdX1ZMYAgAklUWbdpgJhtVR08pl74r8RO8SkoS+FrTD6/jpFaajdEuLW+59b4u8tq4p0bsGoBckhYBh+OeirXJ7ePn5hUdOkc9Mr0z0LiEFabpHJ+W889zZctmn9zJL1r+1YYf833/Xyb9X1kt9G71rAIDksWVHt1x07wdmLry9K/Lk6H3KE71LSDI6jPCIqeVy/vyJpvPL7QvI/3tsufzmP2vNPFQAkgc1nsAQ6DKrv35prTy8pMZcv/jwyXLhYZOp5sCQl3f918p6ae3yyNTKArngkIny3zWNsq6xS5bVtpsVPew2m5w1a5zk5pDLBwAkdmGN7z66XJo7vWYOvFMPHMs8QuhTeX6ufP7AsbKkpk0Wb2k1q64u2dYmvzxthkxiDkUgKfDrAhikDU1d8oU/vh1JCB2xV6nsN7ZQnlvdYJajzyIwwjCGknV4fFKWnytfO2SinH3QOCl12cXdE5Df/XednPPXD+ShD7dJl9ef6N0FAGSgjU1d8s0HlsqWlm4pdtnli3OrJdfGzwn0T4eQHbtPhdy84MDIHIrn/vUDeeDDbeJn4mkg4agUAgZI53q5+62NpoejJxCUImeOGStdnpcrnd4eaXf3SDDIWwrxoclFHZNf7MyRrS1uWbSlVWpa3XLTf9bJH97cKMdPr5Tj962UuRNLzHAzAABG0psbmuUXz30sLd0+mVLmktMOGi8FDpt0euiowMAcvU+FPDi2UC66f4ls3tEtv/3POnlxdYP86DPTZL+qgkTvHpCx+AUL7IFWb2hPxn0fbDVj59Vn9x8j/+/oveS/nzSY7cBI0ZL8WeOL5GcnTJenl9fJPe9slqZOrzyxrM6cdKWXQ6aUymGTS+XQKaUyttgZeSzLvgIAhku/c259bb0Z5qxmjCmQ286ZKf/d0CISZG4YDEyBM0eeWl4nebk5cs6calld3yEvrW4wq/ee9/fF8tnplXLB/ImyL8khYNSRFAJ6ocuBr6hrl0eW1sqLq+vF6w/9uNahPKfNHCMzJ5XJx/WdDBXDqHHl2uQLB48XfzAgK+s6pL7DIytrO6Td0yMvfdxgTmpMoUMml7nkgHFFcsnhk8XGaxQAMEhaEf1RTZs8+VGtvPBxg/j8QbMowpfmjpdvHTFF8p38hMDgaVV9IBg08fO8SSUyvtgp//2kUdY0dMqLHzeY08xxRXLKAVVy5N7lZhEORScXMLL4REdGsiaEjv6S0SFg2lvx3qYW+c+aRjNUx6K9YvtW5Utlfq5Mrsg3X2p2oXcMoyMr6jWrgZQGUYftXSbHTPPJJ/Ud0tjpNa/J5bXtsr3dY076On5g0VaZP7lUDptSJodO0eCLCR0BALGs7xftZHh7Q7O8vq5J3lzfLK3unsh9JpQ45YQZY8wKmRo7sbAG4qHIaZdjppXLYXuVSm2rx8TfH9W2mZPIWtMZW13ilGOmVcjEEqdMKHbJ+BKnFLnskb/RX8Kot3gfwO5ICiHjaOHE3z/YKtvb3GaSxLUNnbKusVM2N3dJuCDIsNuy5HP7Vck5B1Wb4Tv3L9rKUDEkdHWy/FxbTHWaBjtaGfSpKaWSlZUtXwgG5b2NzbJ6e4fUtrmlw+OXV9c2mZPSZNLs8UWmF27m+CKZVpEvdlv2oIKl6B8CBFkAkNpq2tzyhzc2ytrGTlnf2CnRH+suu02mj8mXo6ZWiCMnW/IdNjP8p63bJ2PpZEAc7VWRb1ZfnTWhWHw9AXltbaMs3dYmO7p95rSitj3m/nm5NjPnYkWBwwyd1/hmQrFTxpe4pDzPbmIljVeeXrHd3P+0A8YQswD9ICmEtKcVFCvqtNehXZbXtpkvlujer2jVxU45eEKxZGeLzJ5QLI6cHPPYHe4ehooh4auT9TeRuW4vdObIjLGFMrbQIRPKXLK+sUtW1rZJXZtHato8sq3VbU7PhueFsGdnRXrgDhhbKDPHFZoAqy8aYD27qt48V5ErV06eUUWQBQApRFd60ljo9fWhiqD1TV27LR++V3mejC3MlTkTS6TbFzDJIGsyaY2pNCmk88NIFquOIX70taUTl2c7c+Qr8yfJkU2dsrahw3RwaXyzZUe3WXhDJzrXVVj1VNvmMcMco7ns2aYyWqvbunx+qSp0yEHVRVJd5CCWB/pAUghJZbhlnjoGXqt+Vta1h8pPa9plY3NswGMtjak/nMvy7Ka3YVKpS755+BSpyM81+/DPD7aYJVatZcKLXFQIIfUmqNYqotzsYindR+fCGmcCp/ve32JW/NAhZ25fQDY1d8vf3tsSeVxVQa5Mrcg3Pwr08fqeGFPklMqCXBNYWe8JAEBy6S2Gcvv8pgpIK0g/2NIiH2xuiekYs2WFOsS0Q2FiqUsmlLpMAqi1y8sQMSR03iGn3SYTS1xSXeoyMc1x03Oky9tjLuvQ+ZqWbjO+vqHdaxJFPn/ADJ/XRKa+5vVkeWb5dlNtPb2qQGZWF8ms6iI5aEKxlLjsdG4BqZ4U8ng8cvXVV8sLL7wgTqdTLrzwQnNCarKqENRAKhC0t0uHyOjkdDqXilb06OTQ+kN3V/rDVktKK/LtpjRV+7ZK8uzmi2dHp1cmlefJ+1tapbXLI9UlefQkIO2Gn726rtkEREdOLTevea0k2tzcLc1dXtmyw22Sp00dXqkPn97euKPXv+XMyTZDCopdOeZHhs6zVZ7vkBJXjunJ0yBOJ7e25kHKycqSHFvopHMHaACmSVmCMCAxiJ3Si3aGbd3RLbXtbvn36gZp1SoeR47UtrpNjNTQ4ZVdP221GmNKWZ7pEDtkr3Jpd/tiqoGAZE0U6WtUX6vaaeW0ZZmEkXWbJotyc7KlpsUt21p0Sogs2dDYaRJFO7q80un1y+KtreYUXRk3f1KJ6QjTBJTGKPq3vD0B83vC4w+IuydgqpI6PT3i9QckJzvLDL3XjuXKAodUFeZKZb7DPDeQqlI6KXTjjTfK8uXL5W9/+5vU1NTIj3/8Y6murpYTTzwx0buGPdCcS3OXz1T1rGvoNOXL+qNUT7rs+29fXmvGr+u8PvrB67Dp5WzJzckyH9Jtbp/54WotER9NHze2yCFlLrscUF1kPuDHFDkiCSD9ENfLu6IqCJk0/EyDJw2GppTnyT6VofeGVs0dNKHElGtrBVFDh0caOr1mnol2t1/8waAJjvSkY/w3NncPej80WaTPM7bIad6LVeEKJA2szCnfboawlebZzSqA0ZjPCBg+YqfUpD+INcGji2Csb+qUT+o75eOGDjMvYm+xULRCR475vNd5V755+GQ5YFyhPLh4mxkGFmBJeaQJje01oaO/A8rzck3CaJ/KfHObhhPtHr+sb+wwvzvq27UDzCNNnV55LtwhPVwa02jV3bgipzmvLnbIvuNLxRn0S1WBw8xjqp3OxC9IRimbFOrq6pKHH35Y7r77bjnggAPMac2aNfKPf/wjLQMb/XGkX/p68gVCl7t9/vBtflM1k5OtSZPsUK98dpbprbey2bnhBEu2hG7T++iPwpHqtdJMuu6fZtbr2twmS6/zmtS1e8yYYK0w0HG+fRtYb1WuLUtK80Lj3012PzfbVAJpr4FVAdRbAgjA7ioKHbKtzWPev3Mnl5peNa0uqm3tluYOj1QVO6VOe5/DK/PppJD17R5zanP3mJVrNMBSGvPo55LmcfQzoccf+lzQUEj/rkkI72F/9LNKE0h60v3QzzutMsrPzTGTZJvbHTbJywmd6+16P729QC+b20K3x7sHb7RXNEn2hBgrvKSGTIud4k0/0zo8PSYxrjFOp9sfinV8oXM9acdV5Dav3haIbNfPMP081GEu1ueiOQ8EIpc1+RNN31LucDzV17tL47uKfIfk5WZLWX6uWbGpqsgpwYAuIx+UqVUFkWqKzS3d0tjloyIaGUXfP0VOm+xTWSBV+aGEUaMmWVu6zUgCjUl0zlF9f+vvLP1NoZXRel1pJbQjx2Zu0xhG3z66TTuT9bq+d7WzW086eqGv96lWTE+tyDNJIk0c6VQW5rzIIUUOu5kPKceWHdcYIdnjBySHlE0KrV69Wnp6emTOnDmR2+bOnSt//OMfJRAISLbOFJwAOjdHaBnFUI+4fulab0X9ANHvev3C1/dkUELn+gbVQEN/MFmJn9iTX7zRy2LFiY4j1w8eK3FkkkXZWZKVnSU9+uPN7GMoIRU63+Wy/hfeLeu6LxzUDJSpFCh0mBUE9qkqMM9bmp8rHp8mdXxSUZhryj91v/RDV6sdqgqdoWAmGJAZ44rMKmL6YW1VAo1UsgvIBNEVc/qesqqL9D2nw8a0ikcTzppw1RXPtOLnlPCqHtZ8XLpUbPTQTOvy+FKXNLR7TAn2IVPKTDKpscsrb6xrkh1dPvPZocklDbT0Y8S6rqdd6bxhg6GfIfqDyCSLIomm0GVNGGmyyUqm60eIqYgK/8Bz91jnoc9kfyAgbbqPgVCQp585eq6J+dC5Jt6zzUTe1u2aiNd5yvS6Ppe2p37ehX4ghn4U6rn1Y9H6LA19X+gPUU3+a9l6thm6p3/DmZtjfjHqZWv/9aSfx/r39GtDn0O/X/RvWec9UdeVeXxW6G9oLKrHY9rCOoVXcdGhgNZtGmOa5wr/aNVhKrqfM8YUyv8cM9W0L5JPssZO+tp5ZEmN6Tgy16OKV0JRx+6icyfR97AqDE18FQy9zvUmrXTUbfq+iNx/lwSMxUxiq0mf8GS2+pnVGf48SCT9bNJqHx32pe/hSWUuKXbaJSsYlPFleZHET/S5zg3U2zAcINNpTGOmlMjKksnl+XLAuCLzfrHmMMrLzTG/Mazb+np/jStxSlNnaE6j1u4e87lR3+42SSePP2iGrunniH6va8eZ1XnWF/1uddpDoyT2RN/J+qtH4xPrsj428iGn8Yxv920a9+jx63e1nmt1lfVdb33HaxvoucYrtsj10G1WHGPtQyAqXrF+25pPS+t3b/g+9lybuE38FL5f1GP089m6TUViDys+i4lVdsY9u95mbg/fpvu66+/WyG9b67LVlsHQt03o+8fap3DDRR1D6L7h/czSmCt0m/muCR+zea7I8+58vujPXntujnhMvBu6TavavnHoZEmUlE0KNTQ0SGlpqeTm5kZuq6ioMGPlW1papKysbEB/R+OfeH036ofBPxdvM+NQ4yk3R3+07LxugnFN4tjEvOideiGccNLn7gmGEjr6xtVst38AiRrrR8LghH48mUvmPPSDInbfs2VckcNkxMcU5oonEJQxhU6Tac/NFplYlm8CL/OhWuwyl/PstgHdppf1R1Z5gcNs13bSD4cSV+5ul3vbPpTH6GWtTPJJltglGLe/ORL7Ge+/OdDH6I9e/dEaCDhS5thG/W+G5+TZtY1S7dhC780ceXtTi7R7vFJZ4JSKQmefj9G5h1y5OWZ7MDtbnE6bzKkolQmleTHvbU1+HD2tXGyOXNne1CEvfdxoAgMd6tDa7RWnPcdUJbV3+8yHT0WBXTo9AbO0sk5CqV/MVo+9BmEWf0DMBKt9rT44FKGEvQ6rM88gI02TPe52K6AM/YBOJjWtHjlr9jgzPCWeyPWnb+ykNjZ2yx/e3CTJTBOyBY5QkJMVjm90SLsmf7WCQIe/6g8sHVqrP7L0R5S2UUm+3dw/PzfH/DBw5dpMVZB2+JUVOMTvD5jPRY0h8+054vH7pcvTI2X5DpOQ1hhPfzR4e/wyqTzfdOLpD7umDo95XLdXVwPrkZK8XHHk9Jjbos/N5/AIbNNYqEerzyU4rL85Uvs3mttM3OO0i/j95vsp2fYvnttG63Uxmtv0PaQdN2X59si23m7r85jzHWY+RetvWeeagJ1YUSCtnW6xZWdLfbgSWzvHNGGkv9F0guyWLq+JS6LjFaUJpYGKLkLw9AT3uE0/Z3Q4HZLH+5tb5Etzx5s54RIRP6VsUqi7uzsmqFHWda+3/wxstLKy+Aaui3/22bj+PQDIZGOLXTJ778pE7waQFpI1diovL5TlV58Q178JAAAGJmWnSXc4HLsFMNZ1XU0DAAAAOxE7AQCAtEkKjRkzRnbs2GHGxkeXRWtQU1RUlNB9AwAASDbETgAAIG2SQjNmzJCcnBxZsmRJ5LZFixbJzJkzEzZRIgAAQLIidgIAALtK2QjA5XLJGWecIb/4xS9k2bJl8tJLL8mf//xnOf/88xO9awAAAEmH2AkAAOwqK2ito5aiEyZqYPPCCy9IQUGBfOMb35ALLrgg0bsFAACQlIidAABA2iSFAAAAAAAAkGHDxwAAAAAAADB0JIUAAAAAAAAyEEkhAAAAAACADERSKMPoFFIXXnihPPbYYzG379ixQ77zne/InDlz5LjjjpMnn3wyZvvKlSvlnHPOkdmzZ8tZZ50ly5cvj9n+zDPPyPHHH2+2L1y4UJqbmyXV6TFPnz495rRgwYLI9i1btpjJOQ866CA5+eST5Y033oh5/FtvvSWnnnqqaRNd2UXvn+48Ho/85Cc/kXnz5smRRx5pVrXJNC+++OJur5vvfve7Gfs+iub1es174t13343b++ivf/2rHHXUUeazS197OoluOrXPddddt9vr6b777hvQa0Y/73/zm9/IoYceKvPnz5cbb7xRAoHAqB8XkAqIj3pHLNS/TIp7iG+IY3ZF3CKyfft28z7Q/dV/xxtuuMF8LqTca0MnmkZm8Pv9wWuuuSa47777Bh999NGYbZdccknwa1/7WvDjjz8OPvTQQ8EDDzwwuHTpUrOts7MzeMQRRwR/9atfBdeuXRu89tprg4cffri5Xen9Zs2aFXz88ceDq1atCn71q18NXnzxxcFU9+STTwZPP/30YH19feTU3NxstgUCgeBpp50W/MEPfmDa5I9//GNw9uzZwW3btpnten7QQQcF//SnPwU/+eST4Pe+973gqaeeah6XzvT1pe2yfPny4AsvvBCcM2dO8LnnngtmkjvuuMO8n6JfN62trRn7PrK43e7gwoULzefPO++8E5f30fPPPx+cO3du8D//+Y9pv5NPPjl49dVXB9OlfdQFF1wQvPPOO2NeT11dXQN6zWi7HX300cH3338/+PbbbwePPPLI4D333JOQ4wOSGfFR34iF+pdJcU+mxzfEMbGIW4Lm3/ELX/hC8Jvf/Kb599X9/uxnP2veC6n22iAplCHq6urMG++YY44Jzps3Lybo2bRpk3lDb9myJXLbT37yk+CPf/xjc/nhhx8OHnfccZEXqZ7rC976Gz/60Y8i91U1NTXB6dOnBzdv3hxMZb/73e+C//M//9Prtrfeesu8ka0vPKVB4y233GIu33zzzaa9LfphqIFC9IdmutG2mDlzZswx3n777THtkAn0w/+3v/3tbrdn6vtIrVmzJvj5z3/efDlGBw/DfR99+ctfjtxX6ZexBhtW8JHq7aOOOuqo4Ouvv97r4/b0mtHAKvqz/oknnggee+yxI3osQKohPuofsVDfMi3uyeT4hjgmFnFLiCZ79PgbGhrCtwSDTz/9tElmpdprg+FjGWLFihUybtw4efTRR6WwsDBm29KlS822CRMmRG6bO3eufPjhh5Htej0rK8tc1/ODDz5YlixZEtmuZbMW/VvV1dXm9lS2bt06mTJlSq/b9Nj2339/ycvLi9ymbdRXm7hcLjnggAMi29PR6tWrpaenx5Q4RreJtkWyl36OxusmU99H6r333pNDDjlEHnzwwbi9j/x+v3z00Ucx27U81+fzmddiOrRPR0eHKUvu73Oor9eMPq62tlY+9alPxbTttm3bpL6+fgSPBkgtxEf9IxbqW6bFPZkc3xDHxCJuCamsrJR77rlHKioqZNd2SLXXRs6I/FUkHR0Hr6feNDQ0SFVVVcxt5eXl5s1pbZ82bdpu29esWWMu6xu1t8fX1dVJqn/56Zf6aaedJu3t7fLpT39aLr/8cikoKOizzaxj3tP2dKTHXFpaKrm5uZHb9ENSx9W2tLRIWVmZpDutvtywYYMZM3znnXeaD/UTTzzRjDXO1PeR+vKXv9zr7cN5H7W1tZnXVvT2nJwcKSkpSbk266t99DNIg+s//vGP8tprr5lj+/rXvy5nnnnmHl8z2nYqersVtOj2XR8HZCrio/4RC/Utk+KeTI9viGNiEbeEFBUVmTl/LPpZqfMn6ZxIqfbaICmUJtxudyRI6S2LGZ2l3JVOWhX9hab0uk4eNpDt+tz9bU/FNtMvcp3sS3sHf/nLX5o3p04c9qMf/Uj+8Ic/DLvN0lFfx6zS+bij1dTURNrh5ptvlq1bt5oJ9/S1lq7vo+EYzvtI28u63tfjU9369etNcLX33nvLV7/6VXn//fflZz/7mfkx9tnPfrbf10xv7ZNp70dAER/1j1ho6DIp7iG+6R1xTKxMj1tuuukmM+n6I488YiaJTqXXBkmhNKElaDpreW9uv/12M8t7XxwOx24vML3udDqHtV3L4FK5zd555x1zbHa73dz2q1/9yqyooMGT3q69QINtE80op6u+jllZ7ZLuxo8fb1ZgKC4uNl+KM2bMML0GGkDrqgTp+D4ajuG8j3SbdT1d2+yMM86QY4891vQMqf322082btwo999/vwmu+nvNRAdSu7ZVurQPMBDER/0jFhq6TIp7iG96RxwTK5Pjlptuukn+9re/ye9//3vZd999U+61QVIoTei4zo8//nhIjx0zZow0NjbG3KbXtQetv+1WSdueHp8ubTZ16lRzroGQHvPatWsH3Sb6JZqu9Jh16V4dX68ljlZppH74pUsAOBDWF2H060ZLQPX9kI7vo+EYzvtI21m/NPW69d7U155+AadLm2ngvevrSXvf9Efanl4zus16D1rzoVil2enSPsBAEB/1j1ho6DIt7iG+2R1xTKxMjVuuvfZak/jSxNAJJ5yQkq8NJpqGmbhKJ/GKHqO4aNEic7uaPXu2mVRRxxMrPV+8eLG53dqu97foJGF6sranIn0T68SBWjZtWbVqlfnSnzx5sjk2nZzSKu9T2gZ9tYmWCGo5YSq3yZ7oh5i2T/QEktoGM2fOlOzszPioef31102Arf/e0a8b/XC3JifNpPfRngznfaSvKX1tRW/X156+BrVnKh383//9n1xwwQUxt+kEgxpg7ek1o8GGTt4YvV0v623JOC4fSEaZHh8RC/Uvk+Ie4pveEcfEysS45bbbbpMHHnhAfve738kpp5ySuq+NEVnTDElNl/aLXu5PXXjhhWZZvFWrVgUfeughs8Tm0qVLzbb29vbgoYceGrz22mvNEoR6fsQRR0SW2Fu8eHHwgAMOMI/Tx+vfueSSS4KpzO/3B08//XSzdODHH39slgE8+eSTg1dddZXZ3tPTY65///vfD37yySfBO++80yw7uG3bNrNdl6/VNtTbdfv3vvc9s2yjtVxnuvrZz34WPOWUU8xr58UXXwwefPDBwX//+9/BTKHvFV2KU5fvXbduXfCVV14xy1LeddddGfk+6k300qXDfR8988wz5jWmrzV9zelrT9s1XdpHj2n//fcP3nPPPWZp7H/84x/BAw880LxWBvKa0XbT15/+PT3p5T//+c8JOzYg2REfxSIW2rNMiXuIb3YijomVyXHL2rVrgzNmzAj+/ve/D9bX18ecUu21QVIoA/UW9DQ2Npo3pb44jzvuuODTTz8ds11fjGeccYbZfvbZZwdXrFgRs13/3tFHH21e7AsXLgw2NzcHU11NTY05lnnz5gXnz59v3ogejyeyfePGjcGvfOUr5sNO36hvvvlmzOP1C/Nzn/tccNasWSag2rx5czDddXV1BS+//HLzOtAP8r/85S/BTKMf7BdccIFpAw2Kbr311sgHfCa+j/oLHuLxPtIv08MOOyw4d+7c4BVXXBF0u93BdGofDQY0SNDXzIknnrjbj43+XjMakPzyl780n2GHHHJI8KabbkqrH2NAvBEf7Y5YqH+ZFPcQ34QQx8TK5LjlzjvvNMff2ynVXhtZ+r+RqUECAAAAAABAskqvAa8AAAAAAAAYEJJCAAAAAAAAGYikEAAAAAAAQAYiKQQAAAAAAJCBSAoBAAAAAABkIJJCAAAAAAAAGYikEAAAAAAAQAYiKQQAAAAAAJCBSAoBAAAAwAhZtWqVLF68eI/3CwaD8o9//GPAf/e4446Txx57bEj7pI/Tx6eC4RwngD0jKQQAAAAAI2ThwoWycePGPd7v/fffl2uuuWZU9gkALCSFAAAAACDBtFIIAEYbSSEAAAAAGAHnnXeebNu2Ta644gr53//9X1m3bp184xvfkIMPPliOOuooue222yQQCMjWrVvl/PPPN4+ZPn26vPvuu+L1euWGG24w9zvggAPMMKoHH3xwSPuxfft2+eY3vykHHXSQnHnmmbJ58+aY7Z988onZ11mzZskJJ5wQM4zt1ltvlcsvv1yuvfZamTNnjtmPN954Q+677z45/PDD5dBDD5V77703cv+1a9eaY9T7zpw5U7785S+b41Z6XPr4f/7zn+a4dH9+9KMfmWO1PPDAA3LMMceYNrrjjjuGdLwABo6kEAAAAACMAE2ojB07Vn7yk5/Id77zHZMgqaqqkocffliuuuoqk1jRhMq4cePMfZUmXDShctddd8krr7xibn/++efljDPOMImZxsbGQe/H9773PZN80ue96KKL5G9/+1tkm9vtNrfNnTtXnnrqKfnxj39skjFPPPFE5D7PPvusFBYWypNPPmkSR9///vfNfv797383yaRf//rX0tzcbJ7j0ksvlfHjx5v7aoLH7/fLTTfdFPlb9fX18u9//1vuuecec2wvvPBC5Llef/11uf76683f1wTYRx99ZJJqAEYOSSEAAAAAGAElJSVis9lMQuXll18Wl8tlEjtTp06V448/3iRrNDmi9ykuLjaPqayslNzcXNlvv/1MgkSraSZOnGiSLT6fb0DzE0Vbs2aNfPjhh3LdddfJPvvsIyeffLJ86Utfimx/+umnpby83CRipkyZYip59Lmiq39KS0vNvk6aNMlUGrW3t8uVV15pjkOrgnp6emTTpk0mwXTuueeaqii9r1Y46f21esiix/DTn/7UVERptZCeNPmjNGl12mmnmQSY7usvf/lLcTgccfiXANCXnD63AAAAAADiQodQaZIkJ2fnTzCtCGpoaJC2trbd7q9JozfffFN+9atfyfr162XlypXmdq28GQxNyGhyqrq6OnKbDuvS6iOlf3v16tVmXyz6HJqoskyYMEGysrLMZafTac61Gij6ug4By8vLMwknrfxZvnx5ZL8rKipi9mny5MmRywUFBSapZLWRJpWik1GaEAMwckgKAQAAAMAI663iRYdb9ZXo+f3vf28qZxYsWGAqZ3S42VCXkd91Emu73R65rAmZww47TH7+85/3+fjoRJYlO3v3QSednZ1y9tlnm2SO7uupp55qEkN//vOfY+6nlVB97V9/+wog/kgKAQAAAMAI22uvvcz8OTp8ykp06LCusrIyU8ljVeJYdD6eX/ziF3LSSSeZ69YQrMGuUrbvvvtKa2urGd5lVeisWrUqZr90aJtWA1nVQTofkA7p0mFeg/Hee++ZOYN0SJqVSNK5hwa6zzpkzBpKpjo6Osx+Axg5zCkEAAAAACNEh1RptcynP/1pM8RKK3J0mNRLL71kJlrW4VaaENL5hpQOu/J4PCZR9N///le2bNkiH3zwgVkBTEWv1DUQOu+PVgLpZNc6TEyfVye4tnz+8583cwFZ+/Xqq6+auYx0nqHB0n3u6uoyz6Erqmmlk65kNtB9/upXvyrPPfecPPTQQ2ZfdJ903wCMHJJCAAAAADBCNOmjiRFdXl4nldbl4K2VxL72ta/JZZddZu6nEy8fccQRZk4dTczoJMta0XPKKaeYJe1PPPFEs/JXdJXPQOlQNB3SpX/7d7/7nVkxLHpOn7vvvttMYK37pdVBX/nKV+SSSy4Z9PPovEQLFy6Uq6++2iSbHnvsMZPYaWpqku3bt+/x8fPmzTPtdOedd5phaFpFNWPGjEHvB4CBywoOtv4QAAAAAAAAKY9KIQAAAAAAgAzERNMAAAAAkKJ0dbINGzb0uV2HhumwLADoDcPHAAAAACBF1dTUmBXN+jJmzBhxOp2juk8AUgdJIQAAAAAAgAzEnEIAAAAAAAAZiKQQAAAAAABABiIpBAAAAAAAkIFICgEAAAAAAGQgkkIAAAAAAAAZiKQQAAAAAABABiIpBAAAAAAAIJnn/wMUt1VuKiZMZwAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the data\n", + "file_path = r\"C:\\Users\\waseem\\Downloads\\combined_data\\combined_data.csv\"#change path\n", + "df = pd.read_csv(file_path)\n", + "\n", + "# Convert to datetime\n", + "df[\"date_time_current\"] = pd.to_datetime(df[\"date_time_current\"])\n", + "df[\"date_time_future\"] = pd.to_datetime(df[\"date_time_future\"])\n", + "\n", + "# Sort by forecast time\n", + "df = df.sort_values(\"date_time_current\")\n", + "# filter PERIDID 24\n", + "df_periodid24 = df[df[\"period_id\"] == 24].copy()\n", + "# Sort data by time\n", + "df_periodid24 = df_periodid24.sort_values(\"date_time_current\")\n", + "\n", + "# Print basic dataset information\n", + "print(f\"Data timespan: {df_periodid24['date_time_current'].min()} to {df_periodid24['date_time_current'].max()}\")\n", + "print(f\"Total records: {len(df_periodid24)}\")\n", + "print(f\"Data frequency: {df_periodid24['date_time_current'].diff().value_counts().index[0]}\")\n", + "\n", + "# slicing the data\n", + "start_date = pd.to_datetime('2017-10-07 23:00:00')\n", + "end_date = df_periodid24['date_time_current'].max()\n", + "date_mask = (df_periodid24['date_time_current'] >= start_date) & (df['date_time_current'] <= end_date)\n", + "df_period24 = df.loc[date_mask].copy()\n", + "\n", + "# Create lagged features (12h, 24h, 168h)\n", + "df_period24[\"demand_lag12h\"] = df_period24[\"total_demand\"].shift(24) # 12-hour lag\n", + "df_period24[\"demand_lag24h\"] = df_period24[\"total_demand\"].shift(48) # Daily patterns\n", + "df_period24[\"demand_lag168h\"] = df_period24[\"total_demand\"].shift(372) # Weekly seasonality\n", + "df_period24[\"hour\"] = df_period24[\"date_time_current\"].dt.hour\n", + "df_period24[\"day_of_week\"] = df_period24[\"date_time_current\"].dt.dayofweek\n", + "df_period24[\"month\"] = df_period24[\"date_time_current\"].dt.month\n", + "# 24-hour rolling mean demand (excluding current row)\n", + "# For each hour, calculate rolling mean on properly lagged data\n", + "hour_groups = df_period24.groupby('hour')\n", + "for hour, hour_data in hour_groups:\n", + " # Sort by date\n", + " hour_data = hour_data.sort_values('date_time_current')\n", + "\n", + " # Calculate rolling mean using only data that would be available\n", + " # Shift by 1 since we want to use past values only\n", + " hour_data['demand_rolling_mean_24h'] = hour_data['total_demand'].shift(1).rolling(48).mean()\n", + "\n", + " # Update the main dataframe\n", + " df_period24.loc[hour_data.index, 'demand_rolling_mean_24h'] = hour_data['demand_rolling_mean_24h']\n", + "\n", + "display(df_period24)\n", + "\n", + "\n", + "# Feature Selection\n", + "\n", + "# Define the feature list\n", + "features = [\n", + " \"forecast_demand\", # Original forecast to be corrected\n", + " \"demand_lag12h\", # 12-hour lagged demand\n", + " \"demand_lag24h\", # 24-hour lagged demand (daily seasonality)\n", + " \"demand_lag168h\", # Weekly seasonality (24*7=168 hours)\n", + " \"demand_rolling_mean_24h\", # 24-hour rolling average (recent trend)\n", + " \"hour\", # Hour of day (0-23)\n", + " \"day_of_week\" # Day of week\n", + "]\n", + "\n", + "# Create cleaned dataset with these features\n", + "df_model = df_period24[features + [\"total_demand\"]+ ['date_time_current']].dropna() # target is total_demand\n", + "# Define target\n", + "target = 'total_demand'\n", + "\n", + "\n", + "# Check for any remaining NaN values\n", + "print(f\"NaN values in features: {df_model[features].isna().sum().sum()}\")\n", + "print(f\"NaN values in target: {df_model[target].isna().sum()}\")\n", + "print(df_model.index)\n", + "# ===== Split data temporally =====\n", + "# Use the last 30% for testing\n", + "X = df_model[features]\n", + "y = df_model[target]\n", + "test_size = 0.3\n", + "split_idx = int(len(df_model) * (1 - test_size))\n", + "\n", + "# Split into train/test with proper temporal order\n", + "\n", + "train_mask = df_model.index < split_idx\n", + "test_mask = df_model.index >= split_idx\n", + "\n", + "X_train = X.iloc[:split_idx]\n", + "y_train = y.iloc[:split_idx]\n", + "X_test = X.iloc[split_idx:]\n", + "y_test = y.iloc[split_idx:]\n", + "\n", + "\n", + "# Train Random Forest Model\n", + "\n", + "\n", + "\n", + "\n", + "model = RandomForestRegressor(\n", + " n_estimators=200,\n", + " max_depth=10,\n", + " min_samples_split=5,\n", + " min_samples_leaf=2,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Get model predictions on test set\n", + "y_pred_model = model.predict(X_test)\n", + "# Feature Importance Check\n", + "\n", + "\n", + "\n", + "\n", + "feature_importance = pd.Series(model.feature_importances_, index=features)\n", + "print(\"Feature Importances:\\n\", feature_importance.sort_values(ascending=False))\n", + "\n", + "\n", + "# Model Performance\n", + "#\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# Get original forecast (forecast_demand) from test set\n", + "y_pred_original = X_test[\"forecast_demand\"]\n", + "\n", + "# True values\n", + "y_true = y_test\n", + "\n", + "# Calculate metrics for MODEL\n", + "mse_model = mean_squared_error(y_test, y_pred_model)\n", + "mape_model = np.mean(np.abs((y_test - y_pred_model) / y_test)) * 100\n", + "\n", + "# Calculate metrics for ORIGINAL FORECAST\n", + "mse_original = mean_squared_error(y_test, y_pred_original)\n", + "mape_original = np.mean(np.abs((y_test - y_pred_original) / y_test)) * 100\n", + "\n", + "print(f\"\"\"\n", + "Model Performance:\n", + "- MSE: {mse_model:.2f}\n", + "- MAPE: {mape_model:.2f}%\n", + "\n", + "Original Forecast Performance:\n", + "- MSE: {mse_original:.2f}\n", + "- MAPE: {mape_original:.2f}%\n", + "\"\"\")\n", + "\n", + "#Plotting\n", + "\n", + "\n", + "# Plot a sample of 100 data points\n", + "sample = np.random.choice(len(y_test), 100, replace=False)\n", + "x_axis = range(len(sample))\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "sns.lineplot(x=x_axis, y=y_true.iloc[sample], label='Actual Demand', color='black')\n", + "sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], label='Original Forecast', linestyle='--')\n", + "sns.lineplot(x=x_axis, y=y_pred_model[sample], label='Model Predictions', linestyle='--')\n", + "plt.title(\"Model vs Original Forecast Performance\")\n", + "plt.ylabel(\"Demand\")\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.kdeplot(y_true - y_pred_original, label='Original Forecast Error', fill=True)\n", + "sns.kdeplot(y_true - y_pred_model, label='Model Error', fill=True)\n", + "plt.title(\"Error Distribution Comparison\")\n", + "plt.xlabel(\"Prediction Error\")\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "sns.scatterplot(x=y_true, y=y_pred_original, alpha=0.5)\n", + "plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--')\n", + "plt.title(\"Original Forecast vs Actual\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sns.scatterplot(x=y_true, y=y_pred_model, alpha=0.5)\n", + "plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--')\n", + "plt.title(\"Model Predictions vs Actual\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Performance Improvement\n", + "\n", + "\n", + "improvement_mse = (1 - mse_model/mse_original) * 100\n", + "\n", + "improvement_mape = (1 - mape_model/mape_original) * 100\n", + "\n", + "print(f\"\"\"\n", + "Improvement Over Original Forecast:\n", + "- MSE: {improvement_mse:.2f}%\n", + "\n", + "- MAPE: {improvement_mape:.2f}%\n", + "\"\"\")\n", + "\n", + "# Residuals\n", + "residuals_original = y_true - y_pred_original\n", + "residuals_model = y_true - y_pred_model\n", + "\n", + "plt.figure(figsize=(14, 4))\n", + "plt.subplot(1, 2, 1)\n", + "sns.histplot(residuals_original, kde=True)\n", + "plt.title(\"Original Forecast Residuals\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sns.histplot(residuals_model, kde=True)\n", + "plt.title(\"Model Residuals\")\n", + "plt.show()\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/RF M2-2.ipynb b/src/RF M2-2.ipynb new file mode 100644 index 000000000..031277785 --- /dev/null +++ b/src/RF M2-2.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + " (M2-2)\n", + "includes Temperature\n", + "includes Forecast , actual demand lag and mean , hour and day of week and Forecast error\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data timespan: 2010-01-01 00:01:24 to 2021-03-17 12:00:56\n", + "Total records: 195524\n", + "Data frequency: 0 days 00:30:00\n" + ] + }, + { + "data": { + "text/plain": " id period_id forecast_demand date_time_current \\\n7510461 2017100739 24 6824.10 2017-10-07 23:01:35 \n7510519 2017100740 24 6733.41 2017-10-07 23:31:38 \n7510576 2017100741 24 6665.92 2017-10-08 00:01:42 \n7510632 2017100742 24 6638.01 2017-10-08 00:31:40 \n7510687 2017100743 24 6582.00 2017-10-08 01:01:37 \n... ... ... ... ... \n10853504 2021031713 24 7325.13 2021-03-17 10:01:17 \n10853532 2021031714 24 7271.28 2021-03-17 10:31:09 \n10853559 2021031715 24 7172.65 2021-03-17 11:01:00 \n10853585 2021031716 24 7041.74 2021-03-17 11:31:07 \n10853610 2021031717 24 6955.50 2021-03-17 12:00:56 \n\n date_time_future date_time_current_rounded total_demand \\\n7510461 2017-10-08 11:00:00 2017-10-07 23:00:00 6954.92 \n7510519 2017-10-08 11:30:00 2017-10-07 23:30:00 6928.97 \n7510576 2017-10-08 12:00:00 2017-10-08 00:00:00 6963.49 \n7510632 2017-10-08 12:30:00 2017-10-08 00:30:00 6966.67 \n7510687 2017-10-08 13:00:00 2017-10-08 01:00:00 6939.97 \n... ... ... ... \n10853504 2021-03-17 22:00:00 2021-03-17 10:00:00 7419.77 \n10853532 2021-03-17 22:30:00 2021-03-17 10:30:00 7417.91 \n10853559 2021-03-17 23:00:00 2021-03-17 11:00:00 7287.32 \n10853585 2021-03-17 23:30:00 2021-03-17 11:30:00 7172.39 \n10853610 2021-03-18 00:00:00 2021-03-17 12:00:00 7094.51 \n\n temperature_future temperature_current forecast_interval \\\n7510461 21.2 13.3 0 days 12:00:00.000000000 \n7510519 21.3 12.8 0 days 12:00:00.000000000 \n7510576 21.2 12.7 0 days 12:00:00.000000000 \n7510632 21.3 12.6 0 days 12:00:00.000000000 \n7510687 21.0 12.4 0 days 12:00:00.000000000 \n... ... ... ... \n10853504 19.7 21.3 0 days 12:00:00.000000000 \n10853532 19.5 21.6 0 days 12:00:00.000000000 \n10853559 19.1 21.9 0 days 12:00:00.000000000 \n10853585 18.8 21.8 0 days 12:00:00.000000000 \n10853610 18.6 22.6 0 days 12:00:00.000000000 \n\n demand_lag12h demand_lag24h demand_lag168h hour day_of_week \\\n7510461 NaN NaN NaN 23 5 \n7510519 NaN NaN NaN 23 5 \n7510576 NaN NaN NaN 0 6 \n7510632 NaN NaN NaN 0 6 \n7510687 NaN NaN NaN 1 6 \n... ... ... ... ... ... \n10853504 7695.32 7373.83 6211.99 10 2 \n10853532 7595.23 7345.78 6409.04 10 2 \n10853559 7537.24 7218.99 6566.17 11 2 \n10853585 7511.38 7056.88 7056.38 11 2 \n10853610 7471.58 6999.23 7468.13 12 2 \n\n month demand_rolling_mean_24h forecast_error \\\n7510461 10 NaN 130.82 \n7510519 10 NaN 195.56 \n7510576 10 NaN 297.57 \n7510632 10 NaN 328.66 \n7510687 10 NaN 357.97 \n... ... ... ... \n10853504 3 7522.112083 94.64 \n10853532 3 7512.402917 146.63 \n10853559 3 7273.215625 114.67 \n10853585 3 7267.357708 130.65 \n10853610 3 7010.598542 139.01 \n\n forecast_error_rolling_mean_24h \n7510461 NaN \n7510519 NaN \n7510576 NaN \n7510632 NaN \n7510687 NaN \n... ... \n10853504 -57.879583 \n10853532 -56.128125 \n10853559 -32.638958 \n10853585 -34.120833 \n10853610 5.621042 \n\n[60247 rows x 19 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idperiod_idforecast_demanddate_time_currentdate_time_futuredate_time_current_roundedtotal_demandtemperature_futuretemperature_currentforecast_intervaldemand_lag12hdemand_lag24hdemand_lag168hhourday_of_weekmonthdemand_rolling_mean_24hforecast_errorforecast_error_rolling_mean_24h
75104612017100739246824.102017-10-07 23:01:352017-10-08 11:00:002017-10-07 23:00:006954.9221.213.30 days 12:00:00.000000000NaNNaNNaN23510NaN130.82NaN
75105192017100740246733.412017-10-07 23:31:382017-10-08 11:30:002017-10-07 23:30:006928.9721.312.80 days 12:00:00.000000000NaNNaNNaN23510NaN195.56NaN
75105762017100741246665.922017-10-08 00:01:422017-10-08 12:00:002017-10-08 00:00:006963.4921.212.70 days 12:00:00.000000000NaNNaNNaN0610NaN297.57NaN
75106322017100742246638.012017-10-08 00:31:402017-10-08 12:30:002017-10-08 00:30:006966.6721.312.60 days 12:00:00.000000000NaNNaNNaN0610NaN328.66NaN
75106872017100743246582.002017-10-08 01:01:372017-10-08 13:00:002017-10-08 01:00:006939.9721.012.40 days 12:00:00.000000000NaNNaNNaN1610NaN357.97NaN
............................................................
108535042021031713247325.132021-03-17 10:01:172021-03-17 22:00:002021-03-17 10:00:007419.7719.721.30 days 12:00:00.0000000007695.327373.836211.9910237522.11208394.64-57.879583
108535322021031714247271.282021-03-17 10:31:092021-03-17 22:30:002021-03-17 10:30:007417.9119.521.60 days 12:00:00.0000000007595.237345.786409.0410237512.402917146.63-56.128125
108535592021031715247172.652021-03-17 11:01:002021-03-17 23:00:002021-03-17 11:00:007287.3219.121.90 days 12:00:00.0000000007537.247218.996566.1711237273.215625114.67-32.638958
108535852021031716247041.742021-03-17 11:31:072021-03-17 23:30:002021-03-17 11:30:007172.3918.821.80 days 12:00:00.0000000007511.387056.887056.3811237267.357708130.65-34.120833
108536102021031717246955.502021-03-17 12:00:562021-03-18 00:00:002021-03-17 12:00:007094.5118.622.60 days 12:00:00.0000000007471.586999.237468.1312237010.598542139.015.621042
\n

60247 rows × 19 columns

\n
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN values in features: 0\n", + "NaN values in target: 0\n", + "Index([ 7574338, 7574396, 7574453, 7574509, 7574564, 7574618, 7574671,\n", + " 7574723, 7574774, 7574824,\n", + " ...\n", + " 10853349, 10853382, 10853414, 10853445, 10853475, 10853504, 10853532,\n", + " 10853559, 10853585, 10853610],\n", + " dtype='int64', length=59095)\n", + "Feature Importances:\n", + " forecast_demand 0.988386\n", + "demand_lag24h 0.003162\n", + "forecast_error_rolling_mean_24h 0.002402\n", + "demand_rolling_mean_24h 0.001407\n", + "temperature_current 0.001273\n", + "demand_lag168h 0.001220\n", + "demand_lag12h 0.001070\n", + "day_of_week 0.000583\n", + "hour 0.000496\n", + "dtype: float64\n", + "\n", + "Model Performance:\n", + "- MSE: 53676.22\n", + "- MAPE: 2.14%\n", + "\n", + "Original Forecast Performance:\n", + "- MSE: 55155.44\n", + "- MAPE: 2.17%\n", + "\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2IAAAIhCAYAAAAsFAnkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAodRJREFUeJzs3Qd4W9X9PvD3am9vO17ZkxASkrDDHmW0lN0CZZT558fqAMpq2aOMllIoe+8NhbJHgbAKgSRkx5l24r2Htu7/+R5Hxk7sxElkzffzPHpk6V5dHel66PU553s0Xdd1EBERERERUdwY4vdUREREREREJBjEiIiIiIiI4oxBjIiIiIiIKM4YxIiIiIiIiOKMQYyIiIiIiCjOGMSIiIiIiIjijEGMiIiIiIgozhjEiIiIiIiI4oxBjIiIUoKu64luQlK0gYiI0gODGBFRErv88ssxYcKEAS977bUXksEBBxzQp12TJk3CzJkzceKJJ+L111/fZH/Z55///Oegj//SSy/hr3/96xb3O+WUU9RlW59nIDU1NTjnnHOwbt26Pq9Zzk88ffXVV7jggguw9957Y+rUqfjZz36m3pfGxkakC3lP5b0lIkp3pkQ3gIiINq+goAD33HNPv9vMZjOSxb777ov/+7//U1+HQiE0NzfjnXfewZ/+9CcsXrwYV1xxRc++L7zwAoYNGzboY993333Yddddt7jfNddcg6Hw5Zdf4tNPP+1zn5wTl8uFeLnjjjvw8MMP49BDD8VVV12F7OxsLF26FA899BDef/99PP300yguLkaqk++hU089NdHNICIacgxiRERJzmKxYNq0aUh2ubm5m7Tz4IMPVkHy8ccfxyGHHIIZM2ao+4fq9YwdOxbxssMOO8Ttuf7zn/+owCVh9vTTT++5f/fdd1cB+Oijj8ZNN900YGBPJcOHD090E4iI4oJDE4mI0oQMybvkkktw0UUXqaDz29/+FlVVVWp43mOPPaZ6UmQ42yuvvKL2//HHH3HmmWdit912w/Tp0/H//t//w/Lly3uO980336jHPv/889h///3VPl988cVWt0uG0lmtVnWcgYYMPvHEE6p9U6ZMUcPurr32WnR0dKhtMkxNhgS+9tpr6nHyml599VUVhGTIogzPlN6yioqKTYYmCjmOvC8777wz9thjD9x4443wer2bHWIox+/9XNHevAMPPLBn340f197ejltuuQUHHXSQeh0///nP8fLLL/c5rjzm7rvvVsMJ99xzT+y0007qHKxevXqz7+GDDz6oQuZpp522ybaRI0fi0ksvVa8vOofN7/fj3nvv7XlPJQTLMSKRSM/j5H36y1/+gn/96189Qx3PPvtsNDQ0qO8RCdFyTAl+8j70fpy87vvvv1+9BgnX0ovVe9im+PDDD3HSSSepY+y4446qLc8888wWv782Hpq4YMEC9brleaLtmTt3bp/nksfJc8k+8v38xz/+EdXV1X3Op3y/zJs3D7/61a/UeyLP+cgjj2z2fSciGkrsESMiSgEy1K8/RqMRmqb13JahgEceeaQaytf7Q7eEHhnOJkPp5AP3119/jbPOOkt9aL355pvVB/cHHngAv/71r/Hiiy9izJgxPY+VXparr74aPp9PfRDeWm63WwWOOXPm9Lv9rbfewu23366GMMoH85UrV6qgImFJruX5ZX6WfJCWD/yFhYXqceFwGI8++qjqCZJhkL3b3NtTTz2leo3uuusurFq1Cn//+9/Vh3QJKoOx33774bzzzlPvqbRF2rgxeW8kCMhcLQnCpaWlKojIey7BRkJu1JNPPqkCg4S21tZW1X557TJcsz/19fVYsmSJOl+9z3Vv8txREsbk+SSsSAieOHGiCj3y+isrK3HDDTf0ee8nT56s2iDz4K6//nr85je/UcFZ2iTnQMKa3C9BLuqjjz5CTk6O+r6Q77M777xTBTTpubPb7fjvf/+L888/Xw0xvPDCC9X78+yzz6rjSCiT78GBvr/efPPNPiFaXrf0/Mn3cCAQUOdBwqs8h3xvyRxEaasE33PPPVd9L0jYlcAl4T0vL08dS9r5u9/9TgU5uZaQfNttt2H8+PEqiBIRxRuDGBFRkpOeBvmw3J/LLrtMfSjtPWfsuuuuU8MZRbQn47DDDsOxxx7bs598OB4xYoT6cC1hTsyaNUv1gsiH2H/84x99PuRLb8b2yM/Px/z58/vd9r///Q9lZWU4+eSTYTAYVO+Ww+FQIUVIAJPX09/QRwkcEpQ2RwKahC45tgQyCTMSPpctW6Y+hG+JPG90uJwUIZG2bkx6XOR40rsTDavy4V4CtPQ4ScCVOV3C4/Go+6Lv+9q1a1XIkAAh4WZj0Z6d/p63P5999pma0/a3v/0NRxxxhLpPeg1tNps6rxKOxo0bp+6X9kkQysrKUrdlrtnnn3+uQmR5ebm6TwLdG2+80ec5JKDJa47uM3r0aDU8UkKRFGiR3km5LUE0St4XCf4SCnsHsc19f8lx5H2RNkuPWfS5JLR2dnbC6XSquXPyvSthMEr2Pfzww1WPl/yMRAOqBPnjjz9e3ZYw/MEHH6hAxyBGRInAIEZElORkjpX0AvRn4+IM8iE1GsJ6kwAR1dXVpYYlSm9JNAxEA4IM19q4KEXvx24r+RA8UG+O9HbIB+tjjjlGDeuTsPSLX/xiwP23tm3yIV9CWJQM05Mg9u233w4qiA2GhEnpBdu4x1B6J6XnRYbEyesSMiyu9/seLVoi4aa/IGYydf+p7t3DuaW2yGM2DjfSFglisj0axCSkRkNYNDBLG6IBS0iAlGGXvUnQ6b2PhGW5Le+pBDHpxRISlqQXUsKmfM8J6dUa7DmUdkoQlsAtr0cCk4RKGYopVqxYoXoMZShibxKc5VzIa+2t9/mJhnv5eSAiSgQGMSKiJCcfGOXD+2BID0F/pIcpSj5USzCSD90bk/s2/tDd+7Hbqra2dsAqidJzISFDhq5JT5H0DkmokXldsm1zBtM2CbK9RYeqtbW1IVak927j5xHR97j3c8nQvd6iIXGgoCVhW0LpxnOwNn5+CV9y/uVrCVO9w56Itq/3+e2v6uNg3tOioqJN7pP3NdqL2dTUpCpYSs+atF16X2U5g/7WYtvc88nrkXll8o8IGXYrgV169n75y1+q4YwtLS1qv4G+lxctWtTnPnnsxu8914YjokRhsQ4iogwj82rkw7HMXdqY9C5Eh9DFinw4X7hwIXbZZZcB95H5PRLEonOZpA3S6yEBbntFP6z3fo29A1l0vllvW9tLIr1K0eP291z99XQNljxWhqbKkMGBQoMML5SeRTmn0hYZzrfxa6qrq9vutkTJ8Tcmzy09TEJCtPSASbVMGdooIerKK6/cpueSXl6ZQyjzGmXopwx5lEAmc+2i36sDfS/H4rUSEQ0VBjEiogwjPRBSMEE+HPf+sC49JTJfJlpiPlakul4wGFTFE/ojhROksEM0JMp8NpnLI/OXouGh99DCrSVzpnqTghISRKPrkkmvkBSq6G3jwiJben4JmdJj9cMPP/S5/9///reatyfFSraHzAOUOWiyVlh/86ikyqFUMJReIHld8t69++67m7RFxOL8yvvTO4xJZUOZjyhVKaPbZQiozAmLDpWNnofBDrEU8hokYEqokh4+GVooFTVlGO369esxatQo1dMnRUd6k6IkEgCj88qIiJIRhyYSESU5mVOzcbnu3qSK38bD3bZE5tTIh3upRijFEiQoSeEOea5oKNpaMhwt2k4JeFJB8L333lMfkmWOz0DDK+WDtgxjkwqJ++yzjxrGJz08UpZdKv4J+eAtw8xkzs/WhhrpmZGiEdLrJl9LMZLjjjtOHV/IvDipGCkXKSLx8ccfq96X3uT5hRR3kDZuXKFR5rdJj568d1I1UQpryHEkIMlcvOjjt5UM0ZQCHFJ6X+abyXwpCdRSAEWWJpCeH9kmpH0SgGTonvQoynso75usQya9SbFYa03ms8k8MKkmKfPApBKlzLeT91jIOZLqh9KTJ0NSv//+e/X9JQG499IBWyJBSoKbvK/yvSpDFeUfCPJPAwl6EpD/8Ic/qOUF5Hta5sFJQIwWIJElHIiIkhWDGBFRkpPegIF6k4RUqtvaghrScyEf4CWUyAdZ6bWQOTwShqKFHLaWFPmIFvqQD9wSPqSIgzzHz372swEfJxUFJQjKsDMJMzKPR9onQxOlN0mcccYZqsCGhEdp99aQD/HSYyNhUHrcJEBIOIqSkucSIqXCnrRDqjBKOXcJGVESbKTHSSrzffXVV31KuQsJwlImX7ZLQQwpuy5D6uQ4EvpiQYKWtEOWF5CS8hKASkpKVBVAeV+iw/DkvZdQKe+7DA2U1ybBUM5zrIKJfK9IgI5WRZR1v6Q6YbT369Zbb1Vl8qOl8iX0SjVP6ZX77rvvBv08slTBww8/rN5TeS4JcfL9KfMI5fmjIVgCmrxmOdfSwylFPeT19jdvj4goWWg6Z6kSERHRIEUXzJbgSURE245zxIiIiIiIiOKMQYyIiIiIiCjOODSRiIiIiIgoztgjRkRERERElElBzO/3qwUepfrSrFmz8Oijjw64r5QtlspQUlr42GOPVRWwepPyyAcddJDaLlWTpEpUlJRClmpLUvFKqixdfvnl6r4oqSol5Z97X6RyGBERERERUdoFsdtuu00FqieeeEKtISPrfmy8AKXo6upS64dIYHv11VfVgo5SbljuF7KOigQtKUf8wgsvqJAla4pEybGXLFmiyg1LeeIVK1ao9VV6L4Yp6+jMnj2757Kt6+gQEREREREl7RwxCVHSOyULTMq6KOJf//qXWp9l45K4L7/8Mu677z58+OGHan0UabKsSSNrwsj6IbJ2iSzqKOuWiOrqarVApyy8mZeXpwLcc889p3rLxA8//ICTTz5ZXVutVpx44ok46qijNrtODxERERERUcr3iEkPVSgUUr1bUTNmzMC8efMQiUT67Cv3yTYJYUKup0+fjrlz5/Zsl7AVVVxcrBa5lPsloN1///2bLHYaDofVYphi5cqVarFJIiIiIiKieDAhQerr65GTkwOLxdJzX35+vpo31tLSgtzc3D77jh07ts/jpadr+fLl6uu6ujoUFhZusr2mpgY2mw377LNPn21PPvmkmgcmz9HQ0KCe77XXXlPDGaWH7LjjjsMZZ5zRE/wGq6mpHbHqX5Snzs11x/SYFBs8N8mL5yZ58dwkL56b5MTzkrx4bpKXliTnJtqOpA1iXq+3TwgT0duBQGBQ+0b38/l8m93e29NPP4133nkHDz/8cE9vWDS4yfDHxYsX48Ybb4TRaMTpp5++Va9pMG/41hqKY1Js8NwkL56b5MVzk7x4bpITz0vy4rlJXrkpcm4SFsSk52njoBS9Lb1Yg9k3ut9A2+12e5/7nnnmGRWypOdLqjSKXXfdFV9//bXqnRPSUyYVF2VO2dYGscbG2PaI5eW5Y3pMig2em+TFc5O8eG6SF89NcuJ5SV48N8lLS5JzE21H0gaxoqIiNDc3q3liJpOpZwiihCuPx7PJvjKEsDe5HR2OOND2goKCnttSLVGqNEphj9NOO63PvtEQFjVmzBjU1tZu9WuSEx7rkz4Ux6TY4LlJXjw3yYvnJnnx3CQnnpfkxXOTvPQUOTcJK9YhxTMkgEULbog5c+ZgypQpqsBGb1LtUCocRgs8yvX333/fUwVRruWxUVI1US7R7TL/S0KY9ISdeeaZfY790ksvqQqMvYtHyvDE0aNHD9ErJyIiIiKiTJewHjEZNigl46+99lrcfPPNquCGLOh8yy239PSOud1u1UN26KGH4s4778RNN92EX//613j++efVvLHDDjtM7Svl50855RRMmzZNBTnZb7/99kN5ebkqxHH99dfj6KOPxhFHHKGOGyXFOmSRZ3lOWcBZjiPrmklJ/RtuuCFRbw0RERFRSpF/aEci4U0qX6cjGXYm9QmCwUBK9LpkEi1O50Y6jQwG41YX9kuadcSEhCkJYu+//z5cLpfqrYrOy5K5WhKQZJ2w6KLNsjCzLMYs26677jrssMMOPceShZ7vvvtutLa2Yq+99lJBSoYc/uc//8Ef/vCHfp//o48+QllZGb777jvcfvvtqqS+FO04++yzVSjbWg0NsZ0jlp/vjukxKTZ4bpIXz03y4rlJXjw3ySmVzksoFERraxOCQR8yhXwQz4TQmYoMcTo3FotMp8qFyWQe8Oc3qYNYumEQyww8N8mL5yZ58dwkL56b5JQq50U+RtbVVakPvy5XNoxG03b3EqQCo1FDOJzEJyaDGYf43Mj3fDgcQkdHiwp8hYVlm3zPDzaIJWxoIhERERGlNukN0/UIsrIKVA9BpjCZDAiF2COWuefGqpa6amqqVT8DZnPfZbSSvlgHEREREaUHTeNHSsosWgy+5/lTQ0REREREFGccmkhEREREMVfT5kOLNxi358u2mzHMkznDIyn1MYgRERERUcxD2PGPfQdfHOdR2UwGvPTbmVsVxhoaGvDIIw/gyy8/Q3t7B0pKSnH44b/ACSecqNa7Hch5552NadOm48wzz93icxx33C9wxhnnqONuj++//w4XXfT/MHv2d/1unzVrZr/3H3LIYfjLX1JnWaY5c75FXl4+Ro4cNeB70J9hw4rx8stvIpUwiBERERFRTElPmISw3+5WjmKPdcifr7rNj8e+qVTPO9ggVltbg/POOxPDh4/A9dffioKCQixevBD33fdPfP/9t7jttrtUNcj+3HrrHdA046Ce56GHnoTDYUc83HTTbdhxx5363Ge1plYv4cUXn4e7776/3yAW9cYb725yn6zrlWoYxIiIiIhoSEgIG57jQDK6667bVQ/YnXf+U1XAE3J78uSdcMopJ+C1117Gscee0O9js7KyBl2ZT9a1jRe326N6k9JdXpq8RgYxIiIiIsooTU2NmD37M9x22997QljUsGHDcPjhP8ebb76ugtjbb7+JN998DdnZuaqn7I9/vFxt6z008YUXnsFzzz2Nrq4u9dgVKypw2GE/V8MRew9NvOCCc7DLLrth3rwfMHfuDygsLMLvf38pdtttD3WcVatW4p///Bt+/HG+Wqtq4sQdcNllV222d2iw/H6/Gob54Yfvoa2tFTNm7II//OFPKCoahurq9Tj++CNx1ln/D88//wwOOeRQte3TTz/BQw/9S20fPXoM/u//LsbOO89QxwuFQup4b7/9b/h8Puyyy+649NIrkJWVjfr6OvzjH3fgu+++hd/vw6hRo/G7312KnXaaph770kvP4/nnn0ZzcxNGjRqDiy76I6ZOnabeKyHDD3/727MHNfRzY3PmfIcbbrgGu+++Jz744F2ccspvsWbNarVt2bKlaGxswH33PaLaKb2fs2d/ikDAj1mz9sHFF18Kj8ejhkDefPN1fY7xm9+cjlhj1UQiIiIiyihLly5RC/NOnDi53+0SGCoqliEQCKjbEowkTDzwwOPYddfu0BT1/vvv4JFHHlRh4v77H1WhZe7c7wd87ieffBQHHfQzPPXUCxg3bjz++tcb1cLAcvnTn36P4uISPP74s7jvvkcRDodx3313x+Q133HHLfjss09w9dXX4f77H0MoFMYVV/xRPW/U/Pnz8MgjT+H440/E8uXLcNNN1+LUU8/EE088j0MOORyXXHIRqqoq1b4PP3w/3nnnLVxxxTXqeBKqbr/9ZrXt+uv/jHA4ggceeAyPPvqMGvZ55523qm3Lli3Bv/71DxVon3nmZRXA/vKXP6l2yDDO6BDLE088ZZtfa01NtTp3jzzyNA466FB133vvvY2zzz4Pt99+F8rLh+PKKy9BRcVSFcb//vd7sXr1atx887WbPUassUeMiIiIiDJKe3ubuna73QMO8RNtbd37aZqG0047o9/5Vq+++pIq7nHAAQep21dddR2OOebwAZ97jz1m9RTuOO20M3H66SeqHjqn04WjjjoWRx99POz27jll0qv27LPd4WQwLrnkYhiNP/WzSK+PFLCQ1yFB5I477sb06d1FPa655gYcc8wR+Pbbb9Q8OSGvo7S0TH19ww1/xi9+cZTqHRPHH/9rzJ07Rw3ZvOCC36lewvPP/53qNep+7ivw8ccfqIC79977Yb/9DlA9fuKYY07ApZderL6urq5W7+ewYcNU6Dz77P/DnnvurYJYdBinvP8Ox8BDWg8+eO9N7pNeq1NPPaPn9sknn4aysvKe29K7KL1eoqJiuQrLzz77Ss9rl4ImJ598HNauXT3gMWKNQYyIiIiIMko0aEkAioaF3hoa6tW1DFMTOTm5Axa9WLFieZ9ha/KY6If7/khvTJTT6ewZ5ifh66ijjsO77/4HS5YsUoFg6dKlyM3NHfTruvzyq7HDDjv23I4WG6msXKuCTu9tHk+WaueaNat62ivBKEp6iFau/BD//verPfcFg0HVI9jS0oLW1lZMmDCpZ5v0GEaHEh599HFqCOSCBfPVsEDpgYz2vMkwzNGjx+LUU3+N8eMnYNasfXHkkUdvtkrlxh577NlN7oueq95VFHsrLv7ptrxml8vd5zyNGDFSfV/I63a5XP0eI9YYxIiIiIgoo0jviMwNW7p0cb9BTILQmDHjYLFY1O3odX+655jpfe6TXqGB9Bc4ZH+ZX3b22aeqXizpuZHhixLGZO7ZYOXnF/TbgzNQ+2X4oFz620+GRUqP0KGHHtHnMVardbOhSQLX739/Ptrb23HggQdjr732UQHuqqsuVdttNhsefPBx1SP1xRefqTl4r7/+ihoSKUMYB2MwvVTSzt4sFusW349IJKwuAx0j1jhHjIiIYsLYuAS2Rc/D9uMTsM99UF2j1x80IqJkIUPgZPjc448/ogLHxmXt33rr3zjyyKMGdSzpCZIen6jOzg5UVVVtdZt++GGO6omT0u0nnXSqKuohbdlcqBssGW4ogXHhwh977mttbUFV1doBe+/k/urqdSr0RC/SO/b111+qIZ3Z2dlqHl3U8uVLcfTRh6uCIxKy7rrrX2qo4J57zlIFMoS8Fukle+qpx9QQyQsv/IMaHijFMubPn4t4GT58JDo62vsMQ5R2d3Z2brY3M9bYI0ZERNtPjyDrP6fD2F4FXdbWMVqghbyIOAsRGH1YoltHRAki63sl6/P87neX4LzzzlIFKE4//WxVPVB6yKSQhFQGlLlag3Hssb9ShTDGjh2HESNGqSIWXm+Xmge1NaQkvtfrxeef/1f12H333f/wyisvqrlj20vmW/3iF0fj73+/TVVhlGGJUjFQegMl8EWDUm8nnHASzj//LFXQRMKU9F698MKz+Mc/7lPbjzvu1+q1Si9WdnYO/vGPOzF58hQV0mRI5EcfvaeGHcrabI8++oB6jBS/kF6mxx57CLm5eZg5c1cV2uR1Sw+kkCGaq1atwPjxE3uGCG6sv/ZGh5AOhgxDlLltUl3xD3+4TAXEv/3tNlUJU4ZNStXEeGAQIyKi7Wau/p8KYe2zrkU4fwd1n+vTq2Bf+AyDGFEGyrabYTMZ1CLL8SLPJ8+7NcP4HnzwMTz++MO47rqr1LwnWUfsl788VhWtGGgx543JEEKpJHj77beooCHznWRu0dbMeRKyEPPpp5+FO+/8qzrOmDFjVQn5W2+9QZWD315SYOOee+7C1Vf/SQ0VlBAkvVYDDdPbcccp+POfr8ejjz6owqn0ql1zzU0qrAiZFyfDD//yl8vVHDcpuCEl6mWullRElPf1gQfuRXn5CFx88SW48cZrVK+ZvM4rrviL2i7BUAKwPE+0RL8EvHvvvRvr1lWpSpT9+eUv+69i+Oqr/xn0+3H11der57/44v9T53rvvfdVPXTxpOmx6O8kpaGhHbF6N+WfKPn57pgek2KD5yZ58dwkjuuTS2FZ/RHaD74b0Lo/vFjWfAz7Dw+g6ZQvoWeV89wkKf7cJKdUOS/BYACNjdXIyyuG2dz3A31Nmw8t3mDc2iIhbJin/4IasWYyGXoWdJYhhRLgJFAICSU///lBuPnmO3oqFFL8mHqdm0R970d/freEPWJERLR9Qj5YK95CYOTBPSFMBEr3hH3Bk7Ateg7ePS5LaBOJKP4kFMUrGCWSDCWUdcZkMWOHw4mXXnpOXcswPaLNYbEOIiLaLpbVH8IQaEegvHt9lh4mGwJls2Bb/DwQjt9/xYmI4umss/6fKvAglQJlTTAp137nnf8c8op7lPrYI0ZERNvFtvRlhHLGIuL+af2ZKP/Ig2Bd9b4Kayg6ISHtIyIaStL7JXOciLYWe8SIiGibad4mWNb8F4GyvfvdHskaiVDOONgWPhP3thERESUzBjEiItpm1op/q4VMg2V7DbhPYOSBMK/9FGheE9e2ERERJTMGMSIi2ma2JS8jVDQNutUz4D5StANmO/D9k3FtGxERUTJjECMiom1ibFkJc91cBMr7H5bYw2RDcNguwLJ349U0IiKipMcgRkRE28RcNRu6ZkSwaMYW9w3lTwJqF0Lzt8WlbURERMmOVROJiGibmOrmIewZAZi2XKI5nDdRzSUz1cxBYPj+cWkfESWWoX0dDL6muD1fxJaLiLs0bs9HtL0YxIiIaJuYJYhljxrUvhFnMWDPgXn9/xjEiDIkhOU+ux+0kDduz6mb7Gg66b+DDmOzZs1U1y+//BaGDRvWZ9vrr7+MO+64Fb/97dk488xzt7ot33//HS666P9h9uzvtrjv22+/iUcffRAvv/zmJtuqq9fj+OOPHPCxgzk+JS8GMSIi2npBL4xNyzddxFnWDgtF0OkPoTMQRiCsY1SuA0aDBhTuAPP6bxLSXCKKL+kJkxDWOePCuPRSSfBzzvmnet6teT6TyYQvvvgUxx77qz73f/bZf6FpGpLFQw89gcLCokQ3g2KMQYyIiLaaqXERND2McPboPvd/u7YFj369Fnqv+2aNzsUpM8tUEDN9/wQQ9gPGLQ9nJKLUJ6Fo498TyWTq1OmYPfuzPkGss7MDCxb8iHHjJiBZZGfnIC8vP9HNoBhjECMioq1mqpsP3WBG2F3ec583EMaLP6zHyFwHppVlwWYyoLLZi9krm7DHiBzMKJoMLRxQjw0V75LQ9hMRib333gf33vsPFb6cTpe678svZ2Pq1Gnwer2bDCF85pknUF1djdGjR+OCC36PadOmq23y+Ntuu1k9VgLTkUce1eextbU1+Nvf/orvvvsfcnJycfjhv8Bpp50Jo9G43a8hOnzxrLP+H55//hkccsihyMrKRkXFMrS1tWHlyhW4+ebbscMOO+KRRx7Ahx++h7a2VsyYsQv+8Ic/oahoWL/HkG00tFg1kYiItpq5fn53oQ6juee+dxbXwxsM48Dx+RiVa0exx4qZ5dkY5rbiqe+qEPSMVHM4zNX/S2jbiYiiRo8ei/z8Qnz99Vd9hiXuvfd+m4Swv//9NvzmN6fj8cefwS677IZLL70Y9fV1avvtt9+CtWtX4557HsTvf3+pCjNRuq7jqqsuUwHssceewZVXXoMPPngXTz31WExfy/z58/DII0/h+ONPVLc///xTHHzwz3D33fdhhx0m4447bsFnn32Cq6++Dvff/xhCoTCuuOKPiEQiAx6DhhaDGBERbTVT7dw+hTrqOwL4aFk9dhmeDY/tp8EWBgNw8IQC1LX78eaPNQjljuc8MSJKul6xL774TH0dCATw7bdfY++99+2zz8svP4/jjvs1Djvs5xg+fCTOP/8iFeJeeeVFdHR04JNPPsTvfncpJkyYiN122wOnn35Wz2PnzPkWNTXVuOyyq9Rjp0+fifPP/x1efPG5QbfxlFNOwMEH793ncvvtN/fZ54QTTkRpaRnKy4er27m5eTjqqOPUEEu/P4D33ntb9XLJ848dOw7XXHMD1q5dg2+//WbAY9DQ4tBEIiLaOoFOGFtWIDDiwJ67Xp67Hg6LUfWAbazAZcGM8my8+sM67DN+LHKrPgD0CKDxf4FElHizZu2Lq6/+E0KhEObM+Z8KWNJ71dvq1atVBcXedtxxCtasWYXKyjUIh8MYN258z7ZJk3bo+Vr2kaGAP/vZT+FOeqH8fj9aW1sG1cbbb/8HCgoK+9zndDr73C4uLulze9iw4p6vKyvXqueU4YlRHk8Whg8fodon1/0dg4YWgxgREW0VU8NCaHoEoZzuCfiLazowb30bDp9UCLOx/ypju4/MQUVjJ/5Tl4fTAm0wNi1FOG9SnFtORLSpnXaapq7nz5+Lzz77FPvs03dYorBYLJvcFw5H1KX3EMQok+mnYdsS0qQn7NZb79zkGNF5aVsioWpLIWnjNva+3V/7+3sNA+1HQ4P/jiQioq2eH6YbLYi4y9TtNxfWoMRjxYTCgT9QSEDba2w+PmrKga6Z1HpiRETJQErY77HHXmp44pdffoZ99tl0rUPpMVq4cEGf+xYu/FHdLxc5xuLFi3q2LV++tOfr8vIRqliHVD4sKytXl+rqdapwRrxK5MtwQykMIm2Okt64qqq1Pb1hFH/sESMioq1ikoWcs0YCBhPavCGsbOzCIRMKsKXPExOLPHgbZjTbhsNR/T/4ppwWryYTUYLI+l6p8DwyJ+zmm69HSUmpumzsV786Gbfeej1Gjhylhve9886bWLFiuSp8Ib1ahx56BO6663ZcccU18Pt9aoHmqF133V0tGH399X/Gueeej46OdlVhcebMXQddNbGlpbnf3iqpjjgYDocDv/jF0argiMxVk2GJ9933T7U2mRQeaWxsGNRxKLYYxIiIaOuDWE73XIgfq9vU9ag8xxYfZ7cYUZ5jx6JgGfZY/7WM48EW0xsRpaSILVdVSZVFluNFnk+ed1vsuuseao7YxkU6og488GA0NTXi4YfvV9cyH+xvf7sHI0aMVNulUuLf/347fv/78+F2u1Vhj3vvvUttk7B1661/U0HtnHNOg93uwP77H4QLLrh40O07++z+/3F1770Po7Cw79yxgVxwwe9wzz13qflwwWBQBcG77voXhyMmkKb3HtBK26WhoV19rogF+WySn++O6TEpNnhukhfPzdDTAu3If2gSOqf/H4LD98N9s1ejvsOPX0/f9D/IG3M6bfhqeR2al3+JS80vovGUrxDx/LQOGSUGf26SU6qcl2AwgMbGauTlFcNstmzSS2XwNcWtLRLCZAHpeDCZDAiFfppbRcnDFKdzs7nv/ejP75awR4yIiAbNVN89vyCcPQbBsI7Fte3YdXjOoB8/Nt+JZ5aV9vSsBRjEiNKWhKJ4BSOiVMRiHURENGimuh+hG63qw9XS2g4EwjpG5295WGKUlLjPyc5Hq5YFc33fie9ERESZhEGMiIgGzVQ/H+GsUWoNsPnVbciymZDn2Lr5BeMKnKgIF0KvnT9k7SQiIkp2DGJERLR1hTqyR6k5K/PWtWJ0nnOr622MLXBijV4ErY49YkRElLkYxIiIaHDCfhjb1iLsKUdlsxetvhDGbMWwxCinxYg2ezkcwSZonXVD0lQiii/WfqNMo8fge55BjIiIBsXYsgqaHkHYVYr569tgNRpQmmXfpmNZ8seo68C6eTFuJRHFU3QdrEDAn+imEMVV9HveaNz22oesmkhERINibK5Q11KoY/73tRiZa4dxG/+dV1hUjo51NjSsmoPi8QfHtqFEFDcGgxF2uwsdHc3qtsVihZYB6wNGIhrCYfYCZuK50XVdhTD5npfvfYNh2/u1GMSIiGhQTM0ViFg9aInYsbbFi8MmDW4R0f547GZUacOg1XaXwyei1OXxdC+iHA1jmUA+fEciXEcsk8+N3e7q+d7fVgxiREQ0KMbm5WpY4orGLnW7PHvbhiVGNVvLMLJjSYxaR0SJIj1gWVl5cLtzEA6HkO6kwy8nx4nm5s6kXmw7E2lxOjcyHHF7esKiGMSIiGhQjE3LEXGVYE2TFy6LCS5r99yQbeV3D0dxw2ysleEdrsEvCk1EyUk+mBoMW7ecRap+2LfZbDCbgwxiSUZLsXPDYh1ERLRlegSmlpVqftjqpi4Uubf/w5Yxt7tgR9Wy72LQQCIiotTCIEZERFtkaF8HLexDyFWKNSqIWbf7mJaccgR0EzrW/hCTNhIREaUSBjEiItoiU/NydV2nFcAXimCYx7bdx9SMJtQah8HUsCgGLSQiIkotDGJERLRFxuYV0I1WVHi7C3TEYmiiaLOXocS3DKEwq48REVFmYRAjIqJBVkwswZpmP7JtJtjN21eoIyroGYkxqMKymswpe01ERCQYxIiIaItMUjHRXYJVjbGZHxZlzB0FsxZBVQXniRERUWZhECMiokH1iIWcpahq9cZkfliU3zUcEWjwr5sXs2MSERGlAgYxIiLaLM3bBIO/BfXGAgTDekx7xCJGK5qNhXA0L4KeCou+EBERxQiDGBERDapi4upgrroujGEQEx3OcoyNrMKaZm9Mj0tERJTMGMSIiGiLwxJ1zYCFXW7kOy2wGLWYHl/3lGGcVoV5VS0xPS4REVEyYxAjIqItlq6POIuwsimAIldse8NE0FmKLK0LK9euifmxiYiIkhWDGBERbXFoYshZgvVtfhR5Yh/EvI4SdR2sWxLzYxMRESUrBjEiItosY9NyNJuKENF1DIvx/DDhsxUiDCOc7Su5sDMREWUMBjEiIhpY0Atjxzqs0/Ng1DQUuCyxfw7NiA5rEUajCqubWLCDiIgyA4MYERENyNSyQl2vCOQgz2mB0RDbQh1RAUeJKtixrL5jSI5PRESUbBjEiIhoQMbmCnW92JuFXId5yJ4n4CzBeON6LK1jECMioszAIEZERAMytqxExJqN1R2GIQ1iUrAjD61YX7N+yJ6DiIgomTCIERHRgIytqxF0FMIbDCPXOQTzwzbw2rsrJ+oNy6Hr+pA9DxERUbJgECMiogEZW1ehzVygvh7KHjGfvQgRaCgJrUFdR2DInoeIiChZMIgREdGAjC2r0IBcSImOHPvQBTHdYIbXWoRx2jos4zwxIiLKAAxiRETUL83XAoO/BesjOSqEDVXFxCi/oxgTjetYOZGIiDICgxgREQ04P0ys9GchewiHJUb5HCUYb1jPHjEiIsoIDGJERLTZILa404U8x9AV6uhdOTFfb0Rlbf2QPxcREVGiMYgREdGAQSxszcJ6nxE5cegRi1ZOdLavRGcgNOTPR0RElEgMYkRENGDFRJ+1UH2dN4Sl66N89mHQoWGcoQoV9Z1D/nxERESJxCBGREQDVkxsNeapr3OHsGJiVMRohd+ar+aJLa1jECMiovTGIEZERAMOTaxFLlwWI6zm+Py5kHliO5rXs3IiERGlPQYxIiLahOZvg8HXhKpwNnLiUKgjymcvxhhwLTEiIkp/DGJERDRgxcTlPjdy41CoI8prL0ZBpA7rGxoRjuhxe14iIqJ4YxAjIqLNlK6PcxBzlECDjrLIelS3+eL2vERERPHGIEZERP1WTAyZ3WjXbciNQ8XE3kMTxRhtPdY0eeP2vERERPHGIEZERP32iHVuKF0fj4qJUWGTA0GTG6ONdVjd1BW35yUiIoo3BjEiIuq3dH2zIRcWowaX1RTX5/bZCzHBXI81zQxiRESUvhIaxPx+P6688krMnDkTs2bNwqOPPjrgvosWLcLxxx+PqVOn4thjj8WCBQv6bH/rrbdw0EEHqe3nn38+mpqaera1tbXhqquuwp577ondd98dl19+ubovqrm5GRdeeCF23nlnHHDAAXjjjTeG6BUTEaXO0MQaPQe5Dgs0Lb7P7bcVYpShBqsbGcSIiCh9JTSI3XbbbSpQPfHEE7jmmmtwzz334N13391kv66uLpxzzjkqsL366qsqMJ177rnqfjF//nwVtC644AK88MILKmRdccUVPY+XYy9ZsgQPPvggHnnkEaxYsQJXX311z3bZt729XT32vPPOU9vkmEREmUgLtMPgbcTqgJSuj9+wxCifrRAlkRqsbuYcMSIiSl/xHW/Si4Sol156CQ899BAmT56sLsuXL8czzzyDQw89tM++b7/9NqxWKy677DJomqZC12effaZC2zHHHIOnn34ahx12GI466qiegLf//vujsrISeXl5eO+99/Dcc89hxx13VNulF+7kk09WPXK1tbX45JNP8NFHH6GsrAzjx4/H3Llz8eyzz2KnnXZKyHtDRJRIxtY16nqZz4OcvPgHMekR80RaEPC1od0XgtuWsD9VRERE6dcjJj1UoVBI9W5FzZgxA/PmzUMkEumzr9wn2ySECbmePn26CkzR7dJbFlVcXIySkhJ1v8FgwP33349Jkyb1OWY4HEZnZ6faR/aXENa7HT/88MOQvXYiomSfHybWBLMS1iMmRmi1nCdGRERpK2H/Zqyvr0dOTg4slp/KIufn56teqpaWFuTm5vbZd+zYsX0eLz1d0oMm6urqUFhYuMn2mpoa2Gw27LPPPn22Pfnkk5gwYYJ6Djl2f4+VnrKtFct5FNFjxXtuBm0Zz03y4rmJDWPbaoTMLnQG7MiyxSiIab2u9S33iPUEsaYuTCnxxKYN1C/+3CQnnpfkxXOTvLQkOTeDff6EBTGv19snhIno7UAgMKh9o/v5fL7Nbu9NhjG+8847ePjhhwd17K2Rl+fe6sck4pgUGzw3yYvnZjv5qtBuKwI6geI8JxyW2P2pcDpsW95JtyJscmIHQwPqfGHk5/N8xgN/bpITz0vy4rlJXnkpcm4SFsRkztfGYSd6W3qxBrNvdL+Bttvt9j73yfyzG2+8URXnkCqNgzn21mhsbIe+hf/0bk2Slm+iWB6TYoPnJnnx3MRGVu0yNGrdpesjgRA6g6HtP6jWHcI6u3xb7BETXlshxvrr8UJVCxoa2rf/+WlA/LlJTjwvyYvnJnlpSXJuou1I2iBWVFSkysbLPDGTqbsZMkxQApDH49lk34aGhj73ye3okMKBthcUFPTclmqJUsRDCn6cdtppWzx278cOlpzwWJ/0oTgmxQbPTfLiudk+xpbVqDfvrIYlxmx4R/R8DPK8+K0FGBmoweomL89lnPDnJjnxvCQvnpvkpafIuUlYsQ4pniEBLFpwQ8yZMwdTpkxRBTZ6k7XBpHiGvuEdlevvv/9e3R/dLo+Nqq6uVpfo9tdee02FMOkJO/PMM/sce9q0aVi3bp2aT9a7HXI/EVHGCXbB4K1HVSgbWfbEVSuURZ1LIutR1eJFKJICf02JiIhSJYjJsEEpN3/ttdeqNbs+/PBDtaDzqaee2tM7JnO/hJSzl7XBbrrpJlRUVKhrmdslJevFiSeeqBZhlnL4Uo1Rer32228/lJeXq8If119/PY4++mgcccQR6rjRi1ROlH1kmOKll16qHivHkMWhpbw9EVGmMbatVder/O7YFerYBlKwIyvUAGPEj+rW7r8FRERE6SShCzpLD5WsHyZDBa+77jpceOGFOOSQQ9Q2CUeyfphwuVx44IEHVE+VrBsmJedlcWaHw6G2Swl8CVv33nuvCmVZWVm45ZZb1LYvvvhCrVkmvWJyzN4X6TUT0lvmdDpxwgknqFL3N998M9cQI6KMXkOswudBVgLX74qWsC/X6rC6iSXsiYgo/Wh6dLwfbTeZUB7LYh1SKSyWx6TY4LlJXjw3288+9yE4vroFJ/kuxdFTijEqr/sfXrHgdNrQ2Tm43i1zoAU7f/cHnBe+BOP3PA6/mfnTWo8UW/y5SU48L8mL5yZ5aUlybqLtSOoeMSIiSi7GtjUbeqO0hPaIBc1ZCBusmGxtYI8YERGlJQYxIiLqYWhdgzZTnvraYzcn9N+JPlsRxpnr1aLORERE6YZBjIiI+vSINWk5cFlMMBliVbt+2/htBRiOavaIERFRWmIQIyKibpEwjG2VqIlkJ3RYYpQMkRwWrkaLN4RWbzDRzSEiIoopBjEiIlIMnbXQIkGsDWYhK5HDEnv1iGUFamFGCGuavYluDhERUUwxiBERUc+wRFHhcyV0Mecon70IBkRQqnGeGBERpR8GMSIi6llDTIeGNYHELubce1FnsaO1EVVc1JmIiNIMgxgRESmGtrUIWnMRhCkp5ogFLDmIaGZMstZjXQuHJhIRUXphECMiop6hiV2WfPV1MswRg2ZQBTtGG+tQ1cIeMSIiSi8MYkREpBhbV6PFmAujpsFhMSIZ+G35KEcNqtgjRkREaYZBjIiIFGPbWtTrOapQR4KXEOtTObE4vB6tvhA6/KFEN4eIiChmGMSIiAhaoB0GXzPWhT1JUagjym/tLmEP6FjHgh1ERJRGGMSIiAiG1rXqelXAA08SFOroPTTRpAeQjzYGMSIiSisMYkRE9NMaYl4XspOhUMcGAWueuh5lamTlRCIiSisMYkREpNYQixjtaAzbk2Ix5yi/tbuK4yR7M3vEiIgorTCIERGRKtQhpeIBLanmiIVNDoSMDowxN7JyIhERpRUGMSIiUqXr20256utkmiMW7RUbbmjgWmJERJRWGMSIiEjNEWsy5MJqMqhLMglYc1GsN6Cm3Y9QRE90c4iIiGIiuf7aEhFR/EVCMLSvRx1y4LEmV29YtHJiQbga4YiOunZ/optDREQUEwxiREQZztCxHpoewrqgB+4kG5YYHZoYXUuM88SIiChdMIgREWU444Y1xFYHPHAnYY+YlLA3RXzI1dpZOZGIiNIGgxgRUYYztq2GrhmwwudM2h4xsYO9hQU7iIgobTCIERFlOGNbJcK2PHSGtOScI7ZhUeeJ1masb+XQRCIiSg8MYkREGc4ga4ht6HVKttL1ImxyImy0YZRJ1hJjjxgREaUHBjEiogxnbF2Djg1riLmtybOYcw9NU8MTy2UtMfaIERFRmmAQIyLKcMb2SjQbctUfBKfFiGQkwxOH6XXo8IfR5gsmujlERETbjUGMiCiDaYEOGHzNqNWzVaEOQ5L+VZDKifmhGvU1hycSEVE6SNI/uUREFK/5YaI6lJWUpeujZGii2y9BTGcJeyIiSgsMYkREGV4xUawOuJKydH3vIGYOd6HY4uOizkRElBYYxIiIMnx+mG60YE2XNal7xGRoophsb2aPGBERpQUGMSKiDB+aGHYUotUfTsrS9VF+W3cQG2dpZo8YERGlBQYxIqIMZmxdq4b96VK63paEpes3CJncCBusai2x9ewRIyKiNMAgRkSUwYxtP60h5rEmZ+n6nrXEbPko0+pR1xFAKCLRkYiIKHUxiBERZSpdh7G9Sq0hJpJ5jpgIWHJRGKlFOKKjocOf6OYQERFtFwYxIqIMpXkboYW8qNdzYDcZYDEl958EGUKZG6xVX7NgBxERpbrk/qtLRERDOixRrA9nwWVN3vlhUTI00e2vVl9XtzGIERFRamMQIyLK8DXE1gRc8NiSeH5YrxL25lAHymx+VLdyaCIREaU2BjEiogwOYhGLC+t9xqSfHyb8G9YSm2hrwXr2iBERUYpjECMiylCG9rWIOIrQ1BlI6tL1veeIiXEWlrAnIqLUxyBGRJShjK1rELTlIxDWU2JoYsjsQUQzY4SpiT1iRESU8hjEiIgylLFtLTpM3cP9PCnQI9a9llgeSrVG1LX7uZYYERGlNAYxIqJMFAnB0FGNZmOOupkKc8R61hLT6yEZrJ5riRERUQpjECMiykASwjQ9jHrkwqBpcFiSf2iiCFhykBOsU19znhgREaUyBjEiogwdliiqwx64rUYYNKQEKWHvCdSor7mWGBERpTIGMSKiDA1iOjRUBtxw21JjWKLwW3Nh9TcizwauJUZERCmNQYyIKAMZ2iqh23NR1xWBx5oChTp69Yhp0DHB3s7KiURElNIYxIiIMrRHLCxriHUF4LKmxvwwEbDmquuxlmbOESMiopTGIEZElIGMbWsQtuejzRdKqaGJUjVRjDQ3MYgREVFKYxAjIspAxrZKeC150FOodL2IGK0Imt0oNzShroNriRERUepiECMiyjRBLwzeBrQYu3uXUqlHTPgteSjasJaYLOxMRESUihjEiIgyjLG9Sl03oHsxZ48ltYJYwJqD3HD3WmIsYU9ERKmKQYyIKEPXEKuNZMFi1GA1p9afAqmcmLVhLTHOEyMiolSVWn99iYhouxnaq6BrRqwPOOBOodL1vQt22L21yLYZ2SNGREQpi0GMiCgDe8QijgI0ecMpVbo+ym/NgynchRGOINa3cY4YERGlJgYxIqIMnCOmglhXMKUqJm6ylpitlUMTiYgoZTGIERFlGEPbmp+CWIpVTOwdxEabmlDNIEZERCmKQYyIKMMY26oQshWgIxBKyR6xoDkLEc2EcmMj1xIjIqKUxSBGRJRBtEAHDP4WtJtScw0xRTOoXrFhaORaYkRElLIYxIiIMoihvVJdN2nda4ilYo9YtHJi/oa1xDhPjIiIUhGDGBFRhg1LFLV6VmoHMWsOsoO16muWsCciolTEIEZElGGl63WDGTUBO+xmA8xGDalIStjbvdXIsZsZxIiIKCUxiBERZdhizhFHIZp9YbgsqbeYc++hiTZfPfIdBq4lRkREKYlBjIgoExdz7gzAbUu9xZyjAtY8aIhgrK2dc8SIiCglMYgREWUQQ1slIo58NHUFUnZ+mPBH1xIzN3MtMSIiSkkMYkREGcTYLkGsEM3eYEoHseiizsO5lhgREaUoBjEiogyh+VthCLTDb82HNxiBKxXXENsgYrQjaHKiRONaYkRElJoYxIiIMoSxrXsNsWZDd2+Sx5q6c8REwJKHAr1Bfc3KiURElGoYxIiIMmwx5wZE1xBL3aqJG68lxoIdRESUahjEiIgyqEdMN1pRF7Cp265U7xGz5sLprUa23cQeMSIiSjkMYkREGVUxsRBN3iBcFiOMhtRczLn30ESbtxp5DgvXEiMiopTDIEZElEE9YrKGWHNXEK4UrpjYu4S9OdSBUnuIQxOJiCjlMIgREWUIY/uGxZy96RHEAtZ8dT3W0sS1xIiIKOUwiBERZQJd39AjVojmztReQ2zjRZ1HmJq5lhgREaUcBjEiogyg+ZqhhbwIy9BEbwCeFF5DLCpoyYYOA0oNDVxLjIiIUg6DGBFRBjC2rVXXXeZ8BMJ6yldMVDSD6hUrjHAtMSIiSj0MYkREGcDQXtV3DbE06BGLlrDPDXEtMSIiSj0MYkREGdIjppsdaAhY1G1Pii/mHBWw5sHp41piRESUehjEiIgygLG9CmF7AZp8IRg0DQ5LGgxNVGuJ5cLWxbXEiIgo9TCIERFlyGLOupSuVxUTjUjxtZx7+K15sPrrke8wcmgiERGlFAYxIqIMGZqoStd3BdKidH3vOWIGPYxR1jauJUZERCkloUHM7/fjyiuvxMyZMzFr1iw8+uijA+67aNEiHH/88Zg6dSqOPfZYLFiwoM/2t956CwcddJDafv7556OpqWmTY+i6jjPOOAOvvvpqn/sff/xxTJgwoc/lr3/9awxfKRFRgtcQa1/XvZizBLE0KdQRnSMmRpmauJYYERGllIQGsdtuu00FqieeeALXXHMN7rnnHrz77rub7NfV1YVzzjlHBTYJUTvvvDPOPfdcdb+YP38+rrrqKlxwwQV44YUX0NbWhiuuuKLPMSKRCG688UZ88cUXmxy/oqICJ510EmbPnt1zkTBHRJQONG8DtLBvQxBLj8WcN17UuczQyLXEiIgopSQsiEmIeumll1SAmjx5Mg4++GCcddZZeOaZZzbZ9+2334bVasVll12GMWPGqMc4nc6e0Pb000/jsMMOw1FHHYWJEyeqgPfpp5+isrJSba+trcVpp52Gjz/+GB6PZ5Pjr1ixQj2uoKCg5+JyueLwLhARDT1jW/fvwpC9EC3eYFr1iEWMdgRNTgzTuZYYERGlloQFsSVLliAUCqneragZM2Zg3rx5qveqN7lPtmla9+xyuZ4+fTrmzp3bs116y6KKi4tRUlKi7hcLFy5U973yyitwu92btGXlypUYOXLkkL1WIqJEV0wULYZsyMC9dOoRiw5PzA3Xqa9ZsIOIiFJFwv4a19fXIycnBxZL95o2Ij8/X80ba2lpQW5ubp99x44d2+fxeXl5WL58ufq6rq4OhYWFm2yvqalRXx9wwAHq0p+Ghgb1fK+99poazig9b8cdd5yaSxYNfoO1lbsP6lixPCbFBs9N8uK56Z+xvRIRswsNwe61wxLSI6b1utZjX8Le6atBlq17LTGe/63Dn5vkxPOSvHhukpeWJOdmsM+fsCDm9Xr7hDARvR0IBAa1b3Q/n8+32e2bI71h0eB23333YfHixWoumdFoxOmnn75Vrykvb9Petu01FMek2OC5SV48NxsJ1gLuIvg2DIIYluuEzZSYdcScDlvMjxlxFcDZvgZFWTY0+sPIz+f53xb8uUlOPC/Ji+cmeeWlyLlJWBCTnqeNg1L0ts1mG9S+0f0G2m6327fYjl133RVff/216p0TUjFRKi4+99xzWx3EGhvbpThZzJK0fBPF8pgUGzw3yYvnpn+eupWANRdVDR2wGg0I+4Po9Afj2witO4R1dvli3iPWachGVseXyMo1YWVtOxoa2mP7BGmOPzfJieclefHcJC8tSc5NtB1JG8SKiorQ3Nys5omZTKaeIYgSrjYuqCH7yhDC3uR2dDjiQNul6MZgRENYlBQEkQIfW0tOeKxP+lAck2KD5yZ58dz0ZWhbi1DeRDR3JrB0ffR86EMzR8wU6kSpPYAv1oV47rcRf26SE89L8uK5SV56ipybhBXrmDRpkgpg0YIbYs6cOZgyZQoMhr7NkrXBfvjhB7UOmJDr77//Xt0f3S6PjaqurlaX6PbNkcqNP/vZz3qOLWR44ujRo2PyOomIkmMNsUI0edNrMefeizqLseZmNHQEEAj1LfhERESUjBIWxGTYoJSbv/baa9U6YB9++KFa0PnUU0/t6R2TuV/i0EMPVWuD3XTTTWrNL7mWeWNSsl6ceOKJeOONN1SokmqMUuZ+v/32Q3l5+Rbbseeee6rnkgWc16xZg//85z946KGHVCl9IqJUp3XVQwv7VRBr7JTS9YmZGzaU/JbuRZ3LDE2qw62Ga4kREVEKSOiCzlKlUNYQkzW+rrvuOlx44YU45JBD1LZZs2ap9cOErOn1wAMPqF6vY445RpWlf/DBB+FwONR2KYF//fXX495771WhLCsrC7fccsug2lBaWqqOJT1uRx55JO68805ccsklOPzww4fwlRMRxa9iopDFnJtlDTFLd+XEdBK0ZCGiGVGM7iHq61q9iW4SERHRFiV0jIr0iklPlFw2tnTp0j63d9ppJ1VifiAS0OSyJbKo88ZkDbIXXnhh0O0mIkq1NcR8ljx0BVrhtqff0ERoBlXCPi9cC4M2hWuJERFRSkhojxgREQ0tQ1slIhYXmgLdPWEea/oNTYwW7HD4apDntDCIERFRSmAQIyJKY0YJYvbuQh3CbU2/oYkiYM2BvWs98hwMYkRElBoYxIiI0nyOWMSRj+au7nXDXGnaI+a35sHmrUae04yqFgYxIiJKfgxiRETpPjRRStd3BuCymGA0aEhHMkfM5qtHocPEHjEiIkoJDGJEROlKj8DYIWuIFaDJm56l63vPEdMQwShrC9r8IXT4Q4luEhER0WYxiBERpSmDWkMs0B3EuoKqRyxdydBEUW5oVNfsFSMiomTHIEZElMbDEkV0aKLHlr5BLGDNVdfFWncQW8cgRkRESY5BjIgozRdzDts3LOZsTd8gFjHaEDS5kBuogdVoYI8YERElPQYxIqI0ZWyrQsTiQUfEjFBEhzuNe8SE31YAu68a+S6WsCciouTHIEZElKYMqnR99/wwkc49YtHKifbOdch1mDk0kYiIkh6DGBFROi/m7MhHU1f3Ys7pPEdM+G35sHdVId9pwbpWb6KbQ0REtFkMYkREad0jVqh6xGT9MLs5fcvXC781H3ZvNQqcJlS3+aHreqKbRERENCAGMSKidF1DrH3DGmJdAXisJmjpuZZznyBm0EMYbmmHPxRB44YhmURERMmIQYyIKA0ZOmuhRYIbStfLYs7pPSxRBGz56nqEoUFds2AHERElMwYxIqI0X0OscUOPWLqLLupcrNeqa84TIyKiZMYgRkSUhozta9W1FOtoTPPFnPusJWZ2wxOoURUi2SNGRETJjEGMiChdKyZas+GHBZ2BMDw2MzKB31oAe9c65DnNDGJERJTUGMSIiNK2dH33/DDhsaV3xcQovzV3QxCTEvYMYkRElGZBrLKye+4BERElJ0Pb2u5hiT1riGVGj1jAmg9HdC2xFgYxIiJKsyB26KGH4vjjj8fjjz+O2truSdFERJQ8jBLEnNIjFoBUrXda0n+OWLSEvc1bgwKHEXUdfoTCkUQ3iYiIKHZB7PPPP8cxxxyDjz/+GAceeCB+85vf4Nlnn0VTU9O2HI6IiGIpHIShs2ZDxcTu0vXGDBmI7rfJWmJhjLS0IaJDLexMRESUjLbpT3Nubi5OPPFEPPnkk/j0009xxBFH4LPPPsNBBx2EM888E6+99hq8XpYNJiJKBEPHemh6pDuIdWZG6frePWKifMNaYpUt/FtERETJabv/R1pfX68uNTU1iEQicDqdePHFF7Hffvvh/fffj00riYhoqwp1CBma2F26PjPmh4nAhrXEiiJ1MBo0VHGeGBERJalt+jfp4sWL8e6776rLunXrsOeee+K3v/2t6hGTICb+9a9/4c9//jMOOeSQWLeZiIi2sIaYDg0Rez6aupowsciFTBExWhEwZ8HpXYcC5wQu6kxEROkVxGR+2MyZM3H66aerwh05OTmb7DNjxgxWVyQiSgBDWyV0ez6CuhGtvhA81szpEYsOT4yWsK/i0EQiIkqnIHbrrbfi8MMPh9nc9497IBDomSu22267qQsRESWgYqKjAM1d3WuIZdkzZ45YdHiiBLFClwWrmhjEiIgojeaIXX755Whvb9/k/uXLl+MPf/hDLNpFRETbGcRkfphwZ1CxDuG3dQexfJcV61u9iOh6optERES0iUH/dZby9Ndffz00TYOu69hrr7363U/mixERUWKLdfhH7I+mDYs5S/n6TKLWEvPVotBpQCCso6EjgEK3NdHNIiIi6mPQf51POukkjBs3TlVGPO2003D33XcjKyurZ7sENLvdjvHjxw/2kEREFGshLwzehu7S9a0BuCwmmAyypHPmCFgL1FpiI8ytPSXsGcSIiCjZbNW/SXfZZRd1/dFHH6GkpESFLyIiSh7Gtip1LUGsqVpK1xuRaWRooihDPTTYsK7FhxnliW4VERHRNgaxK664AldddRVcLhfuueeeze57yy23DPawREQU4/lhQuaINXS2Ztz8MOG3dAcxl68aOY7xqGIJeyIiSscFnYmIKHkY2iuhG0zQ7bloyrDFnKN0owUBSzYcXVXId1lQ2cxFnYmIKPkM+l+lvXu52ONFRJS8hTqkNyysG9DsDcKTYYU6+qwl5l2PAq4lRkRE6dQj1tnZiTvuuAMrV65UxTsuu+wyTJs2TRX0WLduXexbSUREW1W6vtUbgBRtz9QgFl1LrMBl5dBEIiJKnyB27bXX4tNPP1XFOt588028//77uPnmm5Gfn4/rrrsu9q0kIqJBMaggVoiGDaXrM7pHTAUxCzr8YbR6uxe3JiIiSukgJiHs9ttvx6hRo/Dee+9h//33x+GHH64Wc/72229j30oiIhoUY7sMTSxEU2d38HBn4Bwx4bflw+atRZGju7ovhycSEVFaBDFZ0NlsNsPn8+Grr77Cvvvuq+5vbW2Fw+GIdRuJiGgQNH8bDP627iDWFYDdbITFmJnLjPhshdAQwXBDo7pd1cKCHURElFy2aczK7rvvjj//+c8qdBkMBhx00EEqkN1www044IADYt9KIiLaIkNbpbqWOWKN64PIytBhicJvK1LXuYF1cFvzOE+MiIjSo0dM5oPtsMMOsFgsuPfee9XaYkuXLlU9Y7LWGBERxZ+xfcMaYs5CNHYF4MrANcSipHx9RDPD0blWzROrZI8YERElmW36K+12u3H11Vf3ue/000+PVZuIiGgbS9frRit0iweNndUYnmNHxtIM8NkLVRDLc+6Nqmb2iBERURoEsWAwiNdffx0//vgjQqGQmjPWG9cZIyJKUOl6ZxEi0NDUFcSOxW5kMr+1QAWxQpcFX69pTnRziIiItn9oogw/vOmmm9Dc3LxJCCMiosQwtEbXEAsirOvIytCKib0Ldjg716i1xBo7g/AGw4luEhER0fb1iH3wwQdqbthee+21LQ8nIqIhYGxbjVDeRNR3+tXtLHvmzhETfnsR7DUfo8DZ/T/HdS0+jC1wJrpZRERE294jJnPEioq6K1IREVESiITVHLGIcxgaO6KLOWd6j1gBDHoII43dwxK5lhgREaV8EDvvvPPU0MQVK1aoOWJERJRYhs4aaJGgmiPW0BGAy2KEOUPXEIvy2wrVdUFoHWwmAyoZxIiIKIls07iVhx56CHV1dfj5z3/e7/bFixdvb7uIiGgrGFtXq2sJYvWdgYxeQyzKb81DRDPC2VmJQnch1rJyIhERJZFt+kt96623xr4lRES0zYxta6BrBkQchWjoXA2PPbOHJSqaEX6bVE6sRKFrD6xhECMiolQPYrvuuqu67ujowNq1azF27FgEAgG1sDMREcWfsXUNIvZ8wGBCQ2cAOxTx93F0eKKjcw0K3VZ8wxL2RESU6nPEJHTJgs4SyI477jjU1tbi8ssvx5lnnonW1tbYt5KIiDbLIEHMWYRAKII2XwhZ7BFTfNYCODvXosjdXcK+M8B5zURElMJB7LbbbkNFRQVee+01WK1Wdd+FF16o1hW78cYbY91GIiIaxBwxGZYoYUNwjlg3v70Q9q4qFDq7g2klhycSEVEqB7H3339fLeo8YcKEnvvk6xtuuAGfffZZLNtHRERboutqjpiqmBhdQyzDS9dH+WxFMEYCGGFuUbdZsIOIiFI6iHV2dsJut29yfyQSQTgcjkW7iIhokDR/CwyBdrWGmMwPM2oanFb2iPUuYZ8fXA+31cQgRkREqR3EDjjgAPztb39TxTqiKisr1bDEfffdN5btIyKirShdL0FMhiUaMnsJsR5+az50GODoXItCl4VBjIiIUjuI/eUvf4HJZMJuu+0Gr9eLY489FgcffDA8Hg/+/Oc/x76VRES02YqJIixriHUE4OGwxB66wQS/Lb+7hL3bijVNXYluEhERkbJNY1daWlpw9NFHY/LkyWpu2Jo1a7D33ntj9OjR23I4IiLaDmp+mNUDmB1o6PCjwNVdRIm6da8l1l058cfqNui6Dk1jlyEREaVQEPvqq69wyy23YPny5eoPWZT8QXvzzTdVCfuZM2cORTuJiGhza4g5hknNDjU0cUw+1xDrzWfdsJZYsRUd/jBavEHkOCyJbhYREWW4QQ9NnD17Ns466yxMnDgRTz31FL7++mssXLgQ33zzDR5//HHVG/bb3/4WP/zww9C2mIiI+jBI6XpnATr8IQTCOrLtLNSxcQl7GZpYtKGEPeeJERFRMhj0X+t7770Xp59+Oi699NI+92dlZam5YnKRr++77z48+OCDQ9FWIiIaoFhHsGwWGjsD6jZL1/flsxXCFPaizNLRE8SmlmYlullERJThBt0jtmTJEjUvbHOOP/54LFq0KBbtIiKiwQh5YeyqU6Xr63uCGHvENg5iIstXhVyHmT1iRESUWkHM5/OpHq/NycnJQVNTUyzaRUREg2BsXdtTMbGhIwC7yQCreZsK4qb1WmI6NDilhL3byiBGRERJYdB/raU4h8Gw+d2laEfvIh5ERDT0FRN7ryHG0vWb0g1mBKy5cHSuRqGLJeyJiCg5bNX4lXfeeQcu18DVuNrb22PRJiIi2oqKibrRCt2Wg4aOZng4LLFfPvswONtXoSjbgq9XNyEi/1xkCXsiIkqgQf/FLikpwaOPPrrF/YqLi7e3TURENEjGNqmYWCRDEtQcsTF5zkQ3KSl5bcPg6liJwjKrqixZ1+7HMI8t0c0iIqIMNugg9vHHHw9tS4iIaBvXECtEOKyjuSsITxl7xPrjc5SgsPa/KHJ2D7GXeWIMYkRElEic0U1ElOJriEmhjiZvEDJDl6Xr++e1F8OghzFcq4VR41piRESUeAxiRESpKhKGsb2qu1BHR3fpei7m3D+fvXvYvKdzFQpcrJxIRESJxyBGRJSiDB3roUVC3WuIdfghpSfcVvaI9Sdo9iBkcsLVvhIFLgvWNrNyIhERJRaDGBFRijK2rFTXEVcx6joCaiFnI3+r90/T1PBEZ8cqFLmtWN3EHjEiIkos/skmIkrhICZrZEUcBajr8CPbbkl0k5K+hL2rfQWGeayobvPBH4okuklERJTBGMSIiFKUqWWFGpYIzaDKsXN+2OZ194itxjC3FRFdCnZweCIRESUOgxgRUQr3iIVdw1SokDXEsu2cH7algh2mcBdGm1vU7VWNDGJERJQ4DGJERCkcxGR+WEtXAOGIjhwHg9jmeB0l6rogsBYemwmrmxjEiIgocRjEiIhSUcgLQ/s6RFwlqG3vLl2fxR6xzfJb8xHRzHB1rFTDE1c1smAHERElDoMYEVEKMraugQYdYVdJT+l6Lua8BZoBXvswONtXqoIdqxo7E90iIiLKYAxiREQpXbq+RJWuz2bp+sFXTuxYiSK3DWtbvGpIJxERUSLwzzYRUarODzO7oFvcqOvwIYul6wddsMPZvgLFHiuCYR3rW32JbhIREWUoBjEiohRk2lCoQxYqrmuXioksXT/Ygh3WQDOG27sD2CoW7CAiogRhECMiSkHG5goVxCIRoL4jwIqJgyRzxERpqAo2kwGrWcKeiIgShEGMiChl1xArRrM3gLCucw2xQfLZhkGH1l05UQp2sEeMiIgShEGMiCjFaL5mGPwtqkdMhiUKBrHB0Y0W+G0FGwp2SOVEBjEiIsrAIOb3+3HllVdi5syZmDVrFh599NEB9120aBGOP/54TJ06FcceeywWLFjQZ/tbb72Fgw46SG0///zz0dTUtMkxdF3HGWecgVdffbXP/c3Nzbjwwgux884744ADDsAbb7wRw1dJRDQ0FRN7l673sHT9oHlt3SXsiz02taiz/G0gIiLKqCB22223qUD1xBNP4JprrsE999yDd999d5P9urq6cM4556jAJiFKAtO5556r7hfz58/HVVddhQsuuAAvvPAC2tracMUVV/Q5RiQSwY033ogvvvhik+PLvu3t7eqx5513Hq6++mp1TCKipC5d7xyG2g4/S9dvJZ+juGdoYmcgjIbO7l5FIiKieEpYmS0JUS+99BIeeughTJ48WV2WL1+OZ555Boceemiffd9++21YrVZcdtll0DRNha7PPvtMhbZjjjkGTz/9NA477DAcddRRPQFv//33R2VlJcrLy1FbW4tLLrkEVVVV8Hg8fY69du1afPLJJ/joo49QVlaG8ePHY+7cuXj22Wex0047xfU9ISIadOl6ewFgsqGu3c/S9VvJay/GsPXvo9TR3RMmwxMLXNZEN4uIiDJMwv6HumTJEoRCIdW7FTVjxgzMmzdP9V71JvfJNglhQq6nT5+uAlN0u/SWRRUXF6OkpETdLxYuXKjue+WVV+B2uzc5tmyTENa7HT/88MMQvXIiou1jalmBsKu7+h9L1289r6MUGnSM1KtgNGhY3eRNdJOIiCgDJeyvd319PXJycmCx/PSf3Pz8fDVvrKWlBbm5uX32HTt2bJ/H5+XlqR40UVdXh8LCwk2219TUqK9l3pdcBmpHf4+VXrSttSEnxkT0WLE8JsUGz03yypRzY2xegXDWCER0qGF1OwxzIelpva71xAcxkd1RgSL3DmqeWLp/z2xOpvzcpBqel+TFc5O8tCQ5N4N9/oQFMa/X2yeEiejtQCAwqH2j+/l8vs1u35Z2DOaxG8vL69vbFgtDcUyKDZ6b5JXW50ZGDLSthmnMPmg1GlXp+mE5TjidNqQCpyMZ2mmD316EPP8qlOXORFWbH/n5afw9M0hp/XOTwnhekhfPTfLKS5Fzk7AgJnO+Ng470ds2m21Q+0b3G2i73W7f5nZs3IbBaGxsR6yKb0mSlm+iWB6TYoPnJnllwrkxtFUhN+RHpykfK6pb1X02TUdnpw9JTesOYZ1dvoT3iIlOewks9YuQ7zbhm7XNaGhoR6bKhJ+bVMTzkrx4bpKXliTnJtqOpA1iRUVFqmy8zBMzmUw9wwQlAG1cUEP2bWho6HOf3I4OKRxoe0FBwaDasa2P3Zic8Fif9KE4JsUGz03ySudzY+gpXV+Mumq/muibEqXro+cjSc6LDE/Mr/8Kw0qtaOwMot0Xgsua2XPt0vnnJpXxvCQvnpvkpafIuUlYsY5JkyapABYtuCHmzJmDKVOmwGDo2yxZG0yKZ0TXepHr77//Xt0f3S6PjaqurlaX6PbNmTZtGtatW9cznyzaDrmfiCgZ54fpBhMijkLUdUjFRDNL128Dr6MMVn8jRtq7exJXNHQmuklERJRhEvbnW4YNSrn5a6+9Vq3Z9eGHH6oFnU899dSe3jGZ+yWknL2sDXbTTTehoqJCXcvcLilZL0488US1CLOUw5dqjFLmfr/99lOl67dE9pHFpC+99FL1WDmGLA598sknD/E7QES0jaXrncMAzdBduj4VesOSUJeju1LuWH0NDBqDGBERxV9C/48qCynL+mGnnXYarrvuOlx44YU45JBD1DYJR7J+mHC5XHjggQdUT5WsGyYl5x988EE4HA61XUrgX3/99bj33ntVKMvKysItt9wy6HbIumNOpxMnnHAC7r//ftx8881cQ4yIkpKpuUINSxQ1bX7kOBjEtoXPXoSIZkZOp1ROtKKioSvRTSIiogyj6dHxfrTdZLJ3LIt1SBWvWB6TYoPnJnllwrnJfWw6gmWz0D7hV7jolR9x4Ph87FTSd15tspLKjslUVGTyvOvQlDcTv/eegVBEx0O/zswh6Znwc5OKeF6SF89N8tKS5NxE27ElnFlARJQiNH8rjF11CHvK1Pww+RuT6+i7/AYNntdRAnfbMpRk2bC8vrNnHjIREVE8MIgREaUIY9MydR12l6G2za++zuXQxO2aJ+Zqr0BZlhWdgTBq27vfUyIionhgECMiShGmpmXQNQMirhJUt/lgNxvgsBgT3ayUrpxoCnsxwdKoblewYAcREcURgxgRUQr1iEWcxYDRogp15No5LHF7dDm7KyeWBVepUFtRzyBGRETxwyBGRJQiTE1LEXaXqq+lR4wVE7dP0JyNoMkFT3sFSrNs7BEjIqK4YhAjIkoRxqaliLjLENGB2g4/8pzsEdsumqaGJ7ral6Mky45l7BEjIqI4YhAjIkoBmq8Fxq56VTGxqTOAYFhnoY4Y8DpK4W5dqnrE1jZ7EQhFEt0kIiLKEAxiREQpwNi8XF2H3eWo2VDdj6Xrt1+XoxSOrkoM92gIR3SsbuLCzkREFB8MYkREKTI/LFoxsabNB5NBg9tmSnSzUp7XWQaDHsYEw3p1m/PEiIgoXhjEiIhSqmKiWVVMzLFbYNAS3ar06BET+d4Vas4dKycSEVG8MIgREaXIGmLRiok17T7kOtgbFgsRox0+awHcbctQmmXFcgYxIiKKEwYxIqIUqpgoqmUNMVZMjOl6Yu7WZapyIocmEhFRvDCIERGlTMXEcnT4wugMhFkxMYa8jnJ42pagzGNFQ2cALV3BRDeJiIgyAIMYEVEKzA8TYXeZGpYoWDExdjqdw2EJNGOMvUPdZq8YERHFA4MYEVGSMzUv66mYKMMSRQ57xGKmy1mursdEVqlqlAxiREQUDwxiRESpVDGx3Yccm0kFBoqNgDUfIaMd2e1LUZJlw7K67p4xIiKiocQgRkSUAmuIybBEUdPqRzaHJcaWpqHLORzu1iUYnmPHopr2RLeIiIgyAIMYEVGSMzUuQ2RD6fpqKV3PiolDMjzR07oYI3LsWNXUBW8wnOgmERFRmmMQIyJKYpqvGQZvd8XEQCiCpq4g8jg/LOa6HOVwdFZibJaGiA4sreXwRCIiGloMYkRESb6Qswi7y3sKdeQ6GcRiTYYmatAxXlsLs1HDoloOTyQioqHFIEZElMSMDQuhG8yIuEtQ1epV9+VzjljMeR0liGhGVbCjPJvzxIiIaOgxiBERJTFTw0I1LBEGE9a1dFdMtJj4qzvWJOz67MWqYIfME1vIIEZEREOMf82JiJKYqV6C2Aj19boWL/Jc7A0b2oIdSzAi14GqFh/afaFEN4mIiNIYgxgRUbIKB7tL12eNhK4Dla0+FLisiW5VWhfscLcvx6ic7rDLeWJERDSUGMSIiJKUsaUCWiSoglibL4iuQBgF7BEb0oIdxrAPIw21sJkNnCdGRERDikGMiCiJ54eJcNYIVLX61Nf5XENsSIcmiqy2pVzYmYiIhhyDGBFRkjLVL0LYOQwwO1DV7IPFqMFjY+n6oRIyu+G35HbPE8txsGAHERENKQYxIqKkrpi4oVBHq1f1hhm0RLcq/XvF3G1LMTLXjvqOABo6A4luEhERpSkGMSKiZKTr3UEsqzuIVUnFRA5LjGvlRLGYvWJERDREGMSIiJKQobMaBn+LKtQRCuuoaQ+wUEecKida/Q0oNrbCbTVxnhgREQ0ZBjEioiRkalikriWI1bT7ENF1lq6Pgy7XiJ6CHSNyubAzERENHQYxIqIkJMMSIxY3dHueWlxY5DvYIzbU/NYChIwOeFoW9VRO1GURNyIiohhjECMiSkKmepkfNhLQNKxr8SHLZoLVzF/ZQ07T1HpiEsTG5DnR6gthTbM30a0iIqI0xL/qRERJyNSwoKdiYlUrC3XEU6drBLJaF2FMvkNVqfy+qjXRTSIiojTEIEZElGS0QDuMbWu7e8RUxUQfChjE4qbLOQJ273q49Q41PPH7ypZEN4mIiNIQgxgRUZIxNixW1xLE2nwhtPtDyGfFxLj2iAlP6yKMzXfi+8pWzhMjIqKYYxAjIkrCQh26wYyIu0TNDxPsEYsfn60IYaMNnpbFGF/oQn1nAOtau88DERFRrDCIERElGbWQs6ccMJiwrtULk0FDtp1BLG40AzqlYEfrQtUjpoHzxIiIKPYYxIiIkoyp/seeQh1rm71qIWcDf1vHVbRyosNiRFm2jUGMiIhijn/aiYiSSdALU+MShHPHqZurGrtQxIWc467TOQLOrkqYgu0YV+BiwQ4iIoo5BjEioiQrW6/pYYSzx6IrEFbzk4Z5bIluVsbpcnVXrPS0Lsa4Aieq2/yoaeM8MSIiih0GMSKiJGKunQvdaEXYU4Y1Td0LCQ9zc35YvHntwxA2WHqCmODwRCIiiiUGMSKiJGKq/QHhrFGqUMfqpi5YjBqyHQxicacZ0eUsV/PEXFYTSrI4T4yIiGKLQYyIKImYa79HKGes+npNUxeK3DYYpGwfJWRhZ0/LQvX1uHwn5nCeGBERxRCDGBFRktC8jTC2VyG8IYitUkGMhToSWrCjcw2MoS6MK3SiqsWHhg5/optFRERpgkGMiCiJ5ocJCWKt3hBafSEUcX5YwnS6RkCDDnfrEtUjJuZUcngiERHFBoMYEVESzQ+LWLMQcRSo+WGCFRMTx2cvQUQzIat1EbLsZpRm2fDVmuZEN4uIiNIEgxgRUZIwS6GO7DGApqn5YXazER6rKdHNyli6wdRTsENMHubGlyubENH1RDeNiIjSAIMYEVEy0HWY6ub2FOpY3eTFMLdVMhklUKdrJLKa56uvdyx2o9kbxJLajkQ3i4iI0gCDGBFREjC0robB36rmh0mHy+pmKdTB+WGJ1uEaA1fnapgCrRiT74TdbMCXq5oS3SwiIkoDDGJEREkyLFFIEGvsDKArEMYwN+eHJVqne7S6zmpZAKNBw8QiN75gECMiohhgECMiSgIyLDHsLIZucalhiaLIw9L1ieazFSFociI7OjxxmBsLq9vR0hVMdNOIiCjFMYgRESVLoY6cMeprqZjosZngtBgT3SzSNHS6RiO7eZ66ObnYDSnV8TWrJxIR0XZiECMiSrRwAKaGhb0KdXSiyMXesGTR4R7dXbBD15FtN6M828Z5YkREtN0YxIiIEszUuBhaOKDmh4UjwNpmL4ZxWGLSkB4xS7ANjs61P5WxX9WEcIRl7ImIaNsxiBERJZi5+lvoBjPCWSNR2eJFIKyrxYMpOXS4RqnraBn7ycUetPpCWFzbnuCWERFRKmMQIyJKMPO6rxDOHQ8YLaio71DV+Yrc7BFLFmGzC157cU/BjtF5DjjMRg5PJCKi7cIgRkSUSHoE5vXfIJQ3Ud2sqO9Esduqwhglj07XKGQ3z1Vfy7mZNMyFz1cwiBER0bZjECMiSiBj0zIY/C0I5e+gFnKuaOhECYclJp0O12i425bBEPap21NLPFhS14Gatu7bREREW4tBjIgogaQ3TNeMCOWMR227Hx2BMEqz7YluFm2kwz0GBj0MT8sidXtKsUf1jH1S0ZjophERUYpiECMiSiDz+q9VtUSYrGpYoihhxcSk43WUImywILvlR3XbbjFiUpELnyxvSHTTiIgoRTGIEREliq7DvO5rhPImqZsVjZ0odFlhNfFXc7LRDSZ0ukYiq6l7YWcxrdSDeeta0dQVSGjbiIgoNfGvPRFRghhbV8HorUcovzuILa+T+WHsDUvugh0/BbGdSrIgK4l9xuGJRES0DRjEiIgSOCxR1wwI5U5AizeIxq4A1w9LYh2uMbD7amH11anbHpsJ4/Kd+JjDE4mIaBswiBERJbBQRzhrFGB2oKK+S91XmsVCHcmqwzNGXec0zum5b1ppFr5d24IOfyiBLSMiolTEIEZElMCFnHvmh9V3INtmgstqTHSzaABBS45a2Dm34X995omFIjpmr+SaYkREtHUYxIiIEsDQVgVjx/qf5oep9cPYG5bs2j0TkNcriOU6LRiZa2f1RCIi2moMYkRECZofJsJ5k+ANhLGu1cf5YSmgLWsinJ1reuaJRYcnfrmqCb5gOKFtIyKi1MIgRkSUoCAW8oyAbnFhReOG+WHZDGLJrs0zQV33Hp64c1kWfKEIvlzdnMCWERFRqmEQIyKKN12HpWq26g0Ti2vb4bKYkGM3J7pltAUhSxa6HGV9gliR24rybBveX/JTLxkREdGWMIgREcWZsWUljO1VCBZNU7eX1LRjeI4dmpboltHg54l90+e+GeXZqmBHV4DDE4mIaHAYxIiI4syy5mPoBjNC+ZPR5g1hXZsfw3NZqCOV5ok5utbB1lXdc9+M8iz4QxHMXsnFnYmIaHAYxIiI4syy5iOECiYDJiuW1HWo+0bkMIilUo+YDq3P8MQClxWjch14f0l9QttGRESpg0GMiCieAp2qUEewcOee+WEFTgucFq4flipCZhe6nOXIbfy2z/3SKybVE7m4MxERDQaDGBFRHEmRDi0SQqhoZ6nZgUU17Shnb1hazBObXp6FYETHpxUcnkhERFvGIEZEFEeWtZ8g7CpBxDUMte1+tPpCHJaYovPE7N5q2Dureu7LdVgwNt+J95eyeiIREW0ZgxgRUTzL1q/+CKHCaT3DEg2ahtJsBrHUnCdmQG7jT/PEosMTv1nTglZvMGFtIyKi1MAgRkQUJ8ampTB2ViM4LDo/rAMlHissRtatTzVhkwOdzuHIq99oeGJZFiK6jk+WNySsbURElBoYxIiI4lm23mhFKG8SwhFgWV2HWj+MUlNb9mTk13XP+YvKspsxodCFdxZzeCIREW0egxgRURyDWKhgR8BoweqmLvhCEYzIdSS6WbSNmnOnwRJsRXbz3D737zYiG99XtaKmzZewthERUfJLaBDz+/248sorMXPmTMyaNQuPPvrogPsuWrQIxx9/PKZOnYpjjz0WCxYs6LP9rbfewkEHHaS2n3/++WhqaurZpus67rjjDuy+++7YddddcdtttyESifRsf/zxxzFhwoQ+l7/+9a9D9KqJKBNp/jaYq79FsOinsvVWowGFLmuim0bbqNM1CgFLNgqrP+lz/86lWWq4KXvFiIgoaYOYBCIJVE888QSuueYa3HPPPXj33Xc32a+rqwvnnHOOCmyvvvoqdt55Z5x77rnqfjF//nxcddVVuOCCC/DCCy+gra0NV1xxRc/jH3vsMRXU5Ph333033nzzTXVfVEVFBU466STMnj275yJhjogoVsyVn0HTwz2FOhZWS9l6G4wcl5C6NANacqaiqOYjVYglymY2YlppFv6zsFb9I5CIiKg/CfsIICHqpZdeUgFq8uTJOPjgg3HWWWfhmWee2WTft99+G1arFZdddhnGjBmjHuN0OntC29NPP43DDjsMRx11FCZOnKgC3qefforKykq1/cknn8RFF12kgpz0il1yySV9nmfFihXqcQUFBT0Xl8sVx3eDiNKdreJNhLJGI+IsRLsvhFVNXRiV60x0s2g7NefuDEdXFVztK/rcv9vIHKxp9qqCLEREREkVxJYsWYJQKKR6t6JmzJiBefPm9Rk2KOQ+2aZp3ZXF5Hr69OmYO3duz3YJWVHFxcUoKSlR99fW1qK6uhq77LJLn+dZt24d6uq6h42sXLkSI0eOHPLXTEQZKtCpytYHS3fv6Q0To/I4PyzVtWVNQthgQ2HNx33un1joQpbNhLcX1SasbURElNxMiXri+vp65OTkwGKx9NyXn5+v5o21tLQgNze3z75jx47t8/i8vDwsX75cfS2BqrCwcJPtNTU16rGi93Z5HiHbDQaDer7XXntNDWeUnrfjjjsOZ5xxRk/wG6yt3H1Qx4rlMSk2eG6SV7KeG8uaD6GFfQiW7gFp2vzqNgzzWOGyGpExtF7XaTRaTzeY0Zq9owpiK8ef03O/0aBhl+HZeG9JPX6/32iYkngMarL+3GQ6npfkxXOTvLQkOTeDff6EBTGv19snhIno7UAgMKh9o/v5fL4Bt8u23sfe+HmkNywa3O677z4sXrwYN954I4xGI04//fStek15ee6t2j9Rx6TY4LlJXkl3bj58G8gfD0/JKATDOhbVtGO30XlwOm3INE5H+r3mruJdULbwPuSb2xF0FPXcf9COxfhwWQMWNvlw4KSf7k9WSfdzQwrPS/LiuUleeSlybhIWxKTnaePAFb1ts9kGtW90v4G22+32PqFL9uv9PLJd5qd9/fXXqndOSMVEqbj43HPPbXUQa2xs7z1fe7uTtHwTxfKYFBs8N8krGc+NFmhH7vIP4Jv4KwRau7CopkOVrR/usaKzM4PKm2vdIayzy5dWPWLC55iEUhhgX/4OGkae0HN/tklDWZYNz321GlMLkncYajL+3BDPSzLjuUleWpKcm2g7kjaIFRUVobm5Wc0TM5m6myHDCCVceTyeTfZtaGjoc5/cjg43HGi7FN2QbdFjl5WV9XwtZLuIhrAoKQgic8u2lpzwWJ/0oTgmxQbPTfJKpnNjWfk+tHAAgdI9VP6Yv74VbqsJ+c6+vfhpL3o+kuS8xFLY7EK7Z7wanljZK4iJXUfk4M2FNWjuCiLbbkYyS6afG/oJz0vy4rlJXnqKnJuEDVqfNGmSCmDRghtizpw5mDJlipq31ZusDfbDDz/0lAGW6++//17dH90uj42S4hxykfsliEnhjt7b5Wu5T4KcVG782c9+1qfEsAxPHD169JC+fiLKDNbl/0YodwJ0R776ozBvfRtG5zkSPn6dYr+4c179NzCGOvvcv/vIHER0sGgHERElTxCTYYFSbv7aa69V64B9+OGHakHnU089tafXKjq/69BDD1Vrg910001qzS+5lnljUrJenHjiiXjjjTdUqJJqjFLmfr/99kN5eXnPdlnQ+ZtvvlGXO++8s+d59txzT/VcsoDzmjVr8J///AcPPfSQKqVPRLQ9NF8LLJWfqt4wUd3mQ1NXEKPzWLY+3TTnTodBD6Kw5tM+93tsJuxc6sEr86q5phgREfWR0DJOUqVQ5middtppuO6663DhhRfikEMOUdtmzZql1g8TsqbXAw88oHqyjjnmGFWW/sEHH4TD0T3mXkrgX3/99bj33ntV6MrKysItt9zS8zxnnnkmDj/8cLXg88UXX4xf/vKXPfO/SktL1bGkx+3II49UIU3WGZP9iYi2h2XV+0AkjGBJd9n6+evaYDZqaiFnSi8BWz7aPONRUvn6Jtv2HpOHtc1efF/VmpC2ERFRctJ0/osuZhoaYlusIz/fHdNjUmzw3CSvZDs3WW/+BobOOnTsfa26/dcPKyAjr3+54zBkIqkSmc4FSvJrP8OoFU/ivwd/AL/9pyqJ8mf2uneXYacSD276+aSEtjEVfm6oG89L8uK5SV5akpybaDu2JHkXNiEiSmFaZx3MlZ8jULanut3mC2FVUxdG53JYYrpqytsFEYMJJVVv9rlf1qScNToXHy9vQFNX3wq/RESUuRjEiIiGgG3Za4BmQLC0O4j9UNWq1jIem8RlzGn7REx2NVesdO3rm5Tr2mNkjjr/by1g0Q4iIurGIEZEFGu6DtuSFxAcNhO6xaXu+nZtC0bkOGA3GxPdOhpCDYV7wdW5GlktP/a532k1YXp5Fl6ZX40IxzIRERGDGBFR7JkaFsDUtAyB4fuq2y3eICoaOjG+kMMS011b1iT4LbndvWL9FO1Y3+rDN2uaE9I2IiJKLgxiREQxZl38IiK2HIQKu9c6nFPZCqOmYWw+g1ja0wxoLNgDxevegSHs77NpTJ4Dw3PseOrbqoQ1j4iIkgeDGBFRLIUDan5YoGwWYOgehvjd2haMzLXDZuav3EzQULgnzKF2FNZ8sknRjkMmFqhhqotq2hPWPiIiSg78VEBEFEOWNR/B4G/pGZbY2BlQ1RLHF3bPFaP057MXo909FqVrX9tk2/TSLBS6LHjif5UJaRsRESUPBjEiohiyLX4RoewxiHiG/zQs0aCpYWmUOeoL90ZB/RdwdKzpc7/BoOHgCQX4ZHmDWuSZiIgyF4MYEVGMaF0NsKz5pKc3LDoscXSuAxYTf91mksaC3RE0uzF81bObbNt9ZA7cNhOe+pa9YkREmYyfDIiIYsS2/HXIYlHBsr3U7br2ANa2eFktMQPpBjPqivZF2drXYAx29NlmNhpw4Lh8/GdhLRo6+hb0ICKizMEgRkQUq7XDFj23Ye0wt7rr27XNMBs1jM5jEMtEdUX7wxj2obTyjU227TMmDyajhmfnrEtI24iIKPEYxIiIYsBUNw+mpqUIjNhf3Y5EgC9WNmFCgUuFMco8QWsOmvJmYsSqZwA90meb3WLEfmPz8eLc9ahtZ68YEVEmYhAjIooB2+IXELHn9awdtqi2HU3eIKaUeBLdNEqg2pKD4Oxci/y62Ztsk1L2FqMB93+xOiFtIyKixGIQIyLaXkEvrLJ2WPm+akFf8fmKRhQ4LRjmtia6dZRAHa4x6HCNwoiVT2+yzW424ojJhWqu2NK6vvPIiIgo/TGIERFtJ+vKd2AIdiAwfD91u8UbxPz1bZhS4obGUYmZTdNQW3wQCuq/hLN9xSab9x6dh2EeK+7670roup6QJhIRUWIwiBERbScp0hHKn4yIa5i6/eWqZpgMGiYVdhftoMwm88QClmyMqnhsk22yxtzROxXju8oW9X1DRESZg0GMiGg7GFrXwLL+K/iH/1SkY/aKRowvdMFq5q9Y6i5lX1PyM5RUvQlb1/pNtk8pdqslDv7x6UqEIuwVIyLKFPyUQES0HWxLXoRuciBYslufIh07sUgH9SJrioWNjn57xTRNw3FTi7G6uQvPfFeVkPYREVH8MYgREW2rSBi2xS8iULYnYOouyiG9YSzSQRuLGG2oLT4QZWtfgcXXsMn24TkOHDS+AA98sRqrGrsS0kYiIoovBjEiom1kWftfGDurERhxgLpd1x7APFWkw8MiHbQJCWK6ZsTIlU/2u/0Xk4uQ67TguneXIswhikREaY9BjIhoG9kWPYNQ1iiEs8eo2+8tqVML9U4exiIdtKmwyYm6Yftj+KrnYQ60brLdYjLg1F3KsKimHc/O4RBFIqJ0xyBGRLQNDB3VsKz+CIGRB6oS5c1dQXy9uhkzyrJgNrI7jPpXU3wwND2E4aue6Xf7mHwnDhifrxZ5Xt3EIYpEROmMQYyIaBuLdMBgQqBslrr9wdJ6mIwaprJIB21GyJKF+qJ9MHLFU/32iolf7jgMOQ4LrnprMfyhSNzbSERE8cEgRkS0LUU6Fj6LQOmegNmBdl8In69sxM6lHjW8jGhz1pceAUMkiNHLH+p3u3wPnbX7cKxq6sLtH1XEvX1ERBQf/MRARLSVzJWfwdixDoGRB6nbHy/vroK3c1lWgltGqdIrVl36Mwxf9SxsXdX97lOeY8evdy7FGwtq8OaCmri3kYiIhh6DGBHRVrIvlCIdIxHOGQtvIIxPljdgp2IP7GZjoptGKUIWeA4b7Ri35J8D7rPX6FzsOTIHt364HMvqOuLaPiIiGnoMYkREW8HQWQvL6g+6S9ZrGt5fWo9gRMeM8uxEN41SbF2xdeVHoqTqTbhblwy436+nl6LIbcVl/16EFm8wrm0kIqKhxSBGRLQVbIufV0U6gmV7q0qJUqRjRmkWXFb2htHWaSjcGz57EcYv+tuA+8h8sXP2HIE2XwiXvrEQARbvICJKGwxiRESDFfbDNv9xBMr3hm5x4vUfq9UH5V1GsDeMtp5uMKFq+LEoqP8SeXVfDLhfgcuKc/cagQXV7bjpg2XQdS72TESUDhjEiIgGybrsdRi99fCP+TnWNnnxzZoW7DEiB1ZWSqRt1Jw7HW2eiZg8/wYYwr4B9xub71SLPb+9qA6PfVMZ1zYSEdHQ4KcHIqLB0HU45j6A4LCZCLtK8NLc9ch1mLFjMdcNo+2gaVg95hTYvLUYs+z+ze6664gc/HxyEe77YjXeXVwXtyYSEdHQYBAjIhpkyXpT0zL4xxyB+evbsLyhE/uMzoORv0VpO/nsxVhf9nOMqngMrtalm933iB0KsfvIHFz37lJ8s7o5bm0kIqLY40cIIqJBkN6wUPZoeHMm4eW56zE8x45ReY5EN4vSRHXpYfDZh2HHedcAenjA/TRNwykzyzCxyIVL/70Qi2vb49pOIiKKHQYxIqItMDYuhqXyMzU37MNlDWjsDGC/sXkyqowoZoU7Vo85DVktCzFi1XOb3ddo0HD2HiMwzG3Dxa8sQFWLN27tJCKi2GEQIyLaAvvchxGx56MmezreXlyLncuzkO+0JLpZlGY63GNRN2w/jFv8Dzg6Vm92XykQc/7eI1XVzv97aT4aOvxxaycREcUGgxgR0WYY2qpgW/Yq/KMPxUvz6mA1GlWlRKKhUDnieAQtWZg651Jo4cBm93VZTbhon1HwBSM4/+Uf0ebjgs9ERKmEQYyIaDOc39wO3eLCXMcemLu+DfuMyVW9EERDIWK0YcW4c+FuW47xS/6xxf3znBZcuM8o1Lb7cfGrC+ANDjy/jIiIkgs/TRARDcBYvxDWZa+ic9yxeHZuI8qz7ZhQ6Ep0syjNdblGoHLEcRi14gnk183e4v4lWTZcsPcoVNR34tI3FiIYjsSlnUREtH0YxIiIBuD66iZEXCV42zdFFejYfxwLdFB81BYfhJacnTDl+yth8TVscX+p4Pn/9hqJOZWtuPo/SxCK6HFpJxERbTsGMSKiAdYNk0qJDWNPwH+WNGDnMhbooDjSDFg55gxoegTTvvsjtMiW539JSfuz9hiO/1Y04Ob3lyGiM4wRESUzBjEioo3pETi/vAmh3Al4cn0xTEaDWkSXKJ5CFg8qJpyH7OZ5mPTjzYN6zLTSLJy2azneXFiLv/93JXSGMSKipMUgRkS0Eeuy12BuWIilJcdiTlUb9hmdp8qFE8Vbh2ccVo8+BcPXvITyVc8P6jG7jcjBr6eX4Pnv1+Ghr9YMeRuJiGjbmLbxcUREaUnzNsE1+zr4S3bHQxV2lHiASUUs0EGJ01C0NxxdlZi04BZ0ukejKX/XLT5mv7H5qqz9Q1+tRZbNjF9NL41LW4mIaPD4L14iol5cn/8FiATxgeuXqGnzY/9xBSzQQQm3duSv0O6ZgGnf/g6utmWDeszPJhbgoPH5uOOTFXhnce2Qt5GIiLYOgxgR0QaWVe/Dtvx1tEw8Ba8s9WJKsRtFbhbooCSgGdV8saAlB7t+eSZcbRVbfoim4dipxdhzZA6ufWcpZq9sjEtTiYhocBjEiIjkQ6u/Fa7/Xo5g0XQ83zQWkQiw16jcRDeLqEfY5MTSHf6IoMmFXb48A872FYMKYyfPLMNOJR5c/uZiLKhui0tbiYhoyxjEiIgAOGdfD0OwAytHnYrPVzapKokOizHRzSLqI2R2YenkPyJscmDXQYYxo0HDmbsPR3m2Db97dQEqm71xaSsREW0egxgRZTwZkmhf8gK6dvgNnl3sQ7bdjGmlnkQ3i6hfIbMHS3a4BGGjDXt8fhIKqz/a4mPMRoNa8NluNuKiV35Ec1cgLm0lIqKBMYgRUUYzdKyH+6M/IFi8C74zz8TS+g7sOyZP9SIQJfMaY4t2vBJtngmY/u3FGL/o79Aioc0+xmU14YK9R6HNH8LvX1sIXzAct/YSEdGmGMSIKHNFwnB/cBFgMKJ1p3Px0rwajMixY1SeI9EtI9qiiMmOignnY+2I4zGq4jHM/Poc2LqqN/uYfJcF588aiYr6TlzzzhJEuOAzEVHCMIgRUcZyzPknzNX/Q+eMC/HhKh+aOgPYb2wey9VT6tA01JQehiWT/wh323LM+uRIjFj5FKAP3Ns1IteB3+5ejk+WN+Jfs1fHtblERPQTBjEiykim6m/h+PZv8I8/Bo3OcXh7cS2mlWUhz8ly9ZR62rMm4cdpN6Axf3dMWvBX7P75yZtdb2xaaZYqbf/E/yrxxo81cW0rERF1YxAjooyjeRvhee88hHPGwzfhWLw6rxpGgwG7j8hJdNOItplUUlwz5hQs2vEKWPxN2PPT4zFu8T9gCPv73f/A8fnYe3Qubv5gOb6saIh7e4mIMh2DGBFllkgYng8uhBbyonOX32FFow//W9uCWaNyYDPzVyKlvg7POCyceg3Wl/1czR3b679HI7fhf/2uMfbr6aUYX+DEuU/NUfPGiIgofvipg4gyiuO7f8Bc+Tm6ZlyIsC0XL/ywHkUuKyYPY7l6Sh+6wYz15b/EgqnXImKwqjXHJv14MwyhvmuISXXQc/YcgTyXBRe8/CNq2nwJazMRUaZhECOijGFe+ykc3/4dvonHI1S4Ez5b0Yi1LV7sNy4PBv42pDTkc5Rg8Y5/wppRJ6JszctquKKn+cc++8jaYn86dCKkRo2EsRZvMGHtJSLKJPzoQUQZwdBWBc8HFyBUOBX+CcegoSOAV+etx5RiN0qzbIluHtHQ0QyoLT4YC3f6i7q5++zfYOySe6BFfgpc2Q4LLthnFJq6gvj9awvQGdj8mmRERLT9GMSIKO1pgQ5k/ec0NVyra8YF0GHA03OqYDEZsc+YvEQ3jyh+vWNTrkR16REYs/xB7P75SXC1Le/ZXuS24v9mjcSKhk6c/9KPaGXPGBHRkGIQI6L0X7T5/fNhbKtE5+5/gm714MtVTVhS24GDx+fDauKvQcocusGEdcOPwsIpV8McbMOen56AUcsfBSLdPWCymPnv9h2NNc1dOPfFeWjoDCS6yUREaYufQIgorTm/uhmWtZ+gc5eLEfGUo7kriJfmrscORW71oZMoE3W5RqqhirXFB2L84r9j6ltH9FRWlAWf/7DfGDR2BnD2c3OxvpUFPIiIhgKDGBGlLdvCZ+CY+wC8O56KUNHOCEeAJ76tVJXi9h2bm+jmESWUDNWtHHkCFu70Z6llryorTvv297B3VqEky4Y/7j8G/nAEpzz9Pb5Y1ZTo5hIRpR0GMSJKS7aFT8P138vhH/UzBEYfpu57Zd56LK3twKGTClWlOCLq7h1bNfMarBh3NnIbv8XeHx+BKXMux6jwalx+4FiMyLHjd68uwL9mr0Iooie6uUREacOU6AYQEcWabd7DcH1+LfyjD4V3yunqv/1frGzCx8sbcMDYPPXBkoh60TQ0FuyB5tydUVD7OYZVf4DSdW+hoWBPjBxzAp7Nm4TH/1eJuVWt+MuhE1CWzZ8hIqLtxSBGROnl87/B9fl18I07Er4dTlYfMCvqO/HsnHWqVP3U0qxEt5AoaUWMNtSWHIy6Yfur3rGi9R9gxne/wxRzFk4ZcQjuqJuBXz/Rjv+bNQq/2rlUDfMlIqJtwyBGRGlB87XA9fmfgWWvwTfhOLVos4SwyiYv7vtiNYqzrDhgXIHcRUSDqK4oPWRykTlj+fVfYof6D/B05CXU2Erx/OzdcM2ig3Hqz/bH+EJXoptLRJSSGMSIKOWZKz+D+6Pfq/XCMOuP8Ofvpu5fVNOO+79YjRy7Bb+YXAQjZ8USbTWvswyVzhNQOeJYeFoXI6/+G5zf+DbMba/iuxfG48PS47DbIafC7WIgIyLaGgxiRJSyDG1VcMz5B+yLnkOwYAq8e10DT/FwoLULX69uxpPfVqn5YEfsUAgL1wsj2j6aEW3ZO6qLNvoUZDXNRdHajzGz+ma0PHE3VpUdi5IDfweDqyDRLSUiSgkMYkSUcgytq+GYcw9sS16Gbnaga8pvERj9M2iaAYFwBC/PrcYHy+qx4zA3DhxfwJ4wohjTjRa0FOyqLuta10GveA9TK5+H9sTzWD7iZBQd8DsYHFwigohocxjEiCg16DrM676E/ccnYFn1HnSLG74dfg3/qEMAk03tIkU5nnp3KerbA9h7dB5mlmdxThjREDNklQIzzsDslqNgrXgTu61+CuHHnsP84aej+ICL4XRyyCIRUX8YxIgouQW7YFv6MuzzH4OpeTnC7jJ4p5yGwIgDAKNF7dLmC+E/C2vx6YpGlObYccrMUuQ6u7cRUXxkZ+cCM0/Df5sOh3vlv7HbmgdQ89jL+HfJBSjb5RhMLc1mlUUiol4YxIgoKRk6a2H78XHYFzwJLdCO4LBd0LHXXxDKn6yqIQpvIIwPl9Xjg6X16va+Y/Iwa0IhvF3+BLeeKHPl5hYAuWfiu9ZDUVTxLM6svgZfvfY8LjH+Brljd8c+Y/Kx64hsLqpORBmPQYyIkoqhrRKOOf+EbcmLgMEC/4j9ERh9GCLOwp59OnxhfLqiQS3Q7AtGMK3Mg12Hd3+wM3AsIlHSDFmsn3Ep/E3zscOql/C0/yr8d8UuuHnhcbjCMBwzyrKx1+hc7D0mF6VZXCCaiDIPgxgRJQVD65oNAexl6BYXfJN+Bf/IgwGzo2ef9a0+/LeiEV+tbkJEByYPc2O34dlw2/irjChZteXuhLacHZHX8A12X/s63tMvxxLnrnijbW888OmOuPMTC8bmO7Dv2HzsNzYPEwpd0PgPFSLKAPz0QkQJZWxeoQKYddlr3QU4Jp/UHcBMVrW9xRvEd2tb8M2aZlS2+FSv18zybEwr9XBoE1Gq0AxqceimvF2QX/8Fyupm4/LOO3CJ3Y6l7j3xfXAEvvi+EJd/XYyIqxj7jCvE/uPyMbXEAxPLnhJRmmIQI6KEMNXNh33uA6rKmm7NhnfHU+AffiBaQwasqunCsroGLKnrQHWbH0ZNw6g8u1qUeVSeAyZO+CdKSbrBhPqifdXF6qtDXv3XGNE8HxO9X+AUgw+wAZGQho7FDjQvdKJRcyFky4c9uwjZecVw5I9AOG8CQrkToNtyEv1yiIi2C4MYEcVPoBO25a/DtvBpmOt/RMCaj0Ulv8a3xulYXRVC1Y8V6AiE1a5ZNhPKs+2YWpqF0bkO2Mz8rzhROvHbCrG+/Eh1gR6Bxd8Ee9c6WIKtMAQ7EehqBbpaYfS1AtXrodfMhl1rhQkR9fiALR/hkl0QLtkdgZLdEc6bCBjYS05EqYNBjIiGTiQMU+NiGNZ8htCq/yKrYQ4MkQAWG8bj7eAJ+CEwFpF2A7JsHch3WrBjsRsFLiuK3FZ4OO+LKHPIYuy2fHXpT3Mwgh9afahu6UCwpQq2rvUY3lmDiSsWYuzKD+BCCEGTG8HyWQgP3weBslmIZI3sqbBKRJSM+EmHiLZdOKDKzBs6a9S1Ub7uqkWkoQKRxuVwdFXCpIfg181YpA/HIn1vLLdPhdldiEK3FSc4LchzWmA1sbeLiAYmPeKj8x3qAhQiFNkZte1+fNTqw3Mt7bC1rcRY3ypMWbEA41a9Bzci6LIWIVC6BwwjZiFYugcinuEMZkSUVBjEiGjzIiEYm5fD1LRMFdYwtmy4dFTD4G3ss2sIJrRobqwPZ6Naz0OTcSK8rnKEssdhWLYLo10WjOP8LiLaTjJPtDTLpi4Yno2IXo6mrj3wWYsPr7W0wN22FCO7VmKHiq8xcsUbMGg62ow5qHbvhJbcnREsmgrjsJ2Q5clGtt0MMwuCEFECMIgR0U/CAZialsJUNw+m+gWqoIapaQm0cEBtjlizEXaVIGgvwjrLGKzxObCsw4plHRY06W6YrC6U5zhQlm1HabYNYzi8kIjiQP6/I8Ob5YJSD4Dh6PCH8XW7Dx+0tcDVXoEC70oMb16Nac1fwLoyhIiuYZU+DN/pI1FpKEWTbQS6XCNhzC6DJ7sAZTlOFfRKsmxqzipL6hNRrCX0U5Lf78d1112H999/HzabDWeccYa69GfRokW45pprsGzZMowdO1Y9bscdd+zZ/tZbb+Guu+5CfX09Zs2ahRtuuAG5ublqm67ruPPOO/Hyyy8jEonguOOOwyWXXAKDofs/YM3NzfjLX/6C2bNnIycnBxdffDF++ctfxuldIBpa8v3vDUbQFQyjKxCGLxiGWYvA5a+Gq3MtXO3LYW9ZpuZySa+XFglC1wyIuMsR8IxA06jjUW8pw+pwASragNVNXtRU+9WxXRYTynNsmFhsx/Bcu/qwQkSUDFxWI8ZanZLQAJQC2BdtAL4PBYG2KtjaV8PZsRq7+tfjoMACOPztgPxqawRCuhEN8Kh/MDXCgTVwImh0ImT2wG92I2B0w2fyoMucgzZDlupt6zBkQTfZYDRoMBkMajilzHX12Mzqd6PMfy10W1DgtMLC4dhElOggdtttt2HBggV44oknsH79evzpT39CSUkJDj300D77dXV14ZxzzsEvfvEL3HrrrXjuuedw7rnn4oMPPoDD4cD8+fNx1VVXqXA2ceJE3HTTTbjiiivwwAMPqMc/9thjKqjdc889CIVCuPTSS5GXl4czzzxTbZd9fT4fXnjhBcybNw9XX301Ro0ahZ122ikh7wtRH7oOzd8CQ1cDtJBXXRDydc91MJiga6buawBN3rAKSusaW9DS2oyO9hYEO5uRE2nEMDRjmNaE4VodyrR6mLXu6oRe3YJKvQDrUYh1hoOwGsVYGymCt9mEQIMctZtRa0WBy6Lmdk0p9qAs26aG9PCfxESUSowmM5A7CsHcUWiRtQqj94c6YfPWwBJogTnYBviaYfe1wR7oxLCQD8ZwM8xhL2xBL+x6F6zoHinQmw9WtGlutMKNdt2OVt2OtogVnboNa2HFYtjQpdsQsbhgtnlgdWXD4cqBw5OLrKw85OTkIScrCzlOG8xG/nIlSncJC2ISrl566SU89NBDmDx5srosX74czzzzzCZB7O2334bVasVll12mhgZI6Prss8/w7rvv4phjjsHTTz+Nww47DEcddVRPwNt///1RWVmJ8vJyPPnkk7joooswc+ZMtV16w/7xj3+oILZ27Vp88skn+Oijj1BWVobx48dj7ty5ePbZZxnEKL5hy9cEY8sqmJqXw9hc0T0fq20NDB3rYAh2Deow0gc8dqP7IgYNXnMWvOZs+E1Z8FonY6ElH22mArSY8tGqZSMQAQJhHaGwjiwN2EkynqbBbjHCLRebCW6rSf2nl4goHYVNTnS6x6BzkPvL6AFTsEOFNnOwHaZQO0zqugNZwQ7khCW8eWEMd0AL+aCF/epiivhgjvhg8OqAF0D9psfu0q3waVbUaDb4DTaEDHaEjHbVCxcwZyFoyULImgO/oxghZwl0dwksWcXw2K3q93W2zcSFsIlSQMKC2JIlS1Tv1M4779xz34wZM3D//fer4YPRYYNCeqlkW3R8tlxPnz5dBSYJYrL97LPP7tm/uLhY9azJ/RaLBdXV1dhll136PM+6detQV1en9pH9JYT13h7tTaOhFY7o8IW6h8x1Ri/+kLru2HDt3TCkTq59wQgC4QiC4QhCEV3ySw+DGg6iqf8img0GVYlPLpYN1zazsft6w+2e7cbua5msLRd1DANgQBgGPQRDOKiuI6EAdBm2Fw4g1GxCS2MLIjJ3KuQHwkE1v0qLhNQfZ3WNMDQ9Ak3fcJxI93EMIS+MwTaYAm0w+Zth6VwHW+d6GMPdYUuHBq+1AK3WQnSYStGSNRmNyEJ9yIXmoIbaLqDRr0GDDiMiyLJqyLcbkeswqQqEuXYzbFYrIiYbIkYbwkabKg29MRuAYRsuRES0dXSDGUFrjrps/YN1tZSH/N43yt+EUBdC/g4EvR0IB33QA12IhHww6QHoQR8MMn834IPVXw2PvgIOeOHRO2DXfuqVC+gmrNfzsFYvxFw9Hy3GXHSac+Gz5EGzuGG2OWC22mExW+Ew63AYuy92QxAOLQA7ArBrQdi0IKwIwqoFYdHkb1pYrfMmf8t+okGXNduMFugGS/e12QHd7Oy+WOTapXr+5Fq2wdT9t0j+7svfc38ooi6BUESN6JCPePIpz2TUYDNt+HttNqq/yfEaxi9tC8olHEGke7m6HtIu+bwg18aBhoLoESASVOe3hxq9Yump2BkKR+AL/fQ5Ri7yvL3JrvIcpg3DXOU5uz+jaOqfpJQ+EhbEZC6XzMeSoBSVn5+v5o21tLT0zO+K7ivzwnqToYXSgyYkUBUWFm6yvaamRj1W9N4uzyOi2/t7bG1t7Va/JsmOvX/2tkf052xbjvni92vh/vbvOBqf9LpXh0F9xJfLhmOr+yIwajFqdBzI5Gppbd9Xov4kdL+eOL6WWAaYdqMT9YYS1OvZqEEOAiGzlCDcQP7Q1sOMesh3aqEBsLsNsJqNcFiMMPf+IyX/yu3snuaQqbzyX+DwRn9BKSnw3CQvnpv4i2y4BDe6X36ja0YjDEYnDEY3IuHuoBLccOnota8MlXSGW+AMtcAdbsFItGMkVgPqsoHkJ++GC8VU/6vexf5zT6jnE9xPn38G+rSj9fpMlKqf9TZH/gWt/rmsknv3q41+aJZ/AnTscwOQ/4uYfibfFoPNywkLYl6vt08IE9HbgUBgUPtG95P5XQNtl229j73x82zp2FsjN9e91Y8ZimOed/Bk4OCHkY7SdaCFe8NldKIbQkREREn1uafvp9TMpm1hm9RMHarP5Gn1uVbmfG0cdqK3pYLiYPaN7jfQdrvd3m+4i34t27d0bCIiIiIiorQJYkVFRapsvMwTi5JhghKAPB7PJvs2NDT0uU9uR4cUDrS9oKBAbYseu/fziOj2gR5LRERERESUVkFs0qRJMJlMquBG1Jw5czBlypQ+hTrE1KlT8cMPP6iJlEKuv//+e3V/dLs8NkqKc8hF7pegJYU7em+Xr+U+CXLTpk1ThTtkvljv7XI/ERERERFRWgUxGRYo5eavvfZatQ7Yhx9+iEcffRSnnnpqT69VdH6XlLNva2tT64NVVFSoa5nbJSXrxYknnog33nhDlcOXaoxS5n6//fZTpeuj2++44w5888036iKLO0efR/aRBaBlbTF5rBxD1hw7+eSTE/XWEBERERFRmtP0aDdTAkiYkiD2/vvvw+VyqXW9Tj/9dLVtwoQJuOWWW1R5eiFh7ZprrsGKFSvUNlm8eYcddug51quvvoq7774bra2t2GuvvXDDDTeoqowiHA6rtcVkH6PRiOOOOw5//OMfe8rhNzY2qrXJvvzySzUk8fe//z1+/vOfJ+Q9ISIiIiKi9JfQIEZERERERJSJ0rUaOBERERERUdJiECMiIiIiIoozBjEiIiIiIqI4YxBLEJmad8YZZ6gCIr09/vjjqhhJ78tf//rXnu1SUEQKiUhpfqn8WFlZucnj9957b+y888648sorVUEUis25kXXvLrzwQvXeHnDAAapSZ2+LFi3C8ccfr87NscceiwULFvTZLtU4DzroILX9/PPPR1NTU1xeTzqS93rjn5NoYR8hPxdS+EeWoTj88MMxe/bsPo/f0s8RxY7f71e/i2bOnKkq1Ep1XIqfDz74YJOflYsuukht4++s+AsEAup3j1RwjtXvK/7dH7pzc+ONN27y8/P0008P6mdEPktIxe7dd98du+66qyoaF4lE4v66Ulltba36fSXvn3yPSxE/+ZuSVj83UqyD4iscDuvXX3+9Pn78eP2VV17ps+2qq67Sr732Wr2urq7n0t7erratW7dOnzZtmv7II4/oy5Yt0y+++GL95z//uR6JRNT2d999V58xY4b+8ccf6/PmzdMPP/xw/brrrkvIa0zHc3Puuefqp512mr506VL9xRdf1HfccUf1PovOzk59r7320m+99Va9oqJCv+GGG/Q999xT3S9kv5122kl/7bXX9MWLF+u/+c1v9HPOOSchrzEdvPHGG/ovf/nLPj8nTU1Napv8PPziF7/Q//jHP6pzcf/99+tTp05VPz+D+Tmi2JKfJzkfCxYs0N9//31955131t95551ENytj/Otf/1K/u3r/rLS2tvJ3VgL4fD79/PPPV39fvv7665j8vuLf/aE7N+L000/XH3jggT4/P11dXYP6GZFztu++++rffvut/tVXX+mzZs3SH3744YS8vlQUiUT0E044QT/rrLPU9768jwcffLD6nZVOPzcMYnFWU1Ojflj3228/febMmZt82P/1r3+tP//88/0+9q677lKPjZJfBvKhJvpL46STTtLvvvvunu3yTSu/JKK/NGjbz82aNWvUL+jKysqe+6688kr9T3/6k/r6pZde0g844ICeH3K5ll8Y0WNceumlPfuK9evX6xMmTNDXrl0bx1eYPv72t7/pf/jDH/rd9uWXX6pfwNEPlEICdPRnY0s/RxQ7cg6mTJnS57299957+7z/NLTkg8qdd965yf38nRVfy5cv14888kj14bH3h/3t/X3Fv/tDd27E3nvvrX/++ef9Pm5LPyMSwnp/jnj99df1/ffff0hfSzqpqKhQ56O+vr7nvjfffFMF2nT6ueHQxDhbuHAhiouL8corr8Dtdm+yfeXKlRg5cmS/j503b54a3tN7UezJkydj7ty5aq20H3/8sc926a4NBoNqoWravnMj771sKysr67lvxowZ+OGHH3q2y+3o2nRyPX36dHVuott7nxs5VklJibqftp6sJ7i5nxNZY9DhcPTcJ+dmoHPR++eIYkt+94RCITX0o/e5kHPw/9u789goyjCO4w9QDk+QQzRoUA6lIh4oFI3x5KgiWAjxNgrxIooSlWo1DYkYQGtELgFDMEa5VVTgDyQeEYkogicqEaQkBVRqRCUGFF3ze5LZTLe7vXZ3tpTvJyl0992dmc677zvz7HvRRSe3ZYU6K1qffvqpFRQU2NKlSzNWX3Hdz27e7N+/37vG1fWeLFxG9L49e/ZYv379quTrrl277JdffsniX9N0dOrUyebPn28dO3asli9NqdzkRb7HI5zGFuknmcrKStu3b5+tWLHCSkpKrHXr1r74tMYr6SK5d+9eO/HEE6u8p0OHDvbTTz/ZH3/84f1mw+l5eXnWrl07T0d6eZPq3KuyDdJ79OhRLf2HH37w31Xxpso7NOzmUjfyw4YNsz///NMuvfRSKy4u9oXhayonUls6Mkfn+oQTTrBWrVrFn9NFVXWV6rr27dvn9PiaOvV62bFjh4+dmDdvnt+AFBYW+pgL6qxo3XzzzUmfT6e+4rqf3bzRdUb3XnPnzrUPP/zQz+vo0aNtxIgRtZYR5ZuE04OAQumJ70N1xx9/vI/hCuiar/F5GnPXlMoNgViGHThwIH5zniy6D0fvyVrDgg/LnDlz7LvvvvOBoi1atPABiRpIGL6hET3WAFPtN3icLB3p5U1N574u6do3eZOZvNLNuwbdqnVy8uTJXqlqAO+ECRO83KSbV8icVOdaON/Zt3v37ngePP/881ZRUeHXFJUv6qzGIZ36iut+dumeTIFYt27d7NZbb7WNGzdaaWmpf+E3aNCgGstIsryh7ktPWVmZTzD02muv+UQbTaXcEIhlmJpDNTtLMrNnz/bZdVLRrDAbNmzwb5BFs/NoBp7Fixd7IKYWssQPiR7rWwOlBY8T09Uki/TyJtW5b9OmTVrp5E3D8krlROe0ZcuW/tzUqVN91jcFb3perS31zQuVI2RWqnMtQX4ge7p06eIzwLVt29ZvKPPz8/1bZX1poesNdVbupVNfcd3PrqKiIrviiiu8pUR69epl5eXlfk+mQKymMhIOuhLzibxpWBD28ssv27Rp0+yMM85oUuWGQCzD1M9469atDX5/EIQFunfvHm8Z6Ny5s3dfDNNjXVxVUejDpcd6j2hshj6oau1BenmT6twH5zZVetD0Xdv7kV5eBZ95lRWd623bttU7L1SOkFk611r2QXWRun4EXUZ0sSTwjUZwExkuK+qWo7qHOiv30qmvuO5nl768SCw/ah3TF4G1lRGlBfVdMLY86K5I3tTPpEmTPPhVMDZkyJAmV26YrKMRWb58uX/I1K8/oO6JKviitRA2bdoUT1PTq5pp9Xzz5s2tT58+VdI1KFE3P/oWB+nRQE4Nsg33H9a51vOiPNDEHUHe6f/Nmzf780F6OG80iFc/QTrqTpWvJn8IrwmicqLPeteuXf2cauKVoPuB6NynyotwOUJm6aKnfAlPhKJzr7pKdRaya926df6lRnh9HJUV3YgEkw1RZ+VWOvUV1/3smj59uvdGCtNkDqnuycJlRIGAJu4Ip+t3Pcf4sLqbNWuWLVmyxJ577jkbOnRo0yw3kc/TiDhNYxqe2rSiosKn15wyZUqsvLw8tmrVqljfvn1jq1ev9nRNna6poLWmRbAugqZbDaYfDl6/du1aXxdh6NChvjYM0s8bGTNmjE+HqvVCtI6Y8iJYR0xrvQ0YMMDPt6bC1f9aoyeYWnXz5s2x3r17+/uC9Ua0tg8attab1hAL1nTTtLNaA2TixImefujQIX88fvx4LycqL5rmNlhfpLZyhMwqLS31ukhlRXWT6qg1a9bk+rCOCKqXNP22lnrYvn177IMPPvCpn1988UXqrBwKT5Gebn3FdT97eaPzedZZZ/naX1rCZuHChb5+qMpGXcqI8kzlTdvTj35fsGBBzv62w3H6+vz8/Ni0adOqrOOmn6ZUbgjEGtnNvm4qtYCd1jNQ+qJFi6qk60I6ePBgT9eNaOKaLvrQXXTRRb5QXUlJiS9SiMzkTWVlpVeyKtxaf0frWYSpMBcVFXn6qFGjYlu2bKmSru1pXRFVFlo4MliAGPWn9Vp0DrXeW//+/b0CPXjwYDxdX2TccsstftFUBbt+/fp6lSNkjtZlKS4u9s+9bkReeumlXB/SEUU3IVqUVudfgdbMmTPjNyPUWbmRuFZVuvUV1/3s5Y1u1HUDrzJSWFhY7UukmsqIgoXJkyf7daqgoCBWVlbGF371MG/ePM+PZD9Nqdw00z/Rt8MBAAAAwJGLTvoAAAAAEDECMQAAAACIGIEYAAAAAESMQAwAAAAAIkYgBgAAAAARIxADAAAAgIgRiAEAAABAxAjEAAAAACBieVHvEACAmlx55ZW2a9eu+OO8vDw79dRT7cYbb7Q77rgjY/u57bbbrH///jZu3Dh77LHH/LmpU6fW+J6///7b3nzzTbv++uurbSPTzjzzzJRp7777rp1yyikZ3ycAIDoEYgCARufxxx+3a665xn8/dOiQbdiwwZ544glr166dFRUVZXx/2nZdrF692ubOnRsPxGbOnGktW7a0bNH2zz///GrPt2/fPmv7BABEg0AMANDoHHfccdapU6f44xEjRtiqVavsnXfeyUogpv3VRSwWq/JYgWE2tW3btsp5AAA0HYwRAwAcFtRFMWh9UpfASZMm2VVXXWWXX3657d+/3/bs2WP33nuvnXvuud69cdasWfbvv//G37927VobMmSInXfeefbkk09WSVPXxKB7orz11ltWWFjo21KXyG+//dY++eQTKykp8W6T6jZYUVHhx6FWq8Abb7xhV199tZ1zzjk2cuRI27hxYzxNx7Rw4UJvTevTp49dd9119s0336R1TnQc06dPt4KCAv/btX8d73333WcXXHCBvf322/bff//Z/Pnz/VzpuHTMW7duTbkNAEA0CMQAAI3aP//84y1h69ev92AioKCjrKzMA65jjjnG7r//fuvQoYOtWLHCpkyZYitXrvRuhLJt2zYbP3683XTTTfb66697d8dNmzYl3d+6deu8q+Ltt9/ugczZZ59t99xzj3cRVJfJk046yT766CM7+eSTq7xPx6PgUK/VOLKLL77Y7r77bvv555/jr1HQpue0XbXCPfXUU2mfn/fff98WL15sjzzyiD/+/PPPrUePHrZs2TK75JJLbPbs2bZgwQI/dp2bLl262J133ml//fVXym0AALKProkAgEZn4sSJHtTIgQMHrE2bNh4YDR8+PP4atYT17dvXf//4449t9+7dtnz5cmvevLl169bNHn30UW/BUuuQgq8LL7wwPtlHaWmpBx/JLF261K699loP2qS4uNhb4n7//XcPnlq0aJG0u+Arr7zirU1B10kFNWoRe/XVV+3hhx+Od7EcOHCg/z569Gh78MEHazwPd911l+8vTC1dauEK3HDDDf73yldffWXNmjWzsWPH+jlTV0rt/6GHHooHsTqvgwYN8mBQrWeJ2wAARINADADQ6DzwwAM2ePBg/71169Ye+CQGJGrZCWzfvt327dvnQUpAXfIUxP3222+enp+fH09TYBV+HLZjx454gCKtWrXyoK422oeCvjB1g9TzgdNOOy3++7HHHuutfTVRi5m6R4YpwEp1HkStgsFrfv31Vz8v4W3ob1crX/i4ErcBAMg+AjEAQKOjYKJr1641vkYBWkBdDdWi88ILL1R7XTARR+JEG6lmO9RYtIYIH09A49AUENa2z1Q6d+5cr/OQ+DjZMSU7rlSvAwBkD2PEAACHvdNPP927JmpadwUu+tFkGjNmzPCuej179rSvv/46/noFId9//33Sbem94TQFLZpoQ2PKtK2ajuHLL7+s8pwe6/lcURDasWNH++KLL+LPqRVuy5YtOT0uAACBGACgCdCkFOpeN2HCBJ8R8LPPPvNxYEcddZR3adRMhZqhcM6cOfbjjz/a008/7YFbMhrnpfFTmthi586dPvGHWtN69+7t29NYsfLycm+FC9P4M43H0kQd6t747LPPekA3atSoBv9d2tfevXur/Rw8eLDO29BxKSB97733vDuizoveH6zTBgDIDbomAgAOewq2FGRpIgoFXUcffbRPPx+M7VIrl9IVVOl/TZhx2WWXJd1Wv379fLIQzTaooEfjqTT7osZdDRgwwLc1bNgwW7RoUZX3KbCprKz0oEfv0xg0zVbYvXv3Bv9d48aNS/r8M88849Pf18WYMWN8en8FYPpfsz9qYhEWhQaA3GoWS+w0DwAAAADIKromAgAAAEDECMQAAAAAIGIEYgAAAAAQMQIxAAAAAIgYgRgAAAAARIxADAAAAAAiRiAGAAAAABEjEAMAAACAiBGIAQAAAEDECMQAAAAAIGIEYgAAAABg0fofdOZH5lW1PBsAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Improvement Over Original Forecast:\n", + "- MSE: 2.68%\n", + "\n", + "- MAPE: 1.32%\n", + "\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the data\n", + "file_path = r\"C:\\Users\\waseem\\Downloads\\combined_data\\combined_data.csv\"#change path\n", + "df = pd.read_csv(file_path)\n", + "\n", + "# Convert to datetime\n", + "df[\"date_time_current\"] = pd.to_datetime(df[\"date_time_current\"])\n", + "df[\"date_time_future\"] = pd.to_datetime(df[\"date_time_future\"])\n", + "\n", + "# Sort by forecast time\n", + "df = df.sort_values(\"date_time_current\")\n", + "# filter PERIDID 24\n", + "df_periodid24 = df[df[\"period_id\"] == 24].copy()\n", + "# Sort data by time\n", + "df_periodid24 = df_periodid24.sort_values(\"date_time_current\")\n", + "\n", + "# Print basic dataset information\n", + "print(f\"Data timespan: {df_periodid24['date_time_current'].min()} to {df_periodid24['date_time_current'].max()}\")\n", + "print(f\"Total records: {len(df_periodid24)}\")\n", + "print(f\"Data frequency: {df_periodid24['date_time_current'].diff().value_counts().index[0]}\")\n", + "\n", + "# slicing the data\n", + "start_date = pd.to_datetime('2017-10-07 23:00:00')\n", + "end_date = df_periodid24['date_time_current'].max()\n", + "date_mask = (df_periodid24['date_time_current'] >= start_date) & (df['date_time_current'] <= end_date)\n", + "df_period24 = df.loc[date_mask].copy()\n", + "\n", + "# Create lagged features (12h, 24h, 168h)\n", + "df_period24[\"demand_lag12h\"] = df_period24[\"total_demand\"].shift(24) # 12-hour lag\n", + "df_period24[\"demand_lag24h\"] = df_period24[\"total_demand\"].shift(48) # Daily patterns\n", + "df_period24[\"demand_lag168h\"] = df_period24[\"total_demand\"].shift(372) # Weekly seasonality\n", + "df_period24[\"hour\"] = df_period24[\"date_time_current\"].dt.hour\n", + "df_period24[\"day_of_week\"] = df_period24[\"date_time_current\"].dt.dayofweek\n", + "df_period24[\"month\"] = df_period24[\"date_time_current\"].dt.month\n", + "# 24-hour rolling mean demand (excluding current row)\n", + "# For each hour, calculate rolling mean on properly lagged data\n", + "hour_groups = df_period24.groupby('hour')\n", + "for hour, hour_data in hour_groups:\n", + " # Sort by date\n", + " hour_data = hour_data.sort_values('date_time_current')\n", + "\n", + " # Calculate rolling mean using only data that would be available\n", + " # Shift by 1 since we want to use past values only\n", + " hour_data['demand_rolling_mean_24h'] = hour_data['total_demand'].shift(1).rolling(48).mean()\n", + "\n", + " # Update the main dataframe\n", + " df_period24.loc[hour_data.index, 'demand_rolling_mean_24h'] = hour_data['demand_rolling_mean_24h']\n", + "\n", + " # Rolling mean of forecast errors\n", + "# Calculate forecast error\n", + "df_period24[\"forecast_error\"] = df_period24[\"total_demand\"] - df_period24[\"forecast_demand\"]\n", + "\n", + "# Group by hour and calculate rolling mean of forecast errors properly\n", + "hour_groups = df_period24.groupby('hour')\n", + "for hour, hour_data in hour_groups:\n", + " # Sort by date\n", + " hour_data = hour_data.sort_values('date_time_current')\n", + "\n", + " # Calculate rolling mean of forecast errors\n", + " # Shift by 1 (to get past values for this hour)\n", + " hour_data['forecast_error_rolling_mean_24h'] = hour_data['forecast_error'].shift(1).rolling(48).mean()\n", + "\n", + " # Update the main dataframe\n", + " df_period24.loc[hour_data.index, 'forecast_error_rolling_mean_24h'] = hour_data['forecast_error_rolling_mean_24h']\n", + "display(df_period24)\n", + "\n", + "\n", + "\n", + "# Feature Selection\n", + "\n", + "# Define the feature list\n", + "features = [\n", + " \"forecast_demand\", # Original forecast to be corrected\n", + " \"temperature_current\", # Temperature at forecast time\n", + " \"demand_lag12h\", # 12-hour lagged demand\n", + " \"demand_lag24h\", # 24-hour lagged demand (daily seasonality)\n", + " \"demand_lag168h\", # Weekly seasonality (24*7=168 hours)\n", + " \"demand_rolling_mean_24h\", # 24-hour rolling average (recent trend)\n", + " \"hour\", # Hour of day (0-23)\n", + " \"day_of_week\", # Day of week\n", + " \"forecast_error_rolling_mean_24h\" # # 24-hour rolling average (recent trend)\n", + "]\n", + "\n", + "# Create cleaned dataset with these features\n", + "df_model = df_period24[features + [\"total_demand\"]+ ['date_time_current']].dropna() # target is total_demand\n", + "# Define target\n", + "target = 'total_demand'\n", + "\n", + "\n", + "# Check for any remaining NaN values\n", + "print(f\"NaN values in features: {df_model[features].isna().sum().sum()}\")\n", + "print(f\"NaN values in target: {df_model[target].isna().sum()}\")\n", + "print(df_model.index)\n", + "# ===== Split data temporally =====\n", + "# Use the last 30% for testing\n", + "X = df_model[features]\n", + "y = df_model[target]\n", + "test_size = 0.3\n", + "split_idx = int(len(df_model) * (1 - test_size))\n", + "\n", + "# Split into train/test with proper temporal order\n", + "\n", + "train_mask = df_model.index < split_idx\n", + "test_mask = df_model.index >= split_idx\n", + "\n", + "X_train = X.iloc[:split_idx]\n", + "y_train = y.iloc[:split_idx]\n", + "X_test = X.iloc[split_idx:]\n", + "y_test = y.iloc[split_idx:]\n", + "\n", + "\n", + "# Train Random Forest Model\n", + "\n", + "\n", + "\n", + "\n", + "model = RandomForestRegressor(\n", + " n_estimators=200,\n", + " max_depth=10,\n", + " min_samples_split=5,\n", + " min_samples_leaf=2,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Get model predictions on test set\n", + "y_pred_model = model.predict(X_test)\n", + "# Feature Importance Check\n", + "\n", + "\n", + "\n", + "\n", + "feature_importance = pd.Series(model.feature_importances_, index=features)\n", + "print(\"Feature Importances:\\n\", feature_importance.sort_values(ascending=False))\n", + "\n", + "\n", + "# Model Performance\n", + "#\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# Get original forecast (forecast_demand) from test set\n", + "y_pred_original = X_test[\"forecast_demand\"]\n", + "\n", + "# True values\n", + "y_true = y_test\n", + "\n", + "# Calculate metrics for MODEL\n", + "mse_model = mean_squared_error(y_test, y_pred_model)\n", + "mape_model = np.mean(np.abs((y_test - y_pred_model) / y_test)) * 100\n", + "\n", + "# Calculate metrics for ORIGINAL FORECAST\n", + "mse_original = mean_squared_error(y_test, y_pred_original)\n", + "mape_original = np.mean(np.abs((y_test - y_pred_original) / y_test)) * 100\n", + "\n", + "print(f\"\"\"\n", + "Model Performance:\n", + "- MSE: {mse_model:.2f}\n", + "- MAPE: {mape_model:.2f}%\n", + "\n", + "Original Forecast Performance:\n", + "- MSE: {mse_original:.2f}\n", + "- MAPE: {mape_original:.2f}%\n", + "\"\"\")\n", + "\n", + "#Plotting\n", + "\n", + "\n", + "# Plot a sample of 100 data points\n", + "sample = np.random.choice(len(y_test), 100, replace=False)\n", + "x_axis = range(len(sample))\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "sns.lineplot(x=x_axis, y=y_true.iloc[sample], label='Actual Demand', color='black')\n", + "sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], label='Original Forecast', linestyle='--')\n", + "sns.lineplot(x=x_axis, y=y_pred_model[sample], label='Model Predictions', linestyle='--')\n", + "plt.title(\"Model vs Original Forecast Performance\")\n", + "plt.ylabel(\"Demand\")\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.kdeplot(y_true - y_pred_original, label='Original Forecast Error', fill=True)\n", + "sns.kdeplot(y_true - y_pred_model, label='Model Error', fill=True)\n", + "plt.title(\"Error Distribution Comparison\")\n", + "plt.xlabel(\"Prediction Error\")\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "sns.scatterplot(x=y_true, y=y_pred_original, alpha=0.5)\n", + "plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--')\n", + "plt.title(\"Original Forecast vs Actual\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sns.scatterplot(x=y_true, y=y_pred_model, alpha=0.5)\n", + "plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--')\n", + "plt.title(\"Model Predictions vs Actual\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Performance Improvement\n", + "\n", + "\n", + "improvement_mse = (1 - mse_model/mse_original) * 100\n", + "\n", + "improvement_mape = (1 - mape_model/mape_original) * 100\n", + "\n", + "print(f\"\"\"\n", + "Improvement Over Original Forecast:\n", + "- MSE: {improvement_mse:.2f}%\n", + "\n", + "- MAPE: {improvement_mape:.2f}%\n", + "\"\"\")\n", + "\n", + "# Residuals\n", + "residuals_original = y_true - y_pred_original\n", + "residuals_model = y_true - y_pred_model\n", + "\n", + "plt.figure(figsize=(14, 4))\n", + "plt.subplot(1, 2, 1)\n", + "sns.histplot(residuals_original, kde=True)\n", + "plt.title(\"Original Forecast Residuals\")\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sns.histplot(residuals_model, kde=True)\n", + "plt.title(\"Model Residuals\")\n", + "plt.show()\n", + "\n", + "\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-04-20T22:10:18.473051800Z", + "start_time": "2025-04-20T22:09:39.147548700Z" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/RFMF1.ipynb b/src/RFMF1.ipynb new file mode 100644 index 000000000..748a6c524 --- /dev/null +++ b/src/RFMF1.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Features: Only 24 Hours lag and forecast" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-04-21T05:00:50.618685400Z", + "start_time": "2025-04-21T05:00:27.984533300Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data timespan: 2017-10-07 23:00:00 to 2021-03-18 00:00:00\n", + "Total records: 30143\n", + "Data frequency: 0 days 01:00:00\n", + "\n", + "NaN counts in key columns:\n", + "\n", + "Available columns:\n", + "['id', 'period_id', 'forecast_demand', 'date_time_current', 'date_time_future', 'date_time_current_rounded', 'total_demand', 'temperature_current', 'forecast_interval', 'Temperature', 'Humidity', 'Wind_speed', 'Rain', 'forecast_error', 'forecast_error_lag24h']\n", + "\n", + "NaN counts in features:\n", + "forecast_demand: 0 NaNs\n", + "forecast_error_lag24h: 24 NaNs\n", + "\n", + "Shape before dropping missing values: (30143, 4)\n", + "Shape after dropping NaN values: (30119, 4)\n", + "\n", + "Training set: 21083 records (70%)\n", + "Test set: 9036 records (30%)\n", + "Training date period: 2017-10-08 11:00:00 to 2020-03-05 22:00:00\n", + "Test date period: 2020-03-05 23:00:00 to 2021-03-17 12:00:00\n", + "\n", + "Model Performance:\n", + "- MSE: 51971.086\n", + "- MAPE: 2.111%\n", + "\n", + "Original Forecast Performance:\n", + "- MSE: 55152.352\n", + "- MAPE: 2.179%\n", + "\n", + "Improvement Over Original Forecast:\n", + "- MSE: 5.768%\n", + "- MAPE: 3.105%\n", + "\n", + "Feature Importance:\n", + " Feature Importance\n", + "0 forecast_demand 0.99128\n", + "1 forecast_error_lag24h 0.00872\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the data\n", + "file_path = r\"C:\\Users\\waseem\\Downloads\\combined_data_new.csv\" # change path\n", + "df = pd.read_csv(file_path)\n", + "\n", + "\n", + "# Convert to datetime\n", + "df[\"date_time_current_rounded\"] = pd.to_datetime(df[\"date_time_current_rounded\"])\n", + "df[\"date_time_future\"] = pd.to_datetime(df[\"date_time_future\"])\n", + "\n", + "# Filter for PERIODID 24 and sort\n", + "df_sliced = df[df[\"period_id\"] == 24].copy()\n", + "df_sliced = df_sliced.sort_values(\"date_time_future\")\n", + "df_sliced = df_sliced.dropna()\n", + "\n", + "\n", + "# Basic dataset info\n", + "print(f\"Data timespan: {df_sliced['date_time_future'].min()} to {df_sliced['date_time_future'].max()}\")\n", + "print(f\"Total records: {len(df_sliced)}\")\n", + "print(f\"Data frequency: {df_sliced['date_time_future'].diff().value_counts().index[0]}\")\n", + "\n", + "\n", + "# Get forecast error\n", + "df_sliced['forecast_error'] = df_sliced['total_demand'] - df_sliced['forecast_demand']\n", + "df_sliced['forecast_error_lag24h'] = df_sliced.sort_values('date_time_current_rounded')['forecast_error'].shift(24)\n", + "\n", + "\n", + "\n", + "# Check for NaN counts in key columns\n", + "print(\"\\nNaN counts in key columns:\")\n", + "for col in ['demand_lag_24h', 'demand_lag_48h', 'demand_lag_7d', 'forecast_error_lag12h']:\n", + " if col in df_sliced.columns:\n", + " print(f\"{col}: {df_sliced[col].isna().sum()} NaNs\")\n", + "\n", + "# Define features that would be available at prediction time (12 hours ahead)\n", + "features = [\n", + " # Basic time features\n", + "\n", + "\n", + " # Original forecast\n", + " 'forecast_demand',\n", + " 'forecast_error_lag24h'\n", + "\n", + "\n", + "]\n", + "\n", + "\n", + "# Define target\n", + "target = 'total_demand'\n", + "\n", + "# Print the columns in the dataframe\n", + "print(\"\\nAvailable columns:\")\n", + "print(df_sliced.columns.tolist())\n", + "\n", + "# Print the number of NaN values for each feature\n", + "print(\"\\nNaN counts in features:\")\n", + "for feature in features:\n", + " print(f\"{feature}: {df_sliced[feature].isna().sum()} NaNs\")\n", + "\n", + "# Create the modeling dataframe\n", + "model_df = df_sliced[features + [target] + ['date_time_current_rounded']].copy()\n", + "\n", + "# Print the shape before dropping missing values\n", + "print(f\"\\nShape before dropping missing values: {model_df.shape}\")\n", + "\n", + "# Drop rows with NaN values\n", + "model_df = model_df.dropna()\n", + "print(f\"Shape after dropping NaN values: {model_df.shape}\")\n", + "\n", + "# If we still have no data, show a clear error and exit\n", + "if len(model_df) == 0:\n", + " print(\"ERROR: No data left after dropping NaN values!\")\n", + " import sys\n", + " sys.exit(1)\n", + "\n", + "# Sort data to ensure temporal order\n", + "model_df = model_df.sort_values('date_time_current_rounded').reset_index(drop=True)\n", + "\n", + "# Split data temporally - use 70-30 split\n", + "X = model_df[features]\n", + "y = model_df[target]\n", + "train_size = 0.7\n", + "split_idx = int(len(model_df) * train_size)\n", + "\n", + "# Split into train/test\n", + "X_train = X.iloc[:split_idx]\n", + "y_train = y.iloc[:split_idx]\n", + "X_test = X.iloc[split_idx:]\n", + "y_test = y.iloc[split_idx:]\n", + "\n", + "# Print split info with actual dates\n", + "print(f\"\\nTraining set: {len(X_train)} records ({train_size*100:.0f}%)\")\n", + "print(f\"Test set: {len(X_test)} records ({(1-train_size)*100:.0f}%)\")\n", + "print(f\"Training date period: {model_df['date_time_current_rounded'].iloc[0]} to {model_df['date_time_current_rounded'].iloc[split_idx-1]}\")\n", + "print(f\"Test date period: {model_df['date_time_current_rounded'].iloc[split_idx]} to {model_df['date_time_current_rounded'].iloc[-1]}\")\n", + "\n", + "# Train model\n", + "model = RandomForestRegressor(\n", + " n_estimators=200,\n", + " max_depth=10,\n", + " min_samples_split=5,\n", + " min_samples_leaf=2,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "y_pred_original = X_test[\"forecast_demand\"]\n", + "\n", + "# Evaluate performance\n", + "def calculate_metrics(y_true, y_pred):\n", + " mse = mean_squared_error(y_true, y_pred)\n", + " mape = np.mean(np.abs((y_true - y_pred) / np.maximum(0.001, y_true))) * 100\n", + " return mse, mape\n", + "\n", + "# Model metrics\n", + "model_mse, model_mape = calculate_metrics(y_test, y_pred)\n", + "\n", + "# Original forecast metrics\n", + "original_mse, original_mape = calculate_metrics(y_test, y_pred_original)\n", + "\n", + "# Print formatted results\n", + "print(\"\\nModel Performance:\")\n", + "print(f\"- MSE: {model_mse:.3f}\")\n", + "print(f\"- MAPE: {model_mape:.3f}%\")\n", + "\n", + "print(\"\\nOriginal Forecast Performance:\")\n", + "print(f\"- MSE: {original_mse:.3f}\")\n", + "print(f\"- MAPE: {original_mape:.3f}%\")\n", + "\n", + "# Calculate improvement percentages\n", + "improvement_mse = (1 - model_mse/original_mse) * 100\n", + "improvement_mape = (1 - model_mape/original_mape) * 100\n", + "\n", + "print(\"\\nImprovement Over Original Forecast:\")\n", + "print(f\"- MSE: {improvement_mse:.3f}%\")\n", + "print(f\"- MAPE: {improvement_mape:.3f}%\")\n", + "\n", + "# Feature importance\n", + "feature_importance = pd.DataFrame(\n", + " {'Feature': features,\n", + " 'Importance': model.feature_importances_}\n", + ").sort_values('Importance', ascending=False)\n", + "\n", + "print(\"\\nFeature Importance:\")\n", + "print(feature_importance)\n", + "\n", + "# Plot actual vs. predicted\n", + "# Plot a sample of 100 data points\n", + "sample = np.random.choice(len(y_test), 100, replace=False)\n", + "x_axis = range(len(sample))\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "sns.lineplot(x=x_axis, y=y_test.iloc[sample], label='Actual Demand', color='black')\n", + "sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], label='Original Forecast', linestyle='--')\n", + "sns.lineplot(x=x_axis, y=y_pred[sample], label='Model Predictions', linestyle='--')\n", + "plt.title(\"Model vs Original Forecast Performance\")\n", + "plt.ylabel(\"Demand\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/RFMF2.ipynb b/src/RFMF2.ipynb new file mode 100644 index 000000000..9e2a5bd31 --- /dev/null +++ b/src/RFMF2.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-04-21T11:09:36.592832400Z", + "start_time": "2025-04-21T11:09:08.672839300Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data timespan: 2017-10-07 23:00:00 to 2021-03-18 00:00:00\n", + "Total records: 30143\n", + "Data frequency: 0 days 01:00:00\n", + "\n", + "NaN counts in features:\n", + "forecast_demand: 0 NaNs\n", + "Temperature: 0 NaNs\n", + "Humidity: 0 NaNs\n", + "Wind_speed: 0 NaNs\n", + "Rain: 0 NaNs\n", + "forecast_error_lag24h: 24 NaNs\n", + "Shape after dropping NaN values: (30119, 8)\n", + "\n", + "Model Performance:\n", + "- MSE: 51395.334\n", + "- MAPE: 2.095%\n", + "\n", + "Original Forecast Performance:\n", + "- MSE: 55152.352\n", + "- MAPE: 2.179%\n", + "\n", + "Improvement Over Original Forecast:\n", + "- MSE: 6.812%\n", + "- MAPE: 3.858%\n", + "\n", + "Feature Importance:\n", + " Feature Importance\n", + "0 forecast_demand 0.986969\n", + "5 forecast_error_lag24h 0.006443\n", + "1 Temperature 0.001943\n", + "2 Humidity 0.001863\n", + "3 Wind_speed 0.001812\n", + "4 Rain 0.000969\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results saved to demand_forecast_results.csv\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Load the data\n", + "file_path = r\"C:\\Users\\waseem\\Downloads\\combined_data_new.csv\" # change path\n", + "df = pd.read_csv(file_path)\n", + "\n", + "\n", + "# Convert to datetime\n", + "df[\"date_time_current_rounded\"] = pd.to_datetime(df[\"date_time_current_rounded\"])\n", + "df[\"date_time_future\"] = pd.to_datetime(df[\"date_time_future\"])\n", + "\n", + "# Filter for PERIODID 24 and sort\n", + "df_sliced = df[df[\"period_id\"] == 24].copy()\n", + "df_sliced = df_sliced.sort_values(\"date_time_future\")\n", + "df_sliced = df_sliced.dropna()\n", + "\n", + "# Basic dataset info\n", + "print(f\"Data timespan: {df_sliced['date_time_future'].min()} to {df_sliced['date_time_future'].max()}\")\n", + "print(f\"Total records: {len(df_sliced)}\")\n", + "print(f\"Data frequency: {df_sliced['date_time_future'].diff().value_counts().index[0]}\")\n", + "\n", + "\n", + "\n", + "# Get forecast error\n", + "df_sliced['forecast_error'] = df_sliced['total_demand'] - df_sliced['forecast_demand']\n", + "df_sliced['forecast_error_lag24h'] = df_sliced.sort_values('date_time_current_rounded')['forecast_error'].shift(24)\n", + "\n", + "\n", + "\n", + "# Define features that would be available at prediction time (12 hours ahead)\n", + "features = [\n", + " 'forecast_demand',\n", + " 'Temperature',\n", + " 'Humidity',\n", + " 'Wind_speed',\n", + " 'Rain',\n", + " 'forecast_error_lag24h'\n", + "]\n", + "\n", + "\n", + "# Define target\n", + "target = 'total_demand'\n", + "\n", + "\n", + "# Print the number of NaN values for each feature\n", + "print(\"\\nNaN counts in features:\")\n", + "for feature in features:\n", + " print(f\"{feature}: {df_sliced[feature].isna().sum()} NaNs\")\n", + "\n", + "# Create the modeling dataframe\n", + "model_df = df_sliced[features + [target] + ['date_time_future'] ].copy()\n", + "# Drop rows with NaN values\n", + "model_df = model_df.dropna()\n", + "print(f\"Shape after dropping NaN values: {model_df.shape}\")\n", + "\n", + "\n", + "# Split data temporally - using 70-30 split\n", + "X = model_df[features]\n", + "y = model_df[target]\n", + "train_size = 0.7\n", + "split_idx = int(len(model_df) * train_size)\n", + "\n", + "# Split into train/test\n", + "X_train = X.iloc[:split_idx]\n", + "y_train = y.iloc[:split_idx]\n", + "X_test = X.iloc[split_idx:]\n", + "y_test = y.iloc[split_idx:]\n", + "\n", + "\n", + "# Train model\n", + "model = RandomForestRegressor(\n", + " n_estimators=200,\n", + " max_depth=10,\n", + " min_samples_split=5,\n", + " min_samples_leaf=2,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "y_pred_original = X_test[\"forecast_demand\"]\n", + "\n", + "# Evaluate performance\n", + "def calculate_metrics(y_true, y_pred):\n", + " mse = mean_squared_error(y_true, y_pred)\n", + " mape = np.mean(np.abs((y_true - y_pred) / np.maximum(0.001, y_true))) * 100\n", + " return mse, mape\n", + "\n", + "# Model metrics\n", + "model_mse, model_mape = calculate_metrics(y_test, y_pred)\n", + "\n", + "# Original forecast metrics\n", + "original_mse, original_mape = calculate_metrics(y_test, y_pred_original)\n", + "\n", + "# Print formatted results\n", + "print(\"\\nModel Performance:\")\n", + "print(f\"- MSE: {model_mse:.3f}\")\n", + "print(f\"- MAPE: {model_mape:.3f}%\")\n", + "\n", + "print(\"\\nOriginal Forecast Performance:\")\n", + "print(f\"- MSE: {original_mse:.3f}\")\n", + "print(f\"- MAPE: {original_mape:.3f}%\")\n", + "\n", + "# Calculate improvement percentages\n", + "improvement_mse = (1 - model_mse/original_mse) * 100\n", + "improvement_mape = (1 - model_mape/original_mape) * 100\n", + "\n", + "print(\"\\nImprovement Over Original Forecast:\")\n", + "print(f\"- MSE: {improvement_mse:.3f}%\")\n", + "print(f\"- MAPE: {improvement_mape:.3f}%\")\n", + "\n", + "# Feature importance\n", + "feature_importance = pd.DataFrame(\n", + " {'Feature': features,\n", + " 'Importance': model.feature_importances_}\n", + ").sort_values('Importance', ascending=False)\n", + "\n", + "print(\"\\nFeature Importance:\")\n", + "print(feature_importance)\n", + "\n", + "# Plot a sample of 100 data points\n", + "sample = np.random.choice(len(y_test), 100, replace=False)\n", + "x_axis = range(len(sample))\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "sns.lineplot(x=x_axis, y=y_test.iloc[sample], label='Actual Demand', color='black')\n", + "sns.lineplot(x=x_axis, y=y_pred_original.iloc[sample], label='Original Forecast', linestyle='--')\n", + "sns.lineplot(x=x_axis, y=y_pred[sample], label='Model Predictions', linestyle='--')\n", + "plt.title(\"Model vs Original Forecast Performance\")\n", + "plt.ylabel(\"Demand\")\n", + "plt.show()\n", + "\n", + "# Create a DataFrame with the prediction results\n", + "results_df = pd.DataFrame({\n", + " 'date_time_future': model_df['date_time_future'].iloc[split_idx:].values, # Using test set dates\n", + " 'total_demand': y_test.values,\n", + " 'forecast_demand': y_pred_original,\n", + " 'model_prediction': y_pred\n", + "})\n", + "\n", + "# Save to CSV\n", + "output_path = \"demand_forecast_results.csv\"\n", + "results_df.to_csv(output_path, index=False)\n", + "print(f\"Results saved to {output_path}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/Temprature and demand relationship.ipynb b/src/Temprature and demand relationship.ipynb new file mode 100644 index 000000000..6ac36e7b9 --- /dev/null +++ b/src/Temprature and demand relationship.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-03-21T18:24:38.362638200Z", + "start_time": "2025-03-21T18:24:38.346536600Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "Demand data: 196513 rows\n", + "Temperature data: 220326 rows\n", + "\n", + "Sample demand data:\n", + " DATETIME TOTALDEMAND REGIONID\n", + "0 1/1/2010 0:00 8038.00 NSW1\n", + "1 1/1/2010 0:30 7809.31 NSW1\n", + "2 1/1/2010 1:00 7483.69 NSW1\n", + "3 1/1/2010 1:30 7117.23 NSW1\n", + "4 1/1/2010 2:00 6812.03 NSW1\n", + "\n", + "Sample temperature data:\n", + " LOCATION DATETIME TEMPERATURE\n", + "0 Bankstown 1/1/2010 0:00 23.1\n", + "1 Bankstown 1/1/2010 0:01 23.1\n", + "2 Bankstown 1/1/2010 0:30 22.9\n", + "3 Bankstown 1/1/2010 0:50 22.7\n", + "4 Bankstown 1/1/2010 1:00 22.6\n", + "\n", + "Preparing datetime formats for matching...\n", + "\n", + "Aggregating temperature data to match demand timestamps...\n", + "Aggregated temperature data: 98075 rows\n", + "\n", + "Matching demand with temperature data...\n", + "Matched data: 196149 rows\n", + "\n", + "Basic statistics:\n", + "Correlation between Temperature and Demand: 0.1490\n", + "\n", + "Demand by Temperature Range:\n", + " TEMP_RANGE mean std count\n", + "0 < 0°C 8094.539375 919.398042 16\n", + "1 0-5°C 7879.990402 1106.816901 2710\n", + "2 5-10°C 8131.803923 1429.521152 19550\n", + "3 10-15°C 8247.963290 1485.797315 42350\n", + "4 15-20°C 7722.975699 1064.080168 62603\n", + "5 20-25°C 8071.439614 1006.542829 51528\n", + "6 25-30°C 9118.949774 1302.724111 14270\n", + "7 30-35°C 9970.298139 1522.167279 2568\n", + "8 35-40°C 11415.496219 1232.875639 484\n", + "9 > 40°C 12368.046857 1293.869756 70\n", + "\n", + "Creating visualizations...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_22160\\3369540576.py:117: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " demand_by_temp = merged_df.groupby('TEMP_RANGE')['TOTALDEMAND'].agg(['mean', 'std', 'count']).reset_index()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analysis complete. Visualizations saved.\n", + "\n", + "Key Findings:\n", + "1. Overall correlation between temperature and demand: 0.1490\n", + "2. Temperature with lowest average demand: 19.0°C\n", + "3. Correlation in cold temperatures (below 19.0°C): -0.1217\n", + "4. Correlation in hot temperatures (above 19.0°C): 0.5502\n", + "5. Correlation in extreme temperatures:\n", + " - Cold (<5°C): 0.0117\n", + " - Hot (>35°C): 0.3878\n", + "\n", + "6. Demand distribution by temperature range:\n", + " TEMP_RANGE 10th Percentile Median (50th) 90th Percentile Count\n", + "0 < 0°C 7293.860 7826.060 9461.995 16\n", + "1 0-5°C 6739.225 7580.005 9536.670 2710\n", + "2 5-10°C 6444.164 7899.400 10234.773 19550\n", + "3 10-15°C 6255.896 8232.980 10245.993 42350\n", + "4 15-20°C 6237.962 7744.020 9118.938 62603\n", + "5 20-25°C 6695.152 8086.395 9366.142 51528\n", + "6 25-30°C 7423.539 9150.565 10741.518 14270\n", + "7 30-35°C 8112.655 9861.885 12051.431 2568\n", + "8 35-40°C 9648.401 11485.600 12898.473 484\n", + "9 > 40°C 10593.807 12648.770 13848.516 70\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_22160\\3369540576.py:215: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " demand_distribution = merged_df.groupby('TEMP_RANGE')['TOTALDEMAND'].agg([\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "This analysis suggests a likely U-shaped relationship between temperature and demand,\n", + "where both very cold and very hot temperatures result in higher electricity demand,\n", + "likely due to heating and cooling requirements respectively.\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import datetime\n", + "\n", + "# File paths\n", + "demand_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\totaldemand_nsw.csv\" # Change Path\n", + "temperature_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\temperature_nsw.csv\" # Chnage Path\n", + "\n", + "# Load data\n", + "\n", + "demand_df = pd.read_csv(demand_path)\n", + "temperature_df = pd.read_csv(temperature_path)\n", + "\n", + "print(f\"Demand data: {len(demand_df)} rows\")\n", + "print(f\"Temperature data: {len(temperature_df)} rows\")\n", + "\n", + "# Display sample data\n", + "print(\"\\nSample demand data:\")\n", + "print(demand_df.head())\n", + "print(\"\\nSample temperature data:\")\n", + "print(temperature_df.head())\n", + "\n", + "# Convert date formats for matching\n", + "print(\"\\nPreparing datetime formats for matching...\")\n", + "\n", + "def convert_demand_datetime(demand_datetime):\n", + " \"\"\"Convert demand datetime to a standard format\"\"\"\n", + " try:\n", + " dt = datetime.strptime(demand_datetime, '%d/%m/%Y %H:%M')\n", + " return dt.strftime('%Y-%m-%d %H:%M')\n", + " except:\n", + " return demand_datetime\n", + "\n", + "def convert_temp_datetime(temp_datetime):\n", + " \"\"\"Convert temperature datetime to a standard format\"\"\"\n", + " try:\n", + " dt = datetime.strptime(temp_datetime, '%d/%m/%Y %H:%M')\n", + " return dt.strftime('%Y-%m-%d %H:%M')\n", + " except:\n", + " try:\n", + " # Try alternative format if the first one fails\n", + " dt = datetime.strptime(temp_datetime, '%d/%m/%Y %H:%M:%S')\n", + " return dt.strftime('%Y-%m-%d %H:%M')\n", + " except:\n", + " return temp_datetime\n", + "\n", + "# Add standardized datetime columns\n", + "demand_df['DATETIME_STD'] = demand_df['DATETIME'].apply(convert_demand_datetime)\n", + "temperature_df['DATETIME_STD'] = temperature_df['DATETIME'].apply(convert_temp_datetime)\n", + "\n", + "\n", + "# We'll create an hourly aggregation to match with demand data\n", + "print(\"\\nAggregating temperature data to match demand timestamps...\")\n", + "\n", + "# Extract hour component for grouping\n", + "temperature_df['HOUR'] = temperature_df['DATETIME_STD'].apply(\n", + " lambda x: x[:13] if len(x) >= 13 else x # in order to get YYYY-MM-DD HH part\n", + ")\n", + "\n", + "# Aggregate temperature by hour (average if multiple readings per hour)\n", + "hourly_temp = temperature_df.groupby(['HOUR', 'LOCATION'])['TEMPERATURE'].mean().reset_index()\n", + "print(f\"Aggregated temperature data: {len(hourly_temp)} rows\")\n", + "\n", + "# Matching demand with temperature based on datetime\n", + "print(\"\\nMatching demand with temperature data...\")\n", + "\n", + "# Extract hour component from demand timestamps too\n", + "demand_df['HOUR'] = demand_df['DATETIME_STD'].apply(\n", + " lambda x: x[:13] if len(x) >= 13 else x # Get YYYY-MM-DD HH part\n", + ")\n", + "\n", + "# Merge demand with temperature\n", + "merged_df = pd.merge(\n", + " demand_df,\n", + " hourly_temp,\n", + " left_on='HOUR',\n", + " right_on='HOUR',\n", + " how='inner'\n", + ")\n", + "\n", + "print(f\"Matched data: {len(merged_df)} rows\")\n", + "\n", + "# If the merge didn't work well, try a different approach\n", + "if len(merged_df) < len(demand_df) * 0.5: # Less than 50% matched\n", + " print(\"Low match rate. Trying alternative approach...\")\n", + "\n", + " # Create a date-only field for matching by day\n", + " demand_df['DATE'] = demand_df['DATETIME_STD'].apply(lambda x: x[:10] if len(x) >= 10 else None)\n", + " temperature_df['DATE'] = temperature_df['DATETIME_STD'].apply(lambda x: x[:10] if len(x) >= 10 else None)\n", + "\n", + " # Calculate daily average temperature\n", + " daily_temp = temperature_df.groupby(['DATE', 'LOCATION'])['TEMPERATURE'].mean().reset_index()\n", + "\n", + " # Merge by date\n", + " merged_df = pd.merge(\n", + " demand_df,\n", + " daily_temp,\n", + " on='DATE',\n", + " how='inner'\n", + " )\n", + "\n", + " print(f\"Date-based match: {len(merged_df)} rows\")\n", + "\n", + "# Basic statistics\n", + "print(\"\\nBasic statistics:\")\n", + "print(f\"Correlation between Temperature and Demand: {merged_df['TEMPERATURE'].corr(merged_df['TOTALDEMAND']):.4f}\")\n", + "\n", + "# Calculate statistics by temperature ranges\n", + "print(\"\\nDemand by Temperature Range:\")\n", + "temp_bins = [-10, 0, 5, 10, 15, 20, 25, 30, 35, 40, 50]\n", + "temp_labels = ['< 0°C', '0-5°C', '5-10°C', '10-15°C', '15-20°C', '20-25°C', '25-30°C', '30-35°C', '35-40°C', '> 40°C']\n", + "merged_df['TEMP_RANGE'] = pd.cut(merged_df['TEMPERATURE'], bins=temp_bins, labels=temp_labels)\n", + "\n", + "# Group by temperature range\n", + "demand_by_temp = merged_df.groupby('TEMP_RANGE')['TOTALDEMAND'].agg(['mean', 'std', 'count']).reset_index()\n", + "print(demand_by_temp)\n", + "\n", + "# Visualizations\n", + "print(\"\\nCreating visualizations...\")\n", + "\n", + "# 1. Scatter plot of Temperature vs Demand\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], alpha=0.5)\n", + "plt.title('Relationship between Temperature and Electricity Demand')\n", + "plt.xlabel('Temperature (°C)')\n", + "plt.ylabel('Total Demand (MW)')\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Add trend line\n", + "z = np.polyfit(merged_df['TEMPERATURE'], merged_df['TOTALDEMAND'], 2) # Quadratic fit\n", + "p = np.poly1d(z)\n", + "temp_range = np.linspace(merged_df['TEMPERATURE'].min(), merged_df['TEMPERATURE'].max(), 100)\n", + "plt.plot(temp_range, p(temp_range), \"r--\", linewidth=2)\n", + "\n", + "plt.savefig('temperature_vs_demand_scatter.png')\n", + "\n", + "# 2. Average Demand by Temperature Range\n", + "plt.figure(figsize=(12, 6))\n", + "sns.barplot(x='TEMP_RANGE', y='mean', data=demand_by_temp)\n", + "plt.title('Average Electricity Demand by Temperature Range')\n", + "plt.xlabel('Temperature Range')\n", + "plt.ylabel('Average Demand (MW)')\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.savefig('avg_demand_by_temp_range.png')\n", + "\n", + "# 3. Time series plot of temperature and demand (sample period)\n", + "if len(merged_df) > 0:\n", + " # Sort by datetime\n", + " if 'DATETIME_STD' in merged_df.columns:\n", + " merged_df['DATETIME_OBJ'] = pd.to_datetime(merged_df['DATETIME_STD'])\n", + " merged_df = merged_df.sort_values('DATETIME_OBJ')\n", + "\n", + " # Get a sample period (first 7 days or less)\n", + " sample_period = merged_df.iloc[:min(24*7, len(merged_df))]\n", + "\n", + " # Create the time series plot\n", + " fig, ax1 = plt.subplots(figsize=(14, 7))\n", + "\n", + " # Plot demand\n", + " color = 'tab:blue'\n", + " ax1.set_xlabel('Date')\n", + " ax1.set_ylabel('Demand (MW)', color=color)\n", + " ax1.plot(sample_period['DATETIME_OBJ'], sample_period['TOTALDEMAND'], color=color)\n", + " ax1.tick_params(axis='y', labelcolor=color)\n", + "\n", + " # Create second y-axis for temperature\n", + " ax2 = ax1.twinx()\n", + " color = 'tab:red'\n", + " ax2.set_ylabel('Temperature (°C)', color=color)\n", + " ax2.plot(sample_period['DATETIME_OBJ'], sample_period['TEMPERATURE'], color=color)\n", + " ax2.tick_params(axis='y', labelcolor=color)\n", + "\n", + " plt.title('Temperature and Demand Over Time')\n", + " fig.tight_layout()\n", + " plt.savefig('temperature_demand_time_series.png')\n", + "\n", + "print(\"\\nAnalysis complete. Visualizations saved.\")\n", + "\n", + "# Additional insights\n", + "print(\"\\nKey Findings:\")\n", + "print(f\"1. Overall correlation between temperature and demand: {merged_df['TEMPERATURE'].corr(merged_df['TOTALDEMAND']):.4f}\")\n", + "\n", + "# Find temperature with minimum demand (U-shaped relationship)\n", + "grouped = merged_df.groupby(merged_df['TEMPERATURE'].round())['TOTALDEMAND'].mean()\n", + "min_demand_temp = grouped.idxmin()\n", + "print(f\"2. Temperature with lowest average demand: {min_demand_temp}°C\")\n", + "\n", + "# Calculate separate correlations for hot and cold temperatures\n", + "if min_demand_temp is not None:\n", + " cold_df = merged_df[merged_df['TEMPERATURE'] < min_demand_temp]\n", + " hot_df = merged_df[merged_df['TEMPERATURE'] > min_demand_temp]\n", + "\n", + " cold_corr = cold_df['TEMPERATURE'].corr(cold_df['TOTALDEMAND'])\n", + " hot_corr = hot_df['TEMPERATURE'].corr(hot_df['TOTALDEMAND'])\n", + "\n", + " print(f\"3. Correlation in cold temperatures (below {min_demand_temp}°C): {cold_corr:.4f}\")\n", + " print(f\"4. Correlation in hot temperatures (above {min_demand_temp}°C): {hot_corr:.4f}\")\n", + "\n", + "# Calculate correlation for extreme temperatures (<5°C and >30°C)\n", + "extreme_cold_df = merged_df[merged_df['TEMPERATURE'] < 5]\n", + "extreme_hot_df = merged_df[merged_df['TEMPERATURE'] > 30]\n", + "\n", + "extreme_cold_corr = extreme_cold_df['TEMPERATURE'].corr(extreme_cold_df['TOTALDEMAND']) if len(extreme_cold_df) > 2 else float('nan')\n", + "extreme_hot_corr = extreme_hot_df['TEMPERATURE'].corr(extreme_hot_df['TOTALDEMAND']) if len(extreme_hot_df) > 2 else float('nan')\n", + "\n", + "print(f\"5. Correlation in extreme temperatures:\")\n", + "print(f\" - Cold (<5°C): {extreme_cold_corr:.4f}\" if not np.isnan(extreme_cold_corr) else \" - Cold (<5°C): Not enough data\")\n", + "print(f\" - Hot (>35°C): {extreme_hot_corr:.4f}\" if not np.isnan(extreme_hot_corr) else \" - Hot (>35°C): Not enough data\")\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2025-03-21T18:24:46.877300600Z", + "start_time": "2025-03-21T18:24:38.378346300Z" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/W1 Initial Exploration.ipynb b/src/W1 Initial Exploration.ipynb new file mode 100644 index 000000000..1820eec56 --- /dev/null +++ b/src/W1 Initial Exploration.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notes\n", + "Data Exploration:\n", + "- Electricity demand is for NSW\n", + "- Temperature is for just Bankstown\n", + "- Different time spacings between electricity demand and temperature\n", + "\n", + "Forecasting analysis:\n", + "- Clear trend between temperature and forecasting inaccuracy\n", + " - Observed in both actual error and relative error\n", + " - Inaccuracy is greater at extremely hot temperatures" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df_temperature = pd.read_csv('data/temperature_nsw.csv', names = ['location', 'date_time', 'temperature'], skiprows = 1)\n", + "df_temperature.date_time = pd.to_datetime(df_temperature.date_time, format = \"%d/%m/%Y %H:%M\")\n", + "\n", + "df_demand = pd.read_csv('data/totaldemand_nsw.csv', names = ['date_time', 'total_demand', 'region_id'], skiprows = 1)\n", + "df_demand.date_time = pd.to_datetime(df_demand.date_time, format = \"%d/%m/%Y %H:%M\")\n", + "\n", + "df_forecast = pd.read_csv('data/forecastdemand_nsw.csv', names = ['id', 'region_id', 'period_id', 'forecast_demand', 'date_time_forecast', 'date_time_prediction'], skiprows = 1)\n", + "df_forecast.date_time_forecast = pd.to_datetime(df_forecast.date_time_forecast, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_forecast.date_time_prediction = pd.to_datetime(df_forecast.date_time_prediction, format = \"%Y-%m-%d %H:%M:%S\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Locations = {'Bankstown'}\n", + "Date Min = 2010-01-01 00:00:00 | Date Max = 2021-03-18 00:00:00\n", + "Temp Min = -1.3 | Temp Max = 44.7\n", + "\n", + " location date_time temperature\n", + "0 Bankstown 2010-01-01 00:00:00 23.1\n", + "1 Bankstown 2010-01-01 00:01:00 23.1\n", + "2 Bankstown 2010-01-01 00:30:00 22.9\n", + "3 Bankstown 2010-01-01 00:50:00 22.7\n", + "4 Bankstown 2010-01-01 01:00:00 22.6\n", + "\n", + "Rows = 220326\n" + ] + } + ], + "source": [ + "# Exploration - Temperature Dataset\n", + "print(\"Locations = {}\".format(set(df_temperature.location)))\n", + "print(\"Date Min = {} | Date Max = {}\".format(df_temperature.date_time.min(), df_temperature.date_time.max()))\n", + "print(\"Temp Min = {} | Temp Max = {}\\n\".format(df_temperature.temperature.min(), df_temperature.temperature.max()))\n", + "print(df_temperature.head())\n", + "print(\"\\nRows = {}\".format(len(df_temperature)))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Regions = {'NSW1'}\n", + "Date Min = 2010-01-01 00:00:00 | Date Max = 2021-03-18 00:00:00\n", + "Demand Min = 5074.63 | Demand Max = 14579.86\n", + "\n", + " date_time total_demand region_id\n", + "0 2010-01-01 00:00:00 8038.00 NSW1\n", + "1 2010-01-01 00:30:00 7809.31 NSW1\n", + "2 2010-01-01 01:00:00 7483.69 NSW1\n", + "3 2010-01-01 01:30:00 7117.23 NSW1\n", + "4 2010-01-01 02:00:00 6812.03 NSW1\n", + "\n", + "Rows = 196513\n" + ] + } + ], + "source": [ + "# Exploration - Demand Dataset\n", + "print(\"Regions = {}\".format(set(df_demand.region_id)))\n", + "print(\"Date Min = {} | Date Max = {}\".format(df_demand.date_time.min(), df_demand.date_time.max()))\n", + "print(\"Demand Min = {} | Demand Max = {}\\n\".format(df_demand.total_demand.min(), df_demand.total_demand.max()))\n", + "print(df_demand.head())\n", + "print(\"\\nRows = {}\".format(len(df_demand)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Regions = {'NSW1'}\n", + "Forecast Date Min = 2009-12-30 12:31:49 | Forecast Date Max = 2021-03-17 23:31:33\n", + "Predict Date Min = 2010-01-01 00:00:00 | Predict Date Max = 2021-03-18 00:00:00\n", + "Forecast Demand Min = 4422.46 | Forecast Demand Max = 14736.66\n", + "\n", + " id region_id period_id forecast_demand date_time_forecast \\\n", + "0 2009123018 NSW1 71 7832.04 2009-12-30 12:31:49 \n", + "1 2009123019 NSW1 70 7832.04 2009-12-30 13:01:43 \n", + "2 2009123020 NSW1 69 7832.03 2009-12-30 13:31:36 \n", + "3 2009123021 NSW1 68 7832.03 2009-12-30 14:01:44 \n", + "4 2009123022 NSW1 67 7830.96 2009-12-30 14:31:35 \n", + "\n", + " date_time_prediction \n", + "0 2010-01-01 \n", + "1 2010-01-01 \n", + "2 2010-01-01 \n", + "3 2010-01-01 \n", + "4 2010-01-01 \n", + "\n", + "Rows = 10906019\n" + ] + } + ], + "source": [ + "# Exploration - Forecast Dataset\n", + "print(\"Regions = {}\".format(set(df_forecast.region_id)))\n", + "#print(\"Periods = {}\".format(set(df_forecast.period_id)))\n", + "print(\"Forecast Date Min = {} | Forecast Date Max = {}\".format(df_forecast.date_time_forecast.min(), df_forecast.date_time_forecast.max()))\n", + "print(\"Predict Date Min = {} | Predict Date Max = {}\".format(df_forecast.date_time_prediction.min(), df_forecast.date_time_prediction.max()))\n", + "print(\"Forecast Demand Min = {} | Forecast Demand Max = {}\\n\".format(df_forecast.forecast_demand.min(), df_forecast.forecast_demand.max()))\n", + "print(df_forecast.head())\n", + "print(\"\\nRows = {}\".format(len(df_forecast)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chamath\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], + "source": [ + "#Note: this code chunk may be slow to run\n", + "#Forecast_interval is the number of hours between prediction and it's forecast\n", + "interval = 60*60 #sets the interval in seconds\n", + "df_forecast[\"forecast_interval\"] = df_forecast.date_time_prediction - df_forecast.date_time_forecast\n", + "df_forecast.forecast_interval = df_forecast.forecast_interval.apply(lambda x: x.total_seconds()/interval)\n", + "\n", + "#Rounding forecast time to intervals of 30mins to match df_demand and only have one record where forecast interval is ~24hrs\n", + "interval_min, interval_max = 23 , 25 #sets a window for forecast periods\n", + "df_forecast_near24hour = df_forecast.loc[(df_forecast.forecast_interval > interval_min) & (df_forecast.forecast_interval < interval_max)]\n", + "df_forecast_near24hour[\"date_time_forecast_rounded\"] = df_forecast_near24hour.date_time_forecast.apply(lambda x: x.round(freq='30min'))\n", + "df_forecast_near24hour_1instance = df_forecast_near24hour.loc[df_forecast_near24hour.groupby(\"date_time_forecast_rounded\")[\"forecast_interval\"].idxmax()]\n", + "\n", + "#Merge forecast data with demand data\n", + "df_forecast_near24hour_1instance_with_demand = pd.merge(df_forecast_near24hour_1instance, df_demand, left_on = \"date_time_forecast_rounded\", right_on = \"date_time\")\n", + "df_forecast_near24hour_1instance_with_demand[\"forecast_error\"] = df_forecast_near24hour_1instance_with_demand.total_demand - df_forecast_near24hour_1instance_with_demand.forecast_demand" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_year = 2018\n", + "plot_hour = 16\n", + "\n", + "df_plot = df_forecast_near24hour_1instance_with_demand.loc[(df_forecast_near24hour_1instance_with_demand.date_time.dt.year == plot_year) & (df_forecast_near24hour_1instance_with_demand.date_time.dt.hour == plot_hour)]\n", + "\n", + "plt.figure(figsize = (12,7))\n", + "plt.subplot(2,1,1)\n", + "plt.title(\"Forecasting 24h into the future\")\n", + "plt.plot(df_plot.date_time.dt.dayofyear, df_plot.forecast_demand, label = '{} {}:00 - Demand Forecast'.format(plot_year, plot_hour))\n", + "plt.plot(df_plot.date_time.dt.dayofyear, df_plot.total_demand, label = '{} {}:00 - Demand Actual'.format(plot_year, plot_hour))\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "plt.subplot(2,1,2)\n", + "plt.plot(df_plot.date_time.dt.dayofyear, df_plot.forecast_error, 'g.-', label = '{} {}:00 - Error'.format(plot_year, plot_hour))\n", + "plt.legend(loc = 'upper right')\n", + "\n", + "plt.figure(figsize = (5,4))\n", + "sns.histplot(df_plot.forecast_error, bins = 50);\n", + "plt.axvline(df_plot.forecast_error.median(), color='r', ls = '--', label = 'median')\n", + "plt.axvline(df_plot.forecast_error.mean(), color='m', label = 'mean')\n", + "plt.legend(loc = 'upper right')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Forecast Error as Portion of Actual Demand')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAAHGCAYAAADe9IW8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeXwV1d0/8M/JAtmBQCAkkGEVgmLtD3AjpeUpVUvr8tSHijZItWq1rbYF+6hVKhZja7XRlraoqIhiq7VuqDwqVG0MLiyKgFx2GCBhD1lv9pzfHzOT3An35s7Mzd0/79frviZ37jl3zl2SfOfM95wjpJQgIiIiIqLQSwh3A4iIiIiI4hWDcSIiIiKiMGEwTkREREQUJgzGiYiIiIjChME4EREREVGYMBgnIiIiIgoTBuNERA4IIYYIIcqEEHVCiD96eTxVCPGGEKJGCPFSONoYKCHEY0KIBeFuR3dCCCmEGGOx7K+FEE8Gu009HH+/EGJGLz3XVCHELiFEvRDiit54TiIKPwbjROSIEOIDIcQpIUTfcLclTG4CcAJAlpRyvpfH/wfAEAADpZSzQtoyB4QQPxRClHvuk1LeLKVcFIRjnSGEeF0IcVwIUSWEeEcIMc5H2ff04DvJybGklA9IKW+w2K6FQogVTo6j139GCHG/0/oW/BbAX6SUGVLK1/y0ZUQg7xsRhQ6DcSKyTQgxAsDXAEgAl4X42JESXCgAtknfK6cpAHZKKdvsPnEEvcZg6Q9gJYBx0E5Y1gF4vXshIcQPAMT6e2GHAuDLUBxIaBgjEIWClJI33njjzdYNwG8ArAVQCuDNbo+lAvgjABVADYByAKn6Y0UAPgJQDeAggB/q+z8AcIPHc/wQQLnHfQngpwB2Adin7/uT/hy1ADYC+JpH+UQAvwawB0Cd/vhwAH8F8Mdu7X0DwC98vM4LAazXX8d6ABfq+58B0AqgBUA9gBnd6t2nP9aqP/4jaJ0f9+jvyzEAzwLop5cfob/GHwE4AKBM33++x/v1BYBveBwjG8AyAJUATgF4Td8/AMCbAI7r+98EMKzbe7tXf1/2AfgBgEIATQDa9fZWe7zO+/WfvwHgEID5evsPA7jO43kH6u9lrf5e3e/5Gfr5PmXrr3+gx75+AHbq74EEkNTt+3Cz/n04pX+uwsdzLwSwotv7PFd/n08AuFt/7JJun9kX+v48aCcOVQB2A7jRx3Fu6vadeEPfvx/A7QA2Q/sevQggxaPedwFs0j/jjwCc7eP59wDoANCoP39f/bln+HitB/TXWq/fLvB8vNv7keTxe1gC7Xe7EcAY/XN4Sv+8K/TPNTHcf4N44y2WbmFvAG+88RZ9Nz0o+QmASXoAMsTjsb/q/9TzoQXFF+qBQwG0APBqAMl68HaOXucD+A/GV0ML2ozAvlh/jiRoAeIRI8gB8CsAW6D1vAoAX9HLngsteE3Qyw0C4PZsv8cxs6EFenP0Y1yt3x+oP/4M9EDVx3vUPfC5Xn/fRgHIAPAKgOf0x4yg6FkA6dBOaPIBnAQwE1og/y39fo5e5y1ogd0A/f38ur5/IIArAaQByATwEroC9XRowfI4/f5QAGd6e8+7v0ZowXgbtFSJZL1dbgAD9Mdf0G9pACZAO1GyGoxfAeBwt31/BfBLdAsYPb4Pb0LrYS+AduJxib/PweO5lurv8VcANAMo9PaZ6fv+A+BvAFIAnKMf65s+jnXadwJawLwOWlCfDcAF4Gb9sf8H7cTmPGi/K3P18n19PP9+mIPv7ve9vdYkb497KwPt9/AAgDOhfeeTAbwG4HH9uzNYfy0/DvffIN54i6UbL0ERkS1CiCJol8v/KaXcCK3H7hr9sQRoQefPpZQVUsp2KeVHUspmaD2wa6SU/5BStkopT0opN9k49O+klFVSykYAkFKu0J+jTUr5R2gBv5F3fAOAe6SUO6TmC73sOmi9k9/Uy80G8IGU8qiX430HwC4p5XP6Mf4BYDuAS2202dMPAJRKKfdKKesB3AVgdreUlIVSygb9NRYDWCWlXCWl7JBSrgawAcBMIcRQAN+GFtSd0t/P/+jvy0kp5ctSSreUsg5aT+fXPY7RAeAsIUSqlPKwlNJO2kMrgN/qx1sFrcd1nBAiEdoJwL36cbcBWG7lCYUQw6AF3vM89k0GMBXA4h6q/l5KWS2lPADgfWiBslX3SSkbpZRfQLvi8BUfbRsO7WrOHVLKJv37+iS0EzQ7/iylrJRSVkG7emC09UYAj0spP9V/V5ZDOzk43+bz96ZnpJRfSi29Khva9+wX+vfyGIBHoP3eEFEvYTBORHbNBfCulPKEfv/v+j5A62lOgRagdzfcx36rDnreEULMF0K49NlKqqFdTh9k4VjLoQW60LfP+SiXBy2lxJMKrcfaie7Pp0LrfRzisc/zNSoAZgkhqo0btMBwKLTXVyWlPNX9IEKINCHE40IIVQhRC6AMQH8hRKKUsgHAVdBSPA4LId4SQoy38RpOSnMOvBtaL3+O/lo822/6vLwRQuQAeBfA3/STHeOE7m/QTuh6yrc/4qUdVlmtmwftfa7z2OfkO+DreAqA+d0+4+H6ccOl+3cwGdp3xWjf49B6yImol3BgDBFZJoRIBfB9AIlCCCPA6Ast2PsKtNSQJgCjofU4ejoILU3EmwZo6Q2GXC9lOgdKCiG+BuAOaD3cX0opO4QQp6ClpBjHGg1gq5fnWQFgq97eQmiX4b2phBaMeCoA8LaP8v50f74CaGkfRwEM0/d5DgY9CC2N5cbuT6T3jGcLIfpLKau7PTwf2hWC86SUR4QQ5wD4HPp7I6V8B8A7+md5P7SUDWMwrlPH9dcyDFqeN6AFlT4JIQZAC8RXSilLPB7KAjAZwItCCEBL3wCAQ0KIWVLKDwNopz/d34NKaO9zpkdAXgAtd9pKfX8OAijp9vrt6On3xltbbP2e6e1rBjDIz4kREQWAPeNEZMcV0Ab5TYB2qf0caAHthwCulVJ2AHgaQKkQIk8IkSiEuECf/vB5ADOEEN8XQiQJIQbqgSKgDWD7nt6rOwbaQMaeZEIL/o4DSBJC/AZaEGd4EsAiIcRYfVaIs4UQAwFASnkI2gDD5wC8bKS9eLEKwBlCiGv09l6lv+43rb5Z3fwDwC+FECOFEBkAHgDwYg9BzgoAlwohLtbfxxQhxDeEEMOklIcB/B+AvwkhBgghkoUQ0zzem0YA1UKIbAD3Gk+oz41+mRAiHVqQVQ/t8wT0kwIhRB+7L0xK2Q4tB36h/hmOB3Ctr/JCiCwA7wBYK6W8s9vDNdB6ho3v10x9/yQAn9ptm01HAYwwZhGRUh6ENqjyd/r7fza07+bzPdQfZeN4SwHcLIQ4T/+epgshviOEyLRYfxO0VKdkPbXnfzweOw4tJWlUt/LThBAFQoh+0FKlfNK/Z+8C+KMQIksIkSCEGC2E+HpP9YjIHgbjRGTHXADLpJQHpJRHjBuAvwD4gZ7/fDu0HvL10GageBDagMkD0AKr+fr+TejK1X0E2iwUR6GlkfgKdgzvQAtGd0JLG2iC+fJ6KYB/QgskaqHNBpHq8fhyABPhO0UFUsqT0Ga6mA9t4OT/AviuR3qOXU/rxyuDNotJE4Bbezj+QQCXQ5sV5ji01/crdP3dngMth3s7tEGAv9D3PwrttZ4A8AnMPfkJ+uuphPYZfB3aQFwAeA/atHlHhBBOXuPPoKUKHdFf5z+gBfze/DeAKQCuE9oCNsatQM/x9/xuHdfrHJVStjholx3G4kwnhRCf6T9fDW2gYyWAV6Hlxa/2Uf8pABP0lI4e5wEHACnlBmh543+BNjh4N7SBtFYtgHYF6BS0GXz+7vHcbugzo+jtOV9v94vQZnbZCGsnltcC6ANgm36cf0FLlSKiXiKkDOTKJBFR9NF7kVcAGKH35lMvE0I8CCBXSjnXb2EiojjGnnEiiitCiGQAPwfwJAPx3iOEGK+nAwkhxLnQ0jleDXe7iIgiHYNxIoobQohCaIurDIWWzkG9JxNa3ngDtBShP8LLqppERGTGNBUiIiIiojBhzzgRERERUZgwGCciIiIiCpO4XvRn0KBBcsSIEeFuBhERERHFsI0bN56QUuZ4eyyug/ERI0Zgw4YN4W4GEREREcUwIYTq6zGmqRARERERhQmDcSIiIiKiMGEwTkREREQUJgzGiYiIiIjChME4EREREVGYMBgnIiIiIgoTBuNERERERGHCYJyIiIiIKEwYjBMRERERhQmDcSIiIiKiMGEwTkREREQUJgzGiYiIiIjChME4EREREVGYMBgnIiIiIgqTpHA3gIiIiCielJSUwOVyAQBUVQUAKIoCACgsLMTdd98dtrZR6DEYJyIiIgoTt9sd7iZQmDEYJyIioqBqbGzE8uXLMXfuXKSmpoa7OWHn2fNdXFwMAFixYkW4mtMj9uIHH3PGiYiIKKjKyspQWlqKsrKycDeFAuB2u9mTHwTsGSciIqKgmjZtGubNm4dp06aFuylkUzT14kcrBuNEREQUVKmpqbj55pvD3QyiiMQ0FSIiIoopjY2NeOyxx9DY2BjuphD5xWCciIiIYgpz1CmaMBgnIiKimMIcdYomzBknIiKimMIcdYom7BknIiIiIgoTBuNERERERGHCYJyIiIiIKEwYjBMRERERhQkHcBIRERHFuJKSErhcLgCAqqoAAEVRUFhYaFplk0KPPeNERBRzuOgLkW9utxtutzvczSAde8aJiCjmGIu+jBw5EhdffHG4m0MUdp6938XFxQCAFStWhKs55IE940REFHO46AsRRQv2jBMRUczhoi9EFC0YjBMREVHE8DXQEAAHG1JMYjBOREREEYmDDCkeMBgnIiKiiMGBhhRvOICTiIiIiChM2DNOREREFCWMnHrm08cOBuNEREREUYb59LGDwTgRERFRlDB6v5lPHzuYM05EREREFCYMxomIiIiIwoTBOBERERHFlMbGRjz22GNobGwMd1P8YjBORERERDGlrKwMpaWlKCsrC3dT/GIwTkREREQxZdq0aZg3bx6mTZsW7qb4xdlUiIiIiCimpKam4uabbw53MyxhME5ERBSFGhsbsXz5csydOxepqanhbk5UMxbSAWBaTIcL6VAoME2FiIgoCkVTTmw0cbvdXFCHQoo940REFHPiodc4mnJiI51n7zcX04k/vq6MAAjJ1RH2jBMRUcyJh15jIyc2Vk82iMIhHFdG2DNOREQxh73GRGRVuK+MMBgnIqKYE00zKRBRfGOaChERRaxoWkUvmtpKRJGDwTgREUWsaMr9dtpWBvFE8Y1pKkREFLGiKffbaVuNIH7kyJG4+OKLLdeLhxljiILFcwYVT8Y+I3fcU7BmVmEwTkREESuacr+dtjXUQTwRaUH3p+s/Q3tSpml/QnsLAOCjz3eZ9ie21QWtLQzGiYiIwijUQTwRadqTMtGQfa6lsulV64LWDuaMExER9YKqqipcf/31qKqqCndTgiYeXiNRqDEYJyIi6gVLly5FeXk5li5dGpLjrVmzBqWlpVizZk1IjgeE/jUSxQMG40RERLpAen5vvPFGFBUV4cYbbwxCyyLDnDlzoCgK5syZE+6mEMUMBuNERES6QHp+s7Oz8fTTTyM7OzsILTvdjBkzMG/ePMyYMcNWvUBOOLZs2QJVVbFlyxbbdYnIOw7gJCIi0t14443YsWNHVPRuOx346XnCcccdd9iqO2XKFBQVFWHKlCm2j0sUSVRVRWJbneWBmYltdVBVNShtYc84ERFFrFAviBPq3u1wCCTVZP369SgvL8f69euD0DKi+MSecSIiilhcEKf3bdy4EaqqYuPGjcjLy7NVl9MpUqxQFAUVVS22pjZUFCUobYmIYFwIkQhgA4AKKeV3hRAjAbwAIBvAZwDmSClbhBB9ATwLYBKAkwCuklLu15/jLgA/AtAO4DYp5TuhfyVERNSbuCBOZImmRZiIgsFz5U5VVeF2u72WS0tL6wzeCwsLe3zOiAjGAfwcgAtAln7/QQCPSClfEEI8Bi3IXqJvT0kpxwghZuvlrhJCTAAwG8CZAPIArBFCnCGlbA/1CyEiot4TDwvihLoX3+nAT6JQ6B7sAjAFtcFYjt6Ot99+G0eOHgWQCKADgPRarrauHkeOngDQ7jfXPOw540KIYQC+A+BJ/b4A8F8A/qUXWQ7gCv3ny/X70B//pl7+cgAvSCmbpZT7AOwGYO26AxERxRwjiA9liorT/HajF7+srCxILdOUlJSguLgYF110EZ5//nnceOONKC4uRklJSVCPS+T0d8PtdvvseY4lkdAz/iiA/wWQqd8fCKBaStmm3z8EIF//OR/AQQCQUrYJIWr08vkAPvF4Ts86REREQWcswpOfn49LL73Ucj2nvfhOe9TjIbihyGInbcyz57u4uBgAsGLFiqC2z45LLrnEUZpKeXm5z+cMazAuhPgugGNSyo1CiG8Yu70UlX4e66lO92PeBOAmACgoKLDVXiIiot7mNBXHbl68EeREYoBDsS2a0sb8cZomc8899/h8LNw941MBXCaEmAkgBVrO+KMA+gshkvTe8WEAKvXyhwAMB3BICJEEoB+AKo/9Bs86JlLKJwA8AQCTJ0/2nuhDRERkk9NcbKc93KEKcHzl8EZC/i5FBw787VlYc8allHdJKYdJKUdAG4D5npTyBwDeB/A/erG5AF7Xf16p34f++HtSSqnvny2E6KvPxDIWgLVZ3ImIiHqB0zx1pznj4ciLj5ccXqJQCnfPuC93AHhBCHE/gM8BPKXvfwrAc0KI3dB6xGcDgJTySyHEPwFsA9AG4KecSYWIiOwKx/zkkX4JP9JzeImiXcQE41LKDwB8oP+8F15mQ5FSNgGY5aN+CQAOCSciIsdBdTjmJ4/VS/iRPkUdUaQI+9SGREREvc1p6kek91J7cjpdXDgwvYXIt4jpGSciIuotgUwXuG7dOnz/+98PWZpKNPXi28H0FiJrGIwTEVHMcZr6sXTpUpSXl2Pp0qW44447gtCy0zkNqqOpF5+ii2eKkSdjn3Fy5YmpR84xGCcioqALx8BIJ+bMmYM1a9Zgzpw5ITsmg2qKNC6XC59u2ISOlGzTftGizY3x8dYDpv0JTVUha1ssYjBORERBF+kpFYYtW7ZAVVVs2bIFeXl5ITlmqBb9od4Xy4NUO1Ky0TTyO5bKpux7K8itiW0MxomIKOiipfc3WtoJRFdb4wEHqJJTDMaJiCjonPb+Ok1vcVovHNMMRlNbyYyDVKk3MBgnIqKItWbNGpSWliI/Px+XXnqp5XrRlMIRTW2NdEbaSKyljFBs4zzjREQUc8KRwuF03u8pU6agqKgIU6ZMCVLL4g/nNadowp5xIqIoFS0zlARixowZmDdvHmbMmGGrXjhSOJz24q9fvx7l5eVYv349e8YDZPR+M2WEogl7xomIopTTVSYpsrBnnCi+MRgnIsuiafnteBAPs2lE0wmH0178d999F+Xl5Xj33XeD1DIiimRMUyEiyzjQLLKEOhUjHGkxoT7hCOQ1Ov083nvvvc7t7Nmzeywby/NaE8UrBuNEZJnTwCgecpvjQTycjIXjNS5cuBBz587FwoULbdXjAEWKZDxxtI7BOBFZxpUC41s40mJC/d0Jx2vMy8vD6tWrLZXlvNYUjXji2DMG40RkmdMe7njIbY4H4ZihZOzYsejfvz/Gjh0b0uM6wStARF144mgdB3ASkWVOB9MZQRwDlPjldPDvQw89hOrqajz00ENBapmZMT3hmjVrQlqXiOIXg3Eisow93OSU0xO5BQsWQFEULFiwwFa9cMz809DQYNoSEVnBYJyIiILO6YmckU+dl5dnq57T4N/p9IQAsGfPHtPWKk4ZShTfGIwTkWXRNOczxTenC+kEklJ1yy23oKioCLfccoutekxvIYpvDMaJyDKmqZBToT6R81xiPlSys7Px9NNPIzs7O2THJKLox2CciCzjQExyymlPtdMUjnCcODpt69SpU1FUVISpU6cGqWVEFMkYjBMRkSWB5DY77amOptQop20NRy8+EUUOBuNERHHGaVAdSGDstKfaaT2nediBnHDYmRO9pKQExcXFKC4uxqJFi5Ceno5nnnkGxcXFKCkpsX1sIopeDMaJiOKM06A6msYMtLS0mLZWBXLCUVJSgurqatvBdGNjIxISEpCYmGj7mEQU/bgCJxFRnHEaVAeyAqfTZe2d1uvTp49pa1UgJxyDBw82bXvC1QmJyMBgnCgOcdnu+BaOZe1DnaYyadIkKIqCSZMm2aoXyHszefJkvPzyy5g8ebKj+kTxrKSkBC6Xy7TPuG+csHoqLCw0ndRGMwbjRHHIaW8jxbdATuKcBrlO6y1btgyqqmLZsmW2/mEH8hpnzpyJ48ePY+bMmXabSxT3XC4XPt3wOdr79O/cl9DaBgD4aPM+U9nEluqQti3YGIwTxaFoyv2lyBGOkzinwfH48eNNW6t4okoUPu19+qMxb7rfcqmV74egNaHDYJwoDoUjTYGin9O5wgHnQbUxK0p+fj4uvfRSy/WmT5+OoqIiTJ/u/x+7p4kTJ0JRFEycONFWPYCBPPXMWxoGED+pGOQbg3EiIrJk7dq1KC8vx9q1a20FxkDoA1XPubvtHM9ueotngLV///7OKQo3bNjAIIpMXC4X1m3cBKTnmB9o7gAArNteYd7fcDxELaNwYzBORERB5zQ1asaMGZg3bx5mzJgRkuONGjXKtLWDUxSSX+k5EBNnWSoqt7wU5MZQpGAwTkREloRj2fZQD/zMyMgwbf3hFIVEFCgu+kNERJYEsmx7tCxr77QnnojIKQbjRERkSSADOEM9g09VVRWuv/56VFVVheR4REROMU2FiIgsCWQAZ6hn8FmyZAnKy8uxZMkSWwMpV61ahdLSUuTk5ODKK68MYguJgs/uDC6cvSU82DNORBRnGhsb8dhjj6GxsTHij+m0ntN5xrdu3WraEkUzl8uFTzduwifbDplutU0dqG3qMO37dOMmr4E7BR97xomI4ozTaQYDyad2Ol+407baXQ3T6EHcvn07AOCjjz5CcXExewop6snUQWgbe7nfckm7Xg/4WJxL3RkG40REccZp/nYgqSYtLS2mrVWB5Kk70dHRgT59+iA7OzskxyOKJcaS9h19zb8/oqUdAPDxFtW0P6GZYzoABuNEccnpaogUG8KxAmufPn1MW6uc5qnb7VE3euY4PSFRYDr6ZqOx4CJLZVMPvBvk1kQHBuNEcYjLdlOoOU1xiZYedSKKPoltdUivWoeEdjeEbPdaRopEdCSmIbGtLmjtYDBOFIdCPc0cBQevcPgWyMwvRBT7CgsLO39WVRVut9trubS0NCiKclqd3sRgnCgOhSNNgXpfqK9wVFVV4fbbb8fDDz9sO6fa6QBOp+ktTnvUifxxOkiRIkskDRplME5EFKVCfYXD6dzdgPPgeNKkSVAUBZMmTbJVz2kQT+SPy+XCuo1fQGQMNu2XzRIAsH7HYfP++mMhaxtFJwbjRERRKtRXOJzO3Q04D46fe+45qKqK5557DnfccYflelzWnoJJZAxG8ldnWyrb+vkLQW5N5FBVFQnNVZYHZiY0V0FV/ZeLdQzGiYjIkunTp6OoqAjTp0+3XXfq1KkoKirC1KlTbdWbM2cO1qxZgzlz5tiqx1QsiiXeUmM4d3fsYDBORESWrF+/HuXl5Vi/fr3tHHWndTdu3AhVVbFx40bk5eVZrsfBrRRLjJU0kTaoa2dTBwDgU9chc2H3iRC2zExRFFTWwtbUhsbgyHjGYJyIiCyZOHEiFEXBxIkTbdcNdX670wGjRBErbRA6Cr/nt1iC65UQNIZ6U0K4G0BERNFh6dKlUFUVS5cutV3XSBux20vtNL2Fs6kQUbRgME5EREHX2NiIxx57DI2Njbbqeaa3EBHFIgbjRERkyaxZs5CSkoJZs2bZrmukjaxZs8ZWPafpLZzakIiiBYNxIiKyZPHixWhqasLixYtDdkyn6S1nnnkm+vfvjzPPPDNILSMi6h0cwElEFKVCPWPIggULsHv3bixYsMB2Xafzfjt9jYsWLUJ1dTUWLVqEZcuW2W0uUVxTVRUJTVVI2feWpfIJTSehqpIzozjEnnEioihVVlaG0tJSlJWV2arnNH87Ly8Pq1evtjXFYKBWrVqF0tJSrFq1ylY9DuAkomjBnnEioijlNJ86HNP+OT3mpk2bOrdXXnml5XoFBQVYv349CgoKbLeVKN4pioLKOoGmkd+xVD5l31tQFO13zViBU7TUQchWr+WlSIbsk4mE5ioA7E1nzzhRFHPaw0mRxenn6DSfOhy9xlVVVaZtsE2ePNm0JaLgKywsxHmTv4oLJioYOigTWekpXm9DB2XigokKzpv8VRQWFoa72WHHnnGiKGakKYwcOdL2iogUOUL9OTqdaSSQHHUjlaasrAxz5861XO+cc87Biy++iHPOOcdvWc8lw/fv34/09HT861//wquvvsrlwYlCgL9jzjAYJ4pioV7VkIIj1J+j04V0AklvufDCC/Hhhx/iwgsvtFVv+vTpKCoqwvTp023Va2xsREJCAhITE23Vo/jhefLmydhXXFxs2s8TOgoWBuNEUcxIU7Ar1LNwhPp40cbp5+jU+++/j/Lycrz//vu28rADSTXZvXu3aWuV56I//q4aeAZKRiC1YsUKmy2leOFyubDusy+QkDnEtL9Dz97asOtI1766o6FsGsUZBuNEcSjUaRFMpwkOpyc527dvN22t+ve//925tZNqAmhpI55bq6ZMmYKioiJMmTLFVj0iKxIyhyBl8g/8lmva8HwIWkPxisE4URwKdVoE02mCw+lJznXXXYcPPvgA1113na3jtbe3m7Z2OO1VX7t2LcrLy7F27dqQzfxCFCtUVYVwn0LSrtf9lhXuE1BV+7/bvUVVVSQ2VyO18n2/ZRObq6GqaghaFRoMxoko6EKdhhEvnJ7kbNy4EaqqYuPGjbbmDA9kusAhQ4Zg3759GDJkiP/CHjhfOMUKVVWBhlOQW16yVqHhOFS1LbiNoojAYJwoDjFtJDY4Pclx2kt99tln4+WXX8bZZ59t+5h33nknZs+ejTvvvNNvWc+BdUYqzdKlSzkrCpFNiqLgcEMi2sZe7rds0o9jGBoAACAASURBVK7XoSjDQtAq7xRFQUVNBxrz/A/WTq18P6ZW+2QwThSHmDYSWUI9wHXt2rWdWzu5306nRASAN954A01NTXjjjTcwYcIEy/U6OjrQp08fDBgwwPYxKT54mxXF14woQPhmRVEUBUcbkyAmzrJUXm55CYqSH+RWUSRgME5EQcfZVHrm9EqF0/d14cKFmDt3LhYuXGirndXV1aatHRdddBGeeeYZXHTRRX7LclYUssOYFSUxK7dznzEjysbd5llQ2muPgCjShHUFTiHEcCHE+0IIlxDiSyHEz/X92UKI1UKIXfp2gL5fCCH+LITYLYTYLIT4fx7PNVcvv0sIYW+YP1GcMYI/YyEWq5yuFOn0ePHC6ZUKp+9rXl4eVq9ebStfHAD++c9/mrZ23H///Whvb8f9999vuy6RP4lZuUg//9rOW+Y3f4HMb/7CtC/9/GtNATtRpAhrMA6gDcB8KWUhgPMB/FQIMQHAnQD+LaUcC+Df+n0A+DaAsfrtJgBLAC14B3AvgPMAnAvgXiOAJ6LTOQ3+jEVf1qxZY6sep6frmdNl7Z1+jlVVVbj++utt54y73W7T1o577rkHiYmJuOeee2zXJSKKZWENxqWUh6WUn+k/1wFwAcgHcDmA5Xqx5QCu0H++HMCzUvMJgP5CiKEALgawWkpZJaU8BWA1gEtC+FKIoorT4M8pz4VbqPc4/RyXLFmC8vJyLFmyxFa9888/37S1Y+XKlWhvb8fKlStt1yUiimURkzMuhBgB4KsAPgUwREp5GNACdiHEYL1YPoCDHtUO6ft87SeiXjRjxgzMmzcPM2bMsFWPA0Yjy6hRo0xbq+666y6cPHkSd911l+1jBpJvTkQUyyIiGBdCZAB4GcAvpJS1QgifRb3skz3s93asm6CluDiaK5eI7OM845HF6dzd2dnZePrppx0d0+mqn0REsS7cOeMQQiRDC8Sfl1K+ou8+qqefQN8e0/cfAjDco/owAJU97D+NlPIJKeVkKeXknJyc3nshRHGAAzF9czq4NRyMzy9UA3gBIDMz07QlIiJNWHvGhdYF/hQAl5Sy1OOhlQDmAvi9vn3dY//PhBAvQBusWaOnsbwD4AGPQZsXAbB/HZWIeuR0IGY4pjYM9TGjaSGlCy64AB9++CEuuOACW/WMAbz5+fm2l6YPZI5yin3e5goHInO+cKLeFu40lakA5gDYIoTYpO/7NbQg/J9CiB8BOADAmCF/FYCZAHYDcAO4DgCklFVCiEUAjNFhv5VS2psmgCiOVFVV4fbbb8fDDz+M7Oxsy/U8B2LaCTjDEagGEjg6EU158aqqmrZWOV25EwCGDh1q2hJ5crlcWP/ZZiT2M0892N6qbT/bc8y8vya+5gtXVRVwn0KC6xX/hd0noKrtwW8U9ZqwBuNSynJ4z/cGgG96KS8B/NTHcz0NwFkyI1GcWbx4McrLy7F48WLce++9lusNHz4cKSkpGD58uP/CHji1YWQxxuX0MD7Hq9WrV3durazc6dnbuXnzZgBAeXk5iouL2atJp0nsl4v+X7vOUtnqD5cFuTVEoRP2nHEiCr22tjbT1qoFCxagqakJCxYssFUvkKkNneYpO535xalw5NM7fW9Gjx5t2lp19OhR09aO1latizMxMdF2XaJ4pygKkDYIHYXf83tD2iCtPEUNBuNEYRaOgX/nnHOOaWvVbbfdZtpaFUjPeLQMGg0kTcXpd+C1115DaWkpXnvtNVv19u/fb9oGy913340VK1ZgxYoVOPvss5GVlYWVK1dixYoV7BUnItIxGCcKM6erWgLOV1KcPn06ioqKMH36dFv1SktLTVur3n33XZSXl+Pdd9+1VQ8AJk6cCEVRMHHiRFv1oiWIB5y31Xg/7b6v+fn5pq1VGRkZpq0dycnJGD9+vK0xCkRE8YDBOFEUe+SRR1BeXo5HHnnEVj2naSMTJkwwba3yzDW267nnnoOqqnjuueds1Qt1nnogwb/TXvVhw4aZtlZ9/PHHpq1VoepRJyKKJwzGiXpJOHKbKysrTVurnAaqt956KxRFwa233mqrntMgHgBmzZqF/v37Y9asWf4LewgkT92JQNNU1q1bZ/u7c/XVVyMlJQVXX321rXojR440ba1yOtaAKJaoqgpZfwytn79g6Sbrj9meuYjiC4Nxom6cBtXhSIuYP38+UlJSMH/+fFv13n//fZSXl+P999+3VW/jxo1QVRUbN260VW/VqlWmrR0vvfQSqqur8dJLL9mqF+qecWOVUSdzmi9duhTl5eVYunSprXqPPvoompqa8Oijj9qq969//cu0tWrMmDGmLRERBY7BOFE3ToNqpz2jq1atQmlpqaNA9dVXX0VTUxNeffVVW/W2bt1q2lpVX19v2lr1y1/+0rS1I1p6xgMxZ84cKIqCOXPm2Kpn5Pzbzf13GlQnJCSYtkTxSFEUiIzBSP7qbEs3kTGYs5tQj/gXlaibUC/e4jQwBoDx48ebtlaNGDHCtLVq27Ztpq1Vf/jDH0xbO6KlZzwQW7Zsgaqq2LJli616V1xxBebNm4crrrjCVr3LLrvMtLWqoqLCtCUiosD5DcaFxt4KH0S9JBzT/jlNN3A6K8oZZ5xh2toxatQoJCYmYtSoUbbqOR2IV1NTY9pa1bdvX9PWjksvvRQpKSm2V9Fcu3YtysvLsXbtWlv1wvGdc3ri4DTX/MSJE6atVdXV1aYtUTRTVRUddUfRtOF5v7eOuqPM+6ag8RuM66te2pvElqiXRNNCKi0tLaatVU5XQwSA22+/He3t7bj99ttt1XM6gM/oubXbg5uVlWXa2vHAAw+gqakJDzzwgK16Tj8Pp9+5QIJ4pyk1niup2vHss8+atv6UlJSguLgYHR0dAICOjg4UFxejpKTE1nGJiOh0SRbLfSKEmCKljPzkS4opTueYBrTgaPny5Zg7d66tXm4jhzsnJwdXXnml5XpOg78vvviiczt79mxbdWfOnInHH38cM2fOtFXv008/7dxee+21lutdfPHFePrpp3HxxRfbOt6RI0dMWzsOHDhg2lrl9PNw2kttXBnJz8+33Ys/duxY9O/fH2PHjrVVr6mpybS1asCAAXC73RgwYICteob09HRH9Sh6lJSUwOVynbbf2FdcXGzaX1hYiLvvvtt2PaNuOCiKguMtfZEy+Qd+yzZteB6KkhuCVlE8shqMTwfwYyGECqABgIDWaX520FpGMcVpYLxkyRKoqoolS5Zg0aJFto7pNKj2zOG2U+/zzz/v3NqZas649OnkEqjTnmojdcNuCofRW1xWVoY777zTcr20tDTT1o5Zs2Zh8eLFtgdwbtiwoXNr5/Pw7KW2e9Lh1H333Yfq6mrcd999WL58ueV6brfbtLVqzJgxqKiosDyA01gtc9asWdi+fTveeust5OXl2TomRReXy4X1n29GnwFDTfvb2rQreF/sP965r+XU4dPqJfUzB67trdr2873HzM9XY/8EvTtVVdFeW42GT/xf6WmvPQJVtXfyShRsVoPxbwe1FRTz7AbGRu/Kpk2bAAD/93//h3379nX2vlhh1N20aZOtoPqss84yba1yGhgnJyebtnYMHjzYtLVq4MCBcLvdGDhwoK167e3tpq1V48aNg6qqGDdunK16AHDs2DHT1qp169aZtlY57RmfOnUqioqKMHXqVFv1AOdXDjy/43YY6TB202L69u2Lr3zlKwzE40SfAUMx+Js3+S137N9PmO4n9cvFwG/cYOkYJz940lHbiGKJpWBcSqkKIb4C4Gv6rg+llF8Er1kUa7Zv327aWpWUlISWlhbbuc0AUFtba9padcEFF0BRFFxwwQW26vXr18+0tWr48OH4+OOPMXy4/XHSDQ0Npq1V11xzDR588EFcc801tuqNHTsW+/bts51OEcjKjU4/R6e5+E57xgPpUXf63Zk5cyaeeuop22lKRl57KAepUnh4SxvxlzJitcMjUiiKghOtKUg/33/KXcMnz0JRhoSgVUTWWQrGhRA/B3AjgFf0XSuEEE9IKe2NGqKo5PnH3EilMOZM7ekPt2e9ffv2ITk5GZs3b0ZxcbHfP/jGY8Y/ixUrVthuq9EjumbNGr/H9Ky3detWuN1uzJ49GwUFBZbrGT3i+/fvt/QaDcYc4a+++iruv/9+S6/T4JmKYYfT2TR2795t2lo1ceJE7Ny501Huv2f6jx1OZ/5wOk4hkCkxU1JSTFurPvjgg87tHXfcYbne+PHj4XK5bE+JSdHH5XJhw+eb0Se762pGa7t2grpZNf/+t1TZW8mXiHqH1TSVHwE4T0rZAABCiAcBfAyAwXicsZubamhqakJKSoqjVAynkpKS0NbWZjuFQwiBhIQEDB061H9hD0bqhtWlwo1AvrVVS6ZMS0uzFcQDwEUXXYQXX3wRF110ka22GnnJy5cvtxXENTc3m7ZWTZ8+HS+//LLtxWkAYPTo0Thy5AhGjx5tq152djYqKyuRnZ1tq96yZcugqiqWLVtmq4fQmBLTiWnTpuGTTz6xHcjbGaTqeeK4d+9eAFrqj93vHEWfPtl5yL/E/3ez4u3HQtAaIurOajAuAHgmibbr+ygOeP6TttNT7bReIDyPefXVV+Po0aN46623/A4a7Y3XaAxuW7lypaOcWqupJt56/1966SW/OfWe9YwThra2NlvB2NGjR01bq2012nnbbbdh8uTJtoK/zz77zLS1KisrC5WVlbanU3TaS+10kDLgfLyB0zQlQ2ZmpqN6RETUe6wG48sAfCqEMNbcvgLAU8FpElHvSExMRF5enu3AyCm7g9uMYPTyyy+Hy+XC888/j69+9auOjp2YmOionpVlzb0F8cY803Z7VJ0EfyNGjIDL5bK9WqjTlJp//OMfndv58+dbrud09h4A+Pjjj01bq+xcqfB2ovr666+H7PeDiIi8szqAs1QI8QGAImg94tdJKe0lcBKRV5mZmTj33HMtB+KeQdWVV16Jbdu24Y033vC7Cqdnvf/+7//Gtm3b8Morr2DChAm229y/f39bbb3qqquwZ88evPPOO7bTRpzmfo8cORK7du2yPfjX6Ywxn3zySefWbjB+9tlno6ysDGefbW+2WM8rHHaE+kSViIh88xuMCyESAGyWUp4FwN51YiIKqtTUVEyaNMlvIN5deno6pkyZYikQ9wzijaB61apVtoLq5ORkjB8/3nIdz974w4cPd27t9MY7nYVlzJgx+OKLLyzPwW3YvHmzaWvHzp07TVur+vbti+bmZvTt29f2MYmIKDL4DcallB1CiC+EEAVSSntL4FFEcTorCpHBblDdW8dsbW21NJ2i53fcmJe8rq7OVhA/aNAg09Yqpyt+AsA555yDt99+G+ecc46tek5POIiIIlFiSzVSK9/vvJ/QWg8A6EjOOK1cLLGaMz4UwJdCiHXQVuAEAEgpLwtKqyjonM6KQhQKvTH4NykpCa2trbaDarvTRRonABUVFQCAiooK2/n0dgaN+lpunLOiEFE0KywsPG2f8beusPD0dENv5aOV1WD8vqC2ghyxO3tDOGY3IQolb4MU33jjDVu50enp6aipqUF6erqjNlgdTOsZVBtzqL/33nu2gurExES0t7c7WhSLiCiSePubFy+xipWc8UQAC6SUM0LQHrKhrKwMpaWlGDlypO0V/4hinZ1Bip6BcWVlZefWzgJVxqDYl19+2fagWCMVx0pQzZNqIoo2gUz9Gg+s5Iy3CyHcQoh+UsqaUDSKrAlkxT8i6lmfPn1slbczKBZgUE1E8YOdhz2zmqbSBGCLEGI1zDnjtwWlVWRJICv+EVEXb9NFrly50vYsNUTB4GucgLHPOJnzxPEDFEmmTJmCoqIiTJkyJdxNiUhWg/G39BsRUUxzOl0kUbC4XC5s2LQZfbPzTftb2rVFu7YcOGna31xVEbK2EVmxfv16lJeXY/369ewZ98Lqoj/LhRCpAAqklDuC3CYiIiLy0Dc7H8p3f2KprPrm34LcGiJ7mFbbM/9rYQMQQlwKYBOAt/X75wghVgazYUREREQU/Yy0Wg7e9M5qmspCAOcC+AAApJSbhBCcSytMjPxBLtxDREREFN2sBuNtUsoaIYTnPhmE9pANXLiHiIiIKLpZDca3CiGuAZAohBgL4DYAHwWvWdQTo/eb06EREUUPp7OiEFFssxqM3wrgbgDNAP4O4B0A9werUURERLHGmBUldZB5VpTmDm341peHzLOiNJ7grChE8cDqbCpuaMG414RkIcRiKeWtvdkwIiKiWJM6KB+jLrf273Lv64uD3BpyStYfQ+vnL5j3NZ4CAIjUAaeVBYaGqmkUhaz2jPsztZeeJ254Xq7kQEwiIqLo4Ct1yOWq1h4f1z3wHorCwkKvKUpEQO8F4xQADsQkIiKKDr46y/yN4/I2JoAIYDAeNp6/zByISURERBSfeisYF/6LEBERRT+7s6Iw9ZCIemIpGBdCzJJSvtTDvj/1esuIiIgikMvlwsZNW5Ca431WlG0VVZ37Go9zRpRI1lF3FE0bnjfvc2sDMRPSBpjKAbmhbBrFEas943cBeMnXPinlM73YJiIioqDz1sPtb85vo4c7NScf46/8hd9jbH/50V5oaeipqoqWUzU49u8n/JZtOXUYqoi+sU++B2JqwXjhWM/gO5dzvlPQ9BiMCyG+DWAmgHwhxJ89HsoC0BbMhhEREQWTy+XCxi+2ID1nWOe+ZpkIANheecpUtuH4oZC2jYLP6UBMot7mr2e8EsAGAJcB2Oixvw7AL4PVKCIiolBIzxmGs66a57fc1hdLQ9CayKEoCqrlcQz+5k1+yx779xNQlJwQtIooNvUYjEspvwDwhRDi71LKVgAQQgwAMFxKeaqnukR2BXLJmIiIiCgaWc0ZXy2EuEwvvwnAcSHEf6SU/rsTiCxyuVz4cts2jBpzRue+Pn1TAACNLeasqL27d4a0bUQUmezObALwRJ6IIovVYLyflLJWCHEDgGVSynuFEJuD2TAKr0D+wQXSwz1qzBn4/Z/8Dxi68+ddl06dTjPGf+JEkcPp76OR950xeLjpMSP3e8fhatP++mMHe6vJ5IWqqmirrsbJD560VL6t+jBUtbFzBWrqfaLxBJJ2vW7e11wDAJB9+5nKAcNAoWc1GE8SQgwF8H0AjETigNFLPXrsONP+vimpAICm1nbT/j27dpxed0xX3b599Xot3ert3oFAuVwufPnlNoz06FEHunrV3c1dver7PHrUXS4Xtn65DSNHjzXVS9brNTS1mvbv27MLAHviiILB5XLhsy+2IHNIgWl/i/5vateRGtP+uqMHOn/OGDwcX73mdkvH+fzvDwfYUqLo4XvGmDr9cc/gexhnjAkTq8H4bwG8A6BcSrleCDEKwK7gNSt2RVNe9Oix41D6V2u9G/N+eoO57phxeGix/x7uX93qf3CQFSPHnIGSRx7zW+7uX95srjd6LBb9cYmlYyyYfwuAriB+hI8gvr5bEL9/T9evitPPn4uMUDzIHFKAc6/9X0tl1z37hyC3JjaoqormqhpUvO3/72NzVSVUNAR0PEVRUNWeioHfuMF/YQAnP3gSijJY61GvqUb1h8ss1Wur0XrUqWecMSY6WArG9cV9XvK4vxfAlcFqVCxzuVzYts2FseO6eo1TUrVe49b2DlPZXTsC7zVmEBccI0aPxcKH/map7MJf/aTzZyOQV0Z1BfJJfbQgvq7RHMSre3eZ6m35chsKRo4xlUnq0xcAUONu6dx3YN/uzp/Zi0+hxr85RET2WF2BMwXAjwCcCSDF2C+lvD5I7YppY8eNw5Klz/gtd8uNPwz4WFrKiAtjzvCebtLc1nUCsHtn4ME/+aeMGosFD/7Fb7lFd/zMdL9g5Bjc9bs/+yjd5Xd33db5s8vlwpatX2J4tyA+MVkL4qsbmk37D3oE8kROuFwufLZ5C/rlmtNNWvV/N3uOdaWb1Bw5AAouRVFQgxPIv+Rmv2Ur3n4MijIoBK06naIoONmWiv5fu85S+eoPl0FRBge5VUShYTVN5TkA2wFcDC1l5QcATu/6oIg05oxx+NOSp/yW+/ktPwpBayjUho8cg9tLrK0C+PDd2oqC7FGnQFLq+uUW4MLr7vJ7jI+W/a4XWkpEka6xsRHLly/H3LlzkapnA1AXq8H4GCnlLCHE5VLK5UKIv0PLIacQiaZcc4p+Ro96/ojRpv0Jeo96VX2TaX/F/j2dP/O7GhtcLhc+37wV/YZ29XC3Cu1fxt7jtaayNYfZw01EvpWVlaG0tBQjR47ExRdfHO7mRByrwbiRzFothDgLwBEAI4LSIvLKyDX3TDcxUk1a2sy55kw3od6QP2I0fv5ba6sO/uk3XUsOuFwubO4WyBtB/MkegnjqfYFe4eg3tABfv8H/idJ/niwJsKVEFGkSmqqQsu8t0z7Rop2Iyz5Zp5UFzKlpnqZNm4Z58+Zh2rRpvd7OWGA1GH9CX3nzHgArAWQA+E3QWhVFPP/ZqaoKAJ3zpfZ2j9+YM8bhL48/7bfcz34cnan8qqqivr7BNIe4L3t37URGRnoIWkVO5I8YjZ/e+5Dfcn+971edP3PgX+8zercH5JnncG7Te7j3n6gz7T9VqYasbWSdMSOK+qa1QePNJyugyvogt4pime8pEV36490D74Iep0VMTU3FzTf7H7cQr6zOpmLMb1cGYFTwmhPd3G53uJsQEYyg2sq0hXt27WBQTQC6etSHKuY/MSKpDwDgeF3XNGaH1b2dP0dTfnuoF6gCgAF5Cr51ywJL7Vu9ZJGlckQU2zglYmhZnU3lAQB/kFJW6/cHAJgvpbwnmI2LBp5fWH5JA6MoChpb2iyvwJnaR/v6qqqKuvqG0+YQ92bv7p3I1IN/o54xf7g/+/bsQmZGOleKC6Khyij8eIH/+aMfX9Q1F7URxOcWjDQX0oP4Y7Xmk+QjB/YF3E6nefEulwubtmzFoPwRpsc7EpIBAIequnozT1TsNz33ps1bkd2tXrte78BJcy9olUddin6KoqBWnITy3Z/4LwxAffNvUAoGBrlVRMHh+fe1+9/VWL0iajVN5dtSyl8bd6SUp4QQM6GlrVAMUlUV9Q0Npy3m48ueXTuQka4FuYqioKml3fKiPyl9EgNqa6gZQbzn/OE92a8H8Z51u09b6PU4e7vqUc9yC0biul9bm5lj2QPaLB+B9Ki7XC58sWUrcoZ3nQDIRC0wrqw2L5py/KA5+B+UPwJX/OI+v+187dF7Tfez80fgOz+710dps7f+4v/5iYgiXVpaWribEBJWg/FEIURfKWUzAAghUgH0DV6ziKxTFAXu5jbLK3Cm9U3qrNfQ1GprBc70lOTOsQGhpKoqauvqTXOI+3Jg7y5kZWaEoFXRTQuov8QQHz3qR2rMPepHu/Wo5wwfie/P95/W8c8/WksRIaI40HAccstL5n1N1do2pf9pZYH8kDQrksRiz7c/VoPxFQD+LYRYBkACuB7A8qC1KsJFU46q0cNtZQ7x3Tu3m3u3W9tR+tcn/dTSzPvpDUhJjq4ebqcURUF9U6utFTgzUpI769Y1tlpe9CczNTmgthpBvDF/uD8H9+1GTWZG3KTiDCkYieI7rM0EsuLB+PsHQUS9x/egSG2GksLx3QPv/B4HRVLssDqA8w9CiM0AZgAQABZJKeN2nnGXy4VtLhfOGDfetD8lVbuc0tYhTft37tgesrZRbFIUBTXuFssrcPZL6xOCVhERkVUcFEm+WO0ZB7QVN9uklGuEEGlCiEwpZZ3fWjHqjHHj8cRT1i4O3PSjuZ0/q6qKhoYGS0vd79qxHenpXbnG9Q0NlqYt3NWth7u5rcPyCpx9kxL8lqPooSgKqhuaba3A2T+9L1RVRU1dvWn+8J4c2r8HdUyNIeqRqqpoPFWDva8vtlS+8UQF1Pb6uLlSFYj22iNo+OTZzvsdDVUAgIT07NPKAUNC2TQKsu5TTHub2S4tLS1o0073BquzqdwI4CYA2QBGQ0tiegzAN4PXNCIiIqKeeUvlcLm0YLxwTPfAewhTP2KMy+XCp+s/Q3tyPyS0NUDIttPK1DS0ouLUHiS21oShhf5Z7Rn/KYBzAXwKAFLKXUKIwUFrVYTTerfdph7vnuzcvh3p6VoKi6IoaG3vwJKlz/itd8uNP0RyYkJnvZa2DsuL/vRhDzcFQFEUVNU32VqBMzsjBQA6e9U9F/TxpWL/HtSzR53ihKIoqE88iVGX32qp/N7XF0MZxikK/fHWyxmTqR/uE0hwvdJ1v0kPLFP6nVYOGBayZkWC9uR+cA+e6rdc2rG1IWiNfVaD8WYpZYsQAgAghEiCNpCTHNi1Y4cpTeXQwQMAgGHDC04rN2ECz+ApPhhBvOcc4r4cVvfCzSA+IqmqiprqWny0zP9UkzWHD0BtzPJbjijeee/9r9Mf6x54D4ur3n9VVZHYWmMp0E5srQnLjGj+WA3G/yOE+DWAVCHEtwD8BMAbwWtWZFMUBW0d0lbOeFKCdiLj7RekqVFbWdDoBTdMmFAYV79QgLYoz50/71q5s/LQQQBA3rDhp5U7c8KEkLaNrFEUBSfrm/DTex/yW/av9/0KA/UedadUVUV1bV3n/OH+HFb3ojErMyx5uKqq4lRN3WlziHtz4tB+tNdldtarqqmzPH/4yYr9kPVa3VM1dZZX1jxVqUI0dB2zuqYW/3nS/2wz1YdVqG4G1UTBEje9/07JNi0FRbbDe1+xAEQi4CWFJRJYDcbvBPAjAFsA/BjAKgDW5rwLISHEJQD+BCARwJNSyt8H61g7d2w/LU3l4AGth3t4QcFpZSfoQXUgv1C7d+4wDeD01aO+e2f4e9T37N6BX93qP6jes3tHZ1Dt7cSjpbkJADpX2zScOWGCqfy+3TtPW4HzcIV2zKH5w03lzjyTQXwkUhQFx+saLa/AmZOZGtDxjCDe6pSFRw/sRXNWV6B6qrbO0hzixw7uQ2tNZkBtjSaKoqAttQYXXuf/JJLuDAAAIABJREFU5OijZb+DMli7xK6qKuqqa7HuWf+fPwDUHT0AtTkroJMqVVXRcKoWW1/0n47VcOwQ1NbaznruUzXY/rL/gdHu44egtsXtXAdEQXfJJZfYHsAZafwG40KIRADLpZTFAJYGv0nO6O38K4BvATgEYL0QYqWUcpuvOocPH8akSZMAAA0NDejo6PBaLiEhAenp6fje976Hu+++2+cH2dSofQGMXnDDhMLAe7i91W9u0nrUu+eH91aP+p5dO05bgdNnUL2r56C6uVlra/fVNj2DaqcnKr5eqxHIG4v8AMCZZ3YL4vfswoL5t5jqHa48BAAYmme+9Ldvzy6cxUA+4iiKgtRat60VOAdnpYXlUqWiKEisqre8Auew7IzOeuJkva0VOAsGanXliTp86xZrCw+tXrIIyqDMzmO2H6/F12/wf7LynydLoOSEp2dcVVXUV9fi878/bKl8/bGDUFsicxAXEdkXaTOjOOE3GJdStgshcoQQfaSULaFolEPnAtgtpdwLAEKIFwBcDsBnMO5UOOYKDfUlKl8BrnEC0H2Bn94Iqp1y+nn4eo2tehCfnmJecOcsPZB3uVzYv2cXFv7qJ6bHj+hBfG63IH5/tyBe3bsLi+74md966l4G/8GkKAr61rhtLfqT269rIHZydYPlFTjz+qcH1NZ4oCgKWvrW4Nxr/Y8ZAIB1z/4BSm6/gE6qFEVBY/IpnHWV/yk8t75YCiVvQGe9hqQqjL/S/2Ja219+FEp+tt9yRBS/rKap7AewVgixEkCDsVNKaW2qhdDIB3DQ4/4hAOf1VGHogAHY8I9/2DvKzp2mu0uWLMGePXsAAA369vbLLgMAjB49GrfcYu519Sa3ttbrc/dGvdzaWuzavQe/L55l2n/08GEAwJChQzv3tezbB2XMaGDnTtw9y1zecPvttwMAHv7Nb7wf0EdbgvkandZ1+hqXLFmC3OHDgJZG035Roy1pPHqQefaDMcOHYXRODrBzJ6bm5Gh1W7vqJtTq9XLM9cZ61MutrUXtrt1Y/rPrTGVOHj8CABiYk9vVjkMqcseO6ayX1tiC1P17vb6W7vLq65DV3gwASHU3I81ivfz6OvTraOk8Zoq7Gemq/7r59XXo71GvT0MTMtR9luply9bOesn1jcg84L+eUXcgtLzBxPpGZB3Yb7lejmjrPGZCfSP6HfRfd1h9PQYntHfWQ4Mb/Q/5DyCHNdQjN6mjs15HgxsDKqwFnsMa6pGbrF3pa29oQLbFesMb6pHbR3Yes7WhHoMqD1ir1xed9Voa6jHosMV6taKzXpO7HjlHDvqtBwDD3Vrd3AEDsK8tEYX/dZWleq43n8LIAVmdx3S76zHk6CG/9Wrd9citTeysV+9uQO4x//Xc7gbk1iZ11qtzN2DosQpLbW1zNyC3Nlk/fgPyjlurB6OefsyaxgYMOeG/blJjA3Jr+3TWq25swMCTlX7rpTY2ILe2b2c9pbEB/S3UA4CMxobOv9VKYwOyqg5bqtfPqNfL/3MCqRst9cJ1TPLNajBeqd8SAERq8qPwsu+0LH4hxE3Q5kzHqLy8Xm1AampgOazBMHr0aK/7jRSOPh6DRseOGe2zPHXxdYLVGcQ/7Ptyube6Vur5+lyOtWoXqzJTu3rxM8eOMZU/ckjFU4+Ye3+rjh0FAGQPNs/Be+SQiqyxY322g4iIiHqXpWBcSnkfAAghMrW7sj6orXLmEADPROZh0E4gTKSUTwB4AgAmT54sccYZAR30lkceCag+ABzJ0nMtbbbFSj1f7TNSOB62mTYSzLb2Zr1wHDMSP8cB552HrKwsdB/Osq/qOAAgZcgg0/6sIYMwQE/FqUpognvEKAuvAKjIyERjRgpwxhk4kpWFkwlNaFD8163IyESTR73jIhn1ykhL9VoyUzvrHUMS6gr81zPqtmZp6SZHZBJqC0ZYrteeldZ5zMqORNQM91/3UEYGOrLSO+sdaktA9TD/gw4PpWcAWRld9VoTcCrf2mDFQ+kZSMjScsYPtghUWax3MD0DiVmZncc82AyMyiuwVC85K6urXpPE8KHW6vXxrOeWGJo73G89ADiYloEU/XfnQEMHBlqsdyAtA6kexzxQ346sIf7nZD6QloE0z3p1bUgbbKVeOjI869W2ImlwvsW2piNTf41qdSuQY62empaOLI9jqqda0DbIf92K1HT086i3a89+qJveNJVpqzsJAEjK7LqS19JUi/5ZI7qOl9qE+oHWOrtOpqYj23iNqU3onz3UTw1NdWo6Bupt9Yb/OyLvmOSb1RU4zwLwHLQVOCGEOAHgWinll0Fsm13rAYwVQowEUAFgNoBrwtsk3zyXbzW2RmAViUu1UnRymk9fXFyMiv178KffmHNpjx/Rzm9zcs3/aCv270H2WWcG2lwiihC+xtS4XCe0x0fkdO0ckRORM1QQRQuraSpPAJgnpXwfAIQQ34A2s8qFQWqXbVLKNiHEzwC8A21qw6cj7GTBp7S0tHA3gcjE1z/Wo61aPnl2t7nBs886k/+MiWJIOCYqIIpXVoPxdCMQBwAp5QdCiIibHkBKuQraHOgRjz3fFMn4j5iIiCg0rAbje4UQC6ClqgBAMQBrUxcQUdyp2L8Hf73vV533T+jpLYO8pLcMZHoLEQForzmC6g+Xmfc1aDnqiekDTysLDA5V04iCqsdgXAjxnJRyDoAPAYwA8Aq0WUv+A+C6HqoSUZzylq5yTE9vGdgtvWVgHKa3nKjYj9ceNS/eU6NPUdnPY4rKExX7MSz7rM77VRX78dZfzIsF1Z7Q6mUNyjXtr6rYj4KBZ4EoWvjOUdeC8cLR3QPvwXH3t4Nil7+e8UlCCAXAXADToQXixnSB3qYSJKI4F8iiT4fVvXh8kXnRl5N6r/pAj171w+pe5Hj0qB85sA/LHjAvv37yqDZf8cAh5tkZjhzYh8G90Bt//OA+/POPXStbVh/Tjtd/8NDTyuX11wJjX8FD3dFWAOhccVP7+azO8j4DlWNaPWO1TUPBwLM6F6giCrW2miM4+cGTpn3t9XoPd8bA08oCg5kaR3HNXzD+GIC3AYwCsMFjvxGUW5v3jIjID18B54lD2lzqOZld8/jnePSo+6p3skKrNzjLPEB6sF7X5XLh6IF9WPGgOQg4pQfVA7oF1UcP7EPuxDN9HrPmsBYYd19tM6//WT2uTgv4DzgCmRXnVKWK1UvMK4XW6T3qmd161E9VqhgxqKtHvebwAfznya456utPavPTZww0z09fc/gAkMOeeLLQwz2KPdxE3fUYjEsp/wzgz0KIJVJK/0tJEhE5FOpAtaSkxOv+U5VaEJ/bzxzE5048s8egOhJ78HwGRse1lSNHDDKv4TZiUM+98S59FcdROVnmB3LOYkAVwVqqKlHx9mOd91vrtOkJkzMHnVYOinmfXezhJrLP6qI/DMSJKKbEQ9AQyGsM5ISj5sgBfLTsd6Z9DSePAQDSBw42lcPgiT0+FwXG60mVS1v0q7B74K0M4kkVURhYnU2FiIjIL5+98Se13P/Rg/t17Rw8kcFfkEXLVRyieMZgnIiIek08XHEgIupNCeFuABERERFRvGLPeBQqKSmBy+XqnLbM6HEqLCzkyp5EREREUYTBeBRLS0vzX4iIKErUHT2Adc/+wbTPfUob+Jk2YPBpZZHLwZ9EFP0YjEch9n4TUazxOfDzlDbwc2xuP/MDuRO5sBERxQQG40REFHaBLGxUf+wgPv/7w6b9jXqPemq3HvX6YweBof0Dba5jjScqsPf1xaZ9zTXavN99+w06rez/b+/e46Qq7ryPfwqQm4ADqBhuA14QvCRGLkaZoEZExQvmUYywg0Tz4Ep2MXkmkuQJi6LsbHbFjPvEfYIPbCQGdk2WNRpUjIpJJENMAAW5CF6QjBqiojCIcQCFev44p7XPMNN9qvpypme+79erXzXT3b8+Vd1nen6nTlUd+kevWCkirY+ScRERKVnN9qjXB1dSPblx4v2ZssSWU2y2rnuDA4dhjRPv/r0/6f3fv+vP1D36o8jDB94PkviOPaJJ/P5df4aBSuJFSoWScckqNWEU0KRREWlRSmkpxXxfLXbLljCJb5x4D+yt9dtFSoiScXGiSaMiIsVVSgccIuJOybhkpZ5vERGRw+nMseSDkvE2RF8aIiIihaEzx+JLyXgbpS8NERH468432fTzmk9+31e/E4DOZccc9jz69vzk94adf2brg/8aec7+MLZTWmzDzj9Dv155r7e0DOrEknxQMt6G6EtDRORTTU1y3LLnLQCGpiXeAPTt+cnzm10V5f23g8fTk+9+vTSZUkQyUjIuLVJqSI3rcBoNxRGRuJr6PogzKVITKkUkn5SMS4uWy3CaLl268Ne//pWDBw/Svn37PNZKRETEnzqOJJ2ScSmY9C+bF198kQMHDjBp0iTat2+f9csm9VhDQwP3338/U6dOpUuXLlm3mf6ajzzyCN/61reYPHkyl19+eey66otRRESKRXO4RMm4FEX79u05cOAAe/bsoVev+JOZVqxYQU1NDf369cuaUOdLp06d2LVrF/v376dTp04F207qAMD1QEVEREqbvuMlnZJxKZj0LxvXHu58GD16NBUVFYwePTrrc9PreuONN/Lb3/6Wnj17smDBgoxx+ehRb9euHQcOHGD37t0cffTRWZ+vXnwREZHWo13SFZC2oUuXLtx0003OibhLQt3YmjVrqK2tZc2aNU5xFRUVkTKurl27Op1unDVrFkuWLOHmm28GYPLkySxZssQpmW7fvj179+5lz549TnUVERGRlkE949KipSfUF110kVPsmDFjqKqqYsyYMU5xPXv2jJSZpCfOu3bt4pZbbuGuu+5yGorz2GOPfVJed911Tts844wzAHjjjTd45JFHYm9TREREWgYl49Ki+SbUuRg+fDjl5eUMHz7cKe7uu++mtraWu+++m7lz58aO2759e6R00bFjRz788EM6duyY9bnpw1teeOEF9u/fz4QJE+jevbuGt4iIiCREw1SkRfMd3gKwcuVKampqWLlypVPc4sWLqaurY/HixU5xO3bsiJRxzZs3L1K6SCXhcZLxdPv37wc+HXMuIiIiyVDPuLRavr3qU6ZMYcWKFUyZMsUpbu7cuUydOtWpVxyCFWNS5bnnnusUe8455/Dwww9zzjnnZH1ues/30KFDOXToEO3atct6gRL1qIuIiBSOesZFGtm4cSN1dXVs3LjRKa5v37489dRT9O3b1ykul2EqN910E2VlZdx0001OcSeeeGKkjCvVo75161anOBEREWmaesal1UoNUxk8eLDT5M9ij1Pv0KFDpHSxcOFC6uvrWbhwId///vdjx5WXl/Pyyy9TXl6e9bnpPd9DhgwBwFqrS36LiIjkgZJxabWKnVT7rqX+7W9/m2uvvZZvf/vbztvctGlTpIwrtdyj67KPkj9aL15EREDDVKQV85386Tvx0zfuoYceYt++fTz00ENOcQDjxo2LlHHV19dHyriMMZFS8mP//v3s3buXP/3pT1mfW11dTWVlJZWVlbzwwgusXr2aCRMmUFlZSXV1deErKyLSijU0NHDvvffS0NBQtG2qZ1ykkZEjR1JRUcHIkSOd4nx74ocOHRopXaxfvz5SxjVw4EBef/11Bg4c6BRnrY2U2aR6fzds2MC+ffv48pe/zJFHHtnien6TmKTa1PCfd955h9ra2tivkb4qzqhRo/JbQRGRNsh3iGsulIyLNLJq1Spqa2tZtWoVl19+ecG3N378eHbu3Mn48eOdY8eMGcPvfvc75wOA3bt3R8pC27dvHwCbN2+OlTSmJ8cuiXx63OrVq4FgxZnjjz8+dlLtmuCmtpna3siRIzn55JOLmsQDGsMvIpLGd+hoEtc3UTIu0siBAwciZVy+R9Op4TQ+fNcZ79atG3v37qVbt25OcR06dODjjz+OPdk0lTjmkjS6JvKNvfvuuxx//PEZn5PPBHfPnj3OMSIikl9J/E/2pWRcJE98j6Z9j94BXn755UgZl2/P+McffxwpC8U3OU6i1zgfBxwikqzmJlS3tCF1El8SPdy+NIFTpBHf3uaGhgZWr17tPOnDd+InwKBBgyJlXKNHj46Ucfm+NyIipaJr16507do16WpIjnK5gnexqWdcpJGxY8dSVVXF2LFjneLmz59PbW0t8+fPd+pJ8Z0wCp9efMf1Ijzdu3ePlHH5DuGR5KX3/G3cuJGGhgauuuoqunTpot4/afO0/0uS1DMu0ojv0fQJJ5wQKeNKnzDq6p133omUcb3++uuRUtqW1Nkb16vMiohI/qlnXCRPjjjiiEhZDLNmzeLaa6917tV57733IqW0flqFRUSkZVLPuEie+I6nPvXUUykrK+PUU0913ubmzZupr69n8+bNTnGpFUpSZVzt27ePlCIiIpIbJeMieeI71nzevHnU19czb968AtXscH/zN38TKeMqKyuLlCIiIpIbDVMRSdjs2bN59dVXmT17tnOs7wFAap3wuOuFp/Tp04f33nuPPn36OMVJaWt8YaMxY8YwcOBATfwUEckD9YyL5InvEoV9+/blqaeeom/fvgWq2eEeeuihSBnXu+++GymlbXrrrbeSroKISKuhnnGRPMlliUJfy5cvp6amhmOOOYarrroqdpzvcJNOnTpFSmkbdGEjEZHCUc+4SJ6sWbOG2tpa1qxZU7Rt+q4zftZZZ0XKuI499thIKSIiIrlRMi6SJ76X3m1oaODee+91vnInwPXXX095eTnXX3+9U9yKFSsiZVxHHXVUpBQRaUmqq6uprKxky5YtbNmyhcrKSiorK6murk66aiLNUjIukie+FwtasWIFNTU1zokxBBdtqaurc754S2oZRdflFNeuXRspRURaIl3SXkqJxoyL5ElDQwP3338/U6dOdU7IffmOU582bRpPP/0006ZNc4rr0aMH77//Pj169HCK69y5M/v27aNz585OcSIiLrS6T/6lVlMCPikrKysBtKJSnqhnXCRPUpMply9f7hTnuzwh+I9TX7hwIfX19SxcuNApzndJxO7du0dKEREpPTrjUBjqGRfJE9/JlKnhLT58e8ZffPHFSBlX6kvY9ct4586dkdJFt27d+OCDD+jWrZtzbKkwxmCtxRhTlO21a9eOQ4cO0a6d+mNEJDP1fBeeknGRPJk+fTrbtm1j+vTpRdtmes/4RRddFDtuzpw5TJ48mTlz5jhtb8eOHZEyri5dutDQ0OA1fOeDDz6IlK2RtTZSFtqhQ4ciZaGkn95ev349Bw4c4PLLL+eoo47S6W0RkZC6RUQa8V3dpFevXtx333306tWrQDU7nO8KLsuWLePgwYMsW7bMKe7ss8+OlHGl3kufFWN8e+NLSfv27SNla3TgwAEAXnrppYRrIiLSsqhnXKSR1JU0Bw8e7NTbXEr27dsXKeMaNmwYjz/+OMOGDXOK6927N++99x69e/d2igP48MMPI2WhFXvICMDBgwcjZVwdO3bkwIEDdOzYsRDVyll6z7cuGCSSrOYmYuosVfLUMy7SiG9vcxJ8l0X8+OOPI2Vcjz76aKSMyzf5B/9Jo76KPWQE4LjjjouUcaV6m1OliEgcmojZsqhnXKSRXCZUlop33nknUsble9GfI444IlK66NGjB7t27XJeTtF3kqJvXC4TTS+99FJ+/OMfc+mllzrH+tAETpG2R73fLZe+iUXyJJcrafryXRZx4MCBkTKuioqKSBmX7zAM8L9Ake8kRd+e+Fwmmv7iF7+IlIVWrAmcIiKSnZJxkTxJjTVfuXJl0lXJavr06ZSXlzuv/LJq1apIGVdZWVmkdLFu3bpIWWi+vfipMeY+Y813794dKQsttapNsS5OJSIizVMyLpInvmt+58L3AOCZZ56hrq6OZ555xinOd73wPn36REoXqcmJrpMUfZNj32Q8NTzFZ5jKkUceGSnj8u3FP+WUUyJlXLkccKSGxGhojIhIlL4VRfJk1apV1NbWOvca58J3sumTTz4ZKePy7eG+/PLLI6UL3yTOdyKmb8K5f//+SOnCd+lH34m4GzZsiJRx5bLMpIbGiIg0TRM4RUqY72RT39U7fHtizzrrLMrKyjjrrLOc4qD464z7Jsa59Br36dOHv/zlL85nDnwnYh5xxBF89NFHzr3/xx57LNu3b+fYY491igPo3r07e/fupXv37s6xIvKp1BKF6csTAlqisISpZ1wkT3wnU+7YsYMLL7zQ+aqWufjc5z4XKePq27dvpIxrzpw51NfXO1/xE4q/1OCVV14ZKeMaMGBApHSRWs/edV1734sF+b6nb7zxRqQUkeRoecLWQz3jInni20s9d+5c6urqmDt3LvPnzy9AzQ7nm4z5jovOJYnzXaO8R48evP/++85LIqbG0buOp3/77bcjpYv01VS+973vxY7z7eH2Hfozffp07rnnHueJvwB79+6NlCLiR73frU9iPePGmHnGmK3GmA3GmIeMMWVpj/1vY8yrxpiXjDEXpd1/cXjfq8aY76bdP9gY80djzCvGmJ8bY1rm5ehEmjB79mzKy8uZPXu2c6zvcoovv/xypIxr4sSJdO7cmYkTJzrFvf/++5HSRf/+/SNlXL4TMU888cRIGVenTp0ipQvfgyPfbQ4aNChSxrVo0aJI6aLYw41EREpFksNUngJOs9Z+FngZ+N8AxphTgGuBU4GLgR8ZY9obY9oD/xe4BDgFmBQ+F+BfgLuttScBu4GvFbUlIjno27cvTz31lPPQD4Dly5dTU1PD8uXLneIGDx4cKeO655572LdvH/fcc49TXGqcsM944VTPtmsPt+8KJb6TG30PGgDKy8sjZVy+Y/jr6+sjZVy+q7BA8a+kKiJSKhL7VrTWpi/j8Afg6vDnCcDPrLX7ge3GmFeBUeFjr1prXwMwxvwMmGCM2QJ8CZgcPud+YA5QnPP9IgnatGnTJ+VVV10VO8532MiMGTNYtWoVM2bMcIrLpdd4zZo1kTIu3yS+X79+7Nmzh379+jnFDRo0iPXr1zv3NoN/ojpgwAB27tzpPE7d9wqsvmdUIKjr5s2bY9U1NUENYPPmzTQ0NPCVr3yFI444QpPUEpD+eaRPGtRnIZIfLWUC5w3A4+HP/YD0DOHN8L7m7u8N1FtrP250v0ird9ppp0XKuKZPn05FRYXz2N+lS5eyb98+li5d6hQ3dOjQSOliyJAhkTIu3zHcvksU+o5tB/jwww8jZVy+PdWjR4+OlHGdcMIJkdKF7zj1/fv3c+jQIbZv3+68Tck/TRoUyb+C9owbY1YATa2dNsta+8vwObOAj4H/SIU18XxL0wcONsPzm6vTjcCN4H4pcJGWZvz48ezcuZPx48c7xfXq1Yv77rvPeXu+l3zP5QqTc+bM4dprr3VeiaVbt27s3LnTebJp37592bZtm/OwoRdeeCFSupg4cSLV1dXOY/F//vOff1LeeuutseOOOuqoSBnX+vXrI6WL1157LVJmkt7bmjrQ/PDDD1myZInzdiV36v0WKayC9oxba8daa09r4pZKxKcClwF/Yz+dufQmkH4esz+wI8P97wJlxpgOje5vrk4LrLUjrLUjjjnmmHw0U6TNePfddyNlXKkhH65DPwAeeOAB9u3bxwMPPOAUd/LJJ0fKuHyHxQwbNixSurjmmmuoqqrimmuucYrz7an2PWvQu3fvSOnCd/32AwcOREoRkdYmydVULga+A1xhrU0/N7sMuNYY08kYMxg4CVgNrAFOCldO6UgwyXNZmMT/hk/HnE8FflmsdogkaeXKldTU1LBy5cqibG/cuHGRMq7PfvazkdLFtm3bImVcvkvp+U78fP311yOli4aGBlavXu2cqPoO/fA9OOrSpUukLAbfNoqIlIokv93+DegOPGWMWW+MuRfAWrsZ+C/gReBXwN9Zaw+GY8L/HngC2AL8V/hcCJL6qnCyZ2/gx8VtikgyRo4cSUVFBSNHjizK9q688kqqqqqcL4iTi507d0bKuEaNGhUp40q9l67v6dFHHx0pXdxzzz3U1tY6r1JTV1cXKePyPTjK5ZL2vrGnn356pBQRaW0SS8attSdaawdYa88IbzelPVZtrT3BWnuytfbxtPuXW2uHhI9Vp93/mrV2VPiaE8OVWERavTVr1lBbW+s8pMJ3fXJfvssFgv964atXr46Uhd7enDlzKCsr87rK6Mcffxwp45o7d26kjKtjx46RMi7fiabgv6LOK6+8EilFRFobnfcTKWFjxoyhqqqKMWPGOMX5rk/uG5fLFTgvueSSSBlX586dI2Vc6ctFuli3bh319fWsW7fOKQ74ZLKo66TRZcuWRcpCy2XIiG8bfddgFym2YndySOuhZFykDfJdFWPr1q2RMq6PPvooUrrwravvRExfuaw08uSTT0bKuL70pS9FykLr06dPpHRx++23Y4zh9ttvd4pz6Y2vrq6msrKSyspKXnzxRdavX8+kSZOorKykuro6a7xILoo9h0daDyXjIiXM98vf9yIzF1xwAcYYLrjggqJsD+Ccc86JlHH5rjTiu3Z7Lm2srq6mc+fOzgnjhAkTqKqqYsKECc7b9OG7tCXAwoULsdaycOFCpzhjTKSMq127dhw4cIBdu3Y5xYn48j1TKaJkXKSE+X75z5gxg4qKCucrac6aNQtrrfO6w7ksbfjmm29GyrjOO++8SBmX73jqK664gvbt23PFFVc4xUFwVcN9+/Z9cnXDQvNto29iDHDsscdGyrhcVnCZNWsWS5YsYcmSJXzxi18EggsiLVmyRGtlS8F16dKFm266qairDUnroGRcpIT5fvmnLvrTq1cvp7jJkydHyrh8J+8Bn1xe3vUy82vXro2UcfmONb/jjjs4ePAgd9xxh1Mc+I9T9z0zMnz4cMrLyxk+fLhT3JlnnhkpXfhehdV3xRjfz19EpNiUjItIbJdccgnl5eXOkyl9L9sO8Oqrr0bKuM4999xIGZfvWYNlD+5GAAAgAElEQVTLLrssUrrwHRrje2Zk4cKF1NXVOQ8ZmTlzJhUVFcycOdMpDvzH1KeuSup6ddLjjjsuUoqItFRKxkXaIN9Z/75JnO+wCPBf9i91Ud9PL+4bT5cuXRg1apTz2YaysrJI6eL888+noqKC888/3znWx759+yKlS9wbb7zhHAf+E2p9e8Z1sSARKRX6lhJpg3yHN/gmxkl4+umnI2Vcvu9NLivG+K4X71tX3/XCZ8+eTV1dHbNnz3aKy8XVV18dKeMqpX1VRNo2JeMibZDvlTvPOOOMSBnXgQMHIqUL30mDBw8ejJRxnXTSSZSVlXHSSSc5xW3bti1SuvAdbuL7OabWQnddE71Hjx6R0sU111wTKeO67bbbImVcvj3qIiLFpmRcpA1atWoVtbW1rFq1yilu/PjxVFVVMX78eKe41157LVK68J386TtmeN68edTX1zNv3jynuOnTp1NRUcH06dOd4nLh26P++c9/PlLG9f7770dKF7t3746UcfmOGfdto4gvXfRHfCkZF5HYGhoaWL16tfM/m+uvv57y8nKuv/565236Tqh87733ImVcs2fPpry83Hkohu8KNQArVqygpqaGFStWOMX59qj79nDPnTuX8vJy5s6d6xQHMGTIkEgZ11tvvRUp49qxY0ekFCk0XfRHfCkZF2mDxo4dS1VVFWPHjnWKmz9/PrW1tcyfP98p7tlnn6Wuro5nn33WKQ78k9yLL744UsbVuXNnBgwY4Ly0YS58L6bju7RlagiO61Ccnj17ctVVV9GzZ0+nOPCfxJtae911DXbfpRRFfOmiP+JLybhIG+SbxB1//PGRMq6tW7dGymIYN24cFRUVjBs3zinO94Ajl1PUL7/8cqQstDfeeCNSxpVLz5/vvIELL7wwUsaVy/h2ER+66I/4UjIuIrH59m5OmjSJsrIyJk2a5LzNXbt2ccMNNzhf1tx3PPWAAQMiZVy5JKq+Qzh8DwB8x7f7Tm4F/wOO5557LlLGNXHiRDp37uw81lySV11dTWVlJZWVlWzZsoUtW7Z88nt1dXXS1RPJOyXjIhKbbzK+dOlS6uvrWbp0qfM2Fy5cSG1trfPa5qeffjrl5eWcfvrpTnG+vca5nKLu1q1bpIzL9wDAd+jP3Llzqa+v9xoz3r9//0gZl+9qOnfeeSf79u3jzjvvdIqTzIo9SbFr16507dq1KNvKlSZwiq8OSVdAREqH71jziRMn8uCDD3r1Uk6ZMoUVK1YwZcoUp7jFixdTV1fH4sWL+c53vhM7bvr06Wzbts251zh1itrH6NGjqaioYPTo0U5xvgcADQ0N3H///UydOtXplLrvhZQAfv/7339Sfu1rX4sdd+utt3Lttddy6623Om3v0KFDkVLyI3UAOHjwYC666KKCbGPWrFkFed1CK8Z7I62TesZFpOBy6RnfuHEjdXV1bNy40Slu2rRpVFRUMG3aNKc4315j3+E04D+kxneMqu/qLd/4xjdo37493/jGN5zigE8OGFwPHNatW0d9fb3zmuiptddd12CXzDRJsXl6b8SXknERic13WIRvYgz+F7YpNt/hNODfxmKfFl+wYAEHDx5kwYIFzrG+Q3HWr18fKeP61a9+FSklPzRJUST/lIyLSGxJ9Pz49hrnkhz7yOWAw7eNvgdHvsNiZs6cSVlZGTNnznSKA//VVDp06BAp4+revXukFCk0rTMuvjRmXERi8x0Xnb5coOt4UN8DgGnTpvHSSy85J8e7du3illtu4a677nIaqpIa3uLDt42+cenJv8vY1s2bN1NfX8/mzZudl7f0XU1lypQpPPbYY85zBgYPHszzzz/P4MGDneKKpbq6+rA11CsrKwEYNmxYyY6bTtcW2phOw1TEl3rGRaTgcrkAi+9pcd+x37496kmspOD73vgmDX/9618jpYvTTjstUsa1aNEi6uvrWbRokVPcsGHDImVL1qVLFw4dOsTBgweTrkpWvvt5Ka2K4ktDeMSXesZFpODOP/98KioqOP/8851jfVf+8I3z7VFPTYrs168fl19+uVNsqazCsHnz5kjpYvz48ezcuZPx48c7xdXV1UXKuLZv3x4ps0n14harBzf9NZ944glmzJjBV7/61Rb9+YPbvtraer5FCkU94yJScL5josF/HKbviiG+Peq5KPbpbd/31Hf8di58r6Q5aNCgSBlXEr3UpTS8oZTqKlIq1DMuIgWXyz/wYq+m4tuj7rsGexJyGYdfW1vrNUl1+fLl1NTUcMwxx3DVVVfFjvPtjd+2bVukzCbVi/vII4/wrW99i8mTJzuf4WgLcllPX0Sapp5xESm4XMZS+vaq+ybHSayI4LtN3/G7vp/Hc889R11dnfOl6QE2bdoUKeOqqamhffv21NTUOMX5XqDo7bffjpTZNHfpdpfLtvuexZHC8P270hU4xZeScRFp0Xx7cYs9uTGXhMp3m8VO4n2XJwT/CZwbNmzg4MGDbNiwwSnulFNOiZRxLVu2LFK6aOmTFFMHDukHDa4HDm2B79+VljYUXxqmIiKx+Q7hyEWxT4v7bi+XlUZ8t5lrEu86YbRjx46R0oXvBM4HH3zwk3Lq1KnO23V16qmnsnXrVk499dRYz8/HJMViD3FqyQcMLUGxlxoVUTIuIrGVyqofSXjppZciZTEUO4nPJWn0revQoUPZunWr87KYr732WqSMa+bMmbz99tteFzbyPVgt1gGnVjeJx/fz0Hh68aVhKiISm28SV0pjKX3r6jsMIwm+Q3iSWEc51UMdt6c6ZdKkSZSVlTFp0qRCVKtJxR42JCKtg5JxkTao2BP/SmkspW9dx48fT1VVlfMwjCSU0gS1V199NVLG9cADD1BfX88DDzzgFJd+tVhXScw3EJHSp2EqIm1QsYebFHt5wlzkOmG0FPh+/rlc2MiX79rmvld97d+/f6R04bsPuEyMbWuXmBdpC9QzLtIGFXuiUS4X/Sm2tnBJ61KaaDZjxgwqKiqYMWOGU5zvmQrfnvhc+E6M9V29RcNiRFoW47oGa2syYsQIu3bt2qSrIdLqJbEKi+RfW/gcq6qqePTRR7nsssuc1zb3Vez3NXVhox/84Ae6sJFIkRhjnrPWjmjqMfWMi0jBtYXe5rYgic+x2OPbk1wVx/V9VQ+3SOugZFxERFos3wm1vpMihwwZEimLwTepfvjhh6mpqeHhhx92ihs9ejQVFRWMHj3aKU5ECkPJuIiItFjFHt9eVlYWKV34JtW5HHCkl3GtWrWK2tpaVq1a5RQnIoWhZFxEJA9yGTKg4QbN8x3CMXz4cMrLyxk+fLhT3JQpUygrK2PKlClOcQDLly+npqaG5cuXO8XlcsXH9FJESpOScRGRPMhlLfVSWoe9VCxatIi6ujoWLVrkFOe7PjnApk2bImVcvgccxphIGVcuV1IVkfxTMi4ikge5DKcopaUGS4XvOuMnnHBCpHRR7KuwPvPMM5EyLk2oFmlZlIyLiORBLglOqSRHpTScxned8SOPPDJSujj77LMpLy/n7LPPdo71ceGFF0ZKESlNSsZFpFUqpcSxVJTScBrfA5xchnAsXryYuro6Fi9e7BzrY8KECVRVVTFhwoSibE9ECkPJuIi0SqWUOJYKDafJbOLEiZSVlTFx4kSnON8Dx1I5oyIimSkZF5FWSYlj/rWF5C+Xg7ilS5dSX1/P0qVLi7ZNESl9xlqbdB0SM2LECLt27dqkqyEiIi1ELpem37VrF7fccgt33XUXvXr1Kso2RaQ0GGOes9aOaOox9YyLiEgsbWEc/u7du3nwwQfZvXu3c2yvXr247777nBJxaBtnHESkeUrGRUQklrYwnGLu3LnU1dUxd+7cpKtSMG3hoEqklCgZFxGRWNrCOPyZM2dSVlbGzJkzk65KwbSFgyqRUqJkXEREYmkLwynWrVtHfX0969atS7oqBdMWDqpESomScRERkZDvJe0hmMB5ww03sGvXrnxXS0RaMSXjIiIioVwuab9w4UJqa2tZuHBhvquVVxqmItKydEi6AiIiIi3F+PHj2blzJ+PHj3eOnTZtGi+99BLTpk0rQM3yR8NURFoWrTOudcZFREREpIC0zriIiEiBaclAEfGhZFxERCQPNBZbRHwoGRcREcmDUhmLrR58kZZFybiIiMSipfsyK5V12NWDL9KyKBkXEZFYSmXpvqSUSo9zqfTgi7QVSsZFRCSWadOmUVFR0eKX7kuKepxFxIeScRERiaVXr17cd9999OrVK+mqtEil0uO8YsUKampqWLFiRdJVERF00R8REZG8SI0Zb+kOHDgQKUUkWeoZFxERCZXKuO9cdOzYMVKKSLKUjIuIiIRyGfddKon82LFjqaqqYuzYsUlXRURQMi4iIvKJXMZ9l8oEzlJZglGkrVAyLiIisZRKz28ucklUS2UCp4i0LErGRUQkllLp+U2KepxFxEfiybgx5hZjjDXGHB3+bowxPzTGvGqM2WCMOTPtuVONMa+Et6lp9w83xmwMY35ojDFJtEVEpDVTz6+ISP4lmowbYwYAFwKvp919CXBSeLsRmB8+txdwG3AWMAq4zRjTM4yZHz43FXdxMeovIiIiIpKLpHvG7wa+Ddi0+yYAP7WBPwBlxpjPABcBT1lrd1lrdwNPAReHj/Ww1j5rrbXAT4Eri9sMEZHWT8NURETyL7Fk3BhzBfBna+0LjR7qB7yR9vub4X2Z7n+zifub2+6Nxpi1xpi1O3fuzKEFIiJti4apiIjkX0GvwGmMWQEc18RDs4DvAeOaCmviPutxf5OstQuABQAjRoxo9nkiIhJVKleYFBEpJQVNxq21TV5RwBhzOjAYeCGca9kfeN4YM4qgZ3tA2tP7AzvC+89rdP9vw/v7N/F8EREREZEWLZFhKtbajdbaY621g6y1gwgS6jOttW8By4DrwlVVvgDssdb+BXgCGGeM6RlO3BwHPBE+ttcY84VwFZXrgF8m0S4RERERERcF7Rn3tBwYD7wKfAhcD2Ct3WWMmQusCZ93h7V2V/jzdOAnQBfg8fAmIiIiItKimWABkrZpxIgRdu3atUlXQ0RERERaMWPMc9baEU09lvTShiIiIiIibZaScRERERGRhCgZFxERERFJiJJxEREREZGEKBkXEREREUmIknERERERkYQoGRcRERERSYiScRERERGRhCgZFxERERFJiJJxEREREZGEKBkXEREREUmIsdYmXYfEGGN2AnXNPHw08K7Hy5ZKXBLbVBvzH5fENtXG/MclsU21Mf9xSWxTbcx/XBLbVBvzH5fENjPFlVtrj2nyEWutbk3cgLWtOa6U6qo2tqxtqo1qo9rYcrapNqqNamPL2aZvnIapiIiIiIgkRMm4iIiIiEhClIw3b0Erj0tim2pj/uOS2KbamP+4JLapNuY/Loltqo35j0tim2pj/uOS2KZXXJuewCkiIiIikiT1jIuIiIiIJETJuIiIiIhIQpSM54kxppcxpmfS9cjGt56l0r5ctZV2ioiISMvQ5seMG2M6AF8Dvgz0BSywA/gl8GNr7UcZYgcCdwIXAPWAAXoAvwa+a639U4Hq3Afol6qrtfbtLM/3qmc+2uda11zjfGJzbWcudRVxZYw5CriYtH0OeMJaW58lbigwoVHcMmvtlgLFedWzxNroFZdQGw0wqlHcapslCUjoc/Staym1sdjvTVG3l+M2S2K/yaWuh72OknHzAEECdj/wZnh3f2Aq0Mta+5UMsc8C/wr8t7X2YHhfe2Ai8E1r7ReybNs1aTwDuBc4CvhzWl3rga9ba5/PZz1zaV8OdfWKy3Gbvu+Pd13D+FJJOJRUtZA2GmOuA24DniS6z10I3G6t/Wkzcd8BJgE/I/o9dy3wM2vtP+c5zqueJdZGr7iE2jgO+BHwSqPtnUjwXfVkPuuZYxt961pKbSz2e1PU7eW4zZLYb3Kpa5N8rhTUmm7ASxkeezlL7Cuej50B/AHYAqwIb1vD+87MELceOKuJ+78AvFCAeno9lmNdveISen9yqet1wDZgPvAP4e3e8L7rMsR9J9zud4HK8Pbd1H0FiPOqZ4m10SsuoTa+BJQ1cX9PMnxfAS8DRzRxf8cs+7hvnFc9S6yNXnEJtXELMKiJ+wcDW1rY5+hb11JqY7Hfm6Jur8Tem6K3salbB2S3MWYi8KC19hCAMaYdQa/o7iyxzxljfkTQq/5GeN8Agl71dRnifgL8rbX2j+l3GmO+ACwCPtdM3JGNYwCstX8wxhxZgHr6xuVSV9+4XGJ925lLXWcBw22j3tNwvPofgeaOqL8GnGobDZ8yxtQAm4HmeuN843zrmUtssdvoGwfFb6Mh6EVv7FD4WHMOEQzDq2t0/2fCx/Id51vPXGKL3UbfOCh+GzvwaU96uj8DR2SIS+Jz9K1rKbWx2O9NsbeXyzZLZb+B3PadwyrR1l0L/AvwI2NMKvkuA34TPpbJdQT/VG8nONVsCJK5R4AfZ4jzTeIeN8Y8RvAPPj1pvA74VQHq6RuXS11943KJ9W1nLnUtlYRDSVVmxW5jNfC8MeZJPt3nBhKcFp2bIe6bwNPGmFcaxZ0I/H0B4nzrmUtssdvoGwfFb+N9wBpjzM+IflddS+bvuCQ+R9+6llIbi/3eFHt7uWyzVPabXOp6mDY/ZjydMaY3wXvyboG380PgBJpO4rZba5v9UjXGXMKn40wNwRHdMmvt8kLW2YdvXXNpY7HfnxzaOBW4lWCs2WF/xNbanzQTdzHwbwTj2w77Z2ytbfIgIIc4r3qWWBu94pJoYxjbE7iI6D73hLU245m88IzfqEZxa2w4T6IAcV71LLE2esUl1MZhNP1d9WIh6pljG33rWkptLPZ7U9Tt5bjNkthvcqnrYa+jZLx5xpjjrLVvecZeZq19NMPjLSKpzlbPfMeVmkK2s4QSDiVVLaiNIiLSyrgMMG9rN+CxHGJvL3JdbyxmPXNpXw519YpL6P3xrqtuuvncgAWecY8WOc6rniXWRq+4hNo4p4Q+R9+6llIbi/3eFHV7JfbeFK2NuuhPBtbaS7M9xxgz1BjzHWPMD40x/yf8eZi19jafbRpjbvSJI8v4XWPMKGPMyPDnU4wxVcaY8a71NMb8FMC3famXKXJcxlhjTEdjzHXGmLHh75ONMf9mjPk74B/zvb2sgcYs8Izz6sHPIc6rnrnEJtBG77MixW4j8P8846YVOc63nrnEFruNvnFQ/DY+5xmXxOfoW9dSamOx35tiby+XbZbKfgOOddUwlQyMMd2stR9keNx7ndkMr/m31tpmP0QTrE3cD/hjet2MMRfb5sfE3gZcQjBh9yngLOC3wFiC0+nVzcQta3wXcD7BxXCw1l4Rs1kYYyoITslvspnX7TyLYDmh940xXQiWezsTeBH4J2vtngyxNwMPWWvfaO45zcT9B8F705VgjfBuwC8ILgKEtfarGWJPILhg1ADgY4IxwA9kqmeM+gy31jp/CRhjPmOt/UsR47zqmUtsAm30igtji9rG1swYc6y19p0ibq+3tfa9Ym1PRNo43y74tnADXs/yuPc6sxle8/oMj91MsK7lw8CfgAlpjz2fIW4j0J4g2Xwf6BHe3wXYkCHueWAJcB5wblj+Jfz53CztWJ328zSC9ZNvA1aReR3lzUCH8OcFBBfjqQhjf5Flm3sILpzyO+DrwDEx3/MNYdkBeBtoH/5usrw/NxMc3PwD8HuCCwdUExw4nJf0/tsSb8CxRd5e76TbnOf2HEWw7OFW4L3wtiW877D1bmO+5uMZHusBfB9YDExu9NiPMsQdR7D2+v8FegNzwu+h/wI+k6U+vRrdeoffdz0JLsTWXNzFjd6nHwMbgP8E+mSI+2fg6PDnEcBrwKsEK92cmyHu+fBv/wSP93wEwYpdSwgO5J8Kv7/WAJ/PENcNuCP8ntwD7CS4PsVXs2yvA/C3BKs8bQBeAB4HbqKJ/2Ex25DxNDzB/5y/JVhVYnSjx/4hQ1xX4NvATKAz8FVgGcFVkrs51jHrWs/AZ9N+PiL8TJcB/wR0zRL792n7zonASoIOnT8Cp2eI+wXBtQVc23M8weof/xjuCwuBTcBSmlgrOy2uHXAD8Fj42T9H0Il4XrH3m2z7TqnsN7nuO4e9lu+b2VpuQFUzt28Bu7LEbgXKm7i/nAwXE8ryms0eABD8M+sW/jwIWAt8I/x9XYa4dU39HP6+PkNcO+B/EfyjOCO877WY7Ujf5hrCxBg4EtiYIW5L2s/Px61rapthnccR/CPeGX6JTAW6Z4jbRHAA1RPYS/gPP/yDznSxgI18mrh3BX4b/jww0+cRPkdJVfNxSqqaj3uC4IJBxzX6jL4DPJUh7sxmbsOBv2SIezB8X68k+CfzINAp9b5liPsVMIPgzNaGsH4Dw/t+maWNh4DtjW4fhWWz3z/p9QH+nSBhKSf4Dns4Q9zGtJ9/A4wMfx4CrM0Qtx24C3gdWB1up2/M/WY1wdnKSQSr6Vwd3n8B8GyGuF8SJBn9Cf5PzQZOIrhGwj9liHuA4O/4C2Fs//Dn+cDPHf6G0/+W38zSxn8n+Jv9JkHyV9PUZ9VE3H8BPyDo3HiaYNWhMcA8YHGGuL0EnU3vhz/vBQ6m7o+53/yA4Dog5wJ3Az/N0sbNaT8/Bnw5/Pk8YFWGuD8D/w3sCtv7ZaBjjP1mJTCd4O9qE0GeMoBgad5fZ4hbRPDdXUHQwXUHwYpPK4AZ+d5vctl3SmW/yXXfOey1XJ7cGm/APoIjsNuauNVnib2Y4J/94wS9uAsI/gm9SlpC0UTchmZuG4H9GeJebPR7t3B7NWROqv9IeJQGtEu7/6hMO3fa8/oTHHn/G1nOFqTFvECQdPWm0T80Mh84LCU8OxB+gYwIfx5CsNJErD+M8PcjgCvCL5SdGeL+F0HiVkfQ2/00QY/DRuC2DHEb+TQx6Qk8l/bYpix1VVIV43NESVXjuExXDM702EGC4WW/aeLWkCFufaPfZxGc3eqdZb9JPxh/PdNrNhF7S7jfnZ523/YYn8XzzW0j0zYJDohTZ+P+0Nw+lWV7XyRIAt4K39OME7izvD+Zvh9faPT7mrBsB2z13DcyXdXwIMF3Y/rfcOr3A1nauCHt5w4E/x9/AXTK0sb1YWnC99Ok/Z7pTOU9BMsF90m7L85+k/5ZrCfs8c22vcbvK43+P2Wp67qw7A5MAZYTHJAvAsYVYL/Z0Oj3P4RlJ7JcudNnv8ll3ymV/SbXfeew13J5cmu8EQwvGN7MY2/EiG9HcKR4FXB1+HP7LDFvA2cQJBjpt0HAjgxxvybsoU67r0O4Ix3MENepmfuPJsOptCaefykZEoVGz/1T2h/ea4RJJ8EBRKZ/jEcRHF1uIziI+CiMfwb4XJZtZvpD7ZIlti9hAkZw0aergVFZYr5BkKAuIPiHnjqIOAZYmSVWSVXzcUqqmo97kuBUbPo/jj4EB0krMsRtAk5q5rFmv+cIzta0a3TfVIIe/bo47QP+Me5nkfacVAdADUHCkvWMHMG8ndRZzdcI/xmHj2X6ZzwjfF+/RNB7+K8EPWq3k7lH7bC/G4JT7BcDi7LU9VmCM3gTCToBrgzvP5fMB46/ByrCny8nmPOTeizT98Yfwm2ld8a0A75CMP+oubhXgIGu+034+GH7MZ8OVWx2GGf63zhwX3P7VTOxwwm+H28O2xdnv3kN+B8E/8O3OG6vmuD/1fHA9wh6cwcC15NhdZtm9p1eBMM/MvVwP0fQuTAKeJdPO6tOzLKPP0d45o+g82Zl2mMvZojz2m9y2XdKZb9J23e+7LPvHPZaLk9ujTfgZMJT22n3pRLHZk+J57jNH6e+UJt47D8zxPUnrSe10WOjc61Xkd7vrsDgGM/rDnwu/COJ9TkAQxJoz6kEiftQxzglVc3HKKlqPq4nwRWDtwK7CU5zbwnvyzT052rg5GYeuzJD3J3A2Cbuv5jM/xjvoIlxmgRJw39n2wfSnn85QULwVozn3tbolhoadxzZhxucB/ycYKjbRoKeyhvJMC6WYJJ+rHY0Efs5grNjjwNDgf9DMNZ4M3BOlrjV4XNrU58pQQfAzRniBoXte4dgrtPL4c8/J8P3MfB3NNMJQobhDeHjS2jiDDHwP4GPMsT9ezP7zglAbYz3th1BUvU7MnRupT1/UaNbn7T95ukY8V8l6Dh6l2Bow4sEY4aPyhCTsbMmQ9wFBPPGthAMOXmQIOl9h7Q5ZE3EfYngzN/LBB1kZ6XtN3fG2G92hrGpbWXcb3LZd0plvwljfpLLvhN5LZ8dorXfiDF0QzfdcrkRTap2EU2qemaIU1KVOfY8mk6qOmSISSKp+izRpGpIeH/GpCp8zlCClZC6Nbq/2aFxaXEX5DHukkJsr3EswUTz0xJqY0HiwucM89zmMM/P/yyCHtXeBIncLcD4GPUcxadDvk4hOFDOGpdLbDNxl5J2YB4j7osEV8eNs72z8tTGUwk6EQr2vjaq66kOn+PZvm0MY3oTnE1fEjemiddwGkedr7hs+02jmM8A7xW7jVrasAnGmHXW2s8nXQ9pm4wx11trF7W2uHCpyhOstZtael1zjSvUNsPlO/+O4MDtDIIJ3L8MH3veWntmnuNmEKwYUZS4hOpa1O2lbfPrBAfjrnX1ibuN6PK2owiG/mVb3rZxXKxlcXOJzWOcbxtjxeV5my2yjU0sbwxBL3vW5Y19l0ZuAXHg38bYsYfxzf5b8w34etJ10K3t3og5SbZU40qpri2tjfivqFQScaVU1xJso8/ytl5xSWxTbSxIXC7LG6/ziU0gruhtbOrWATmMtfZHSddBWjdjzIbmHiIYO17ScUlssy20kWBy+AcA1to/GWPOA/7bGFNO5qu+lkpcKdW1lNr4sbX2IPChMWabtfb98DUajDGHChCXxDbVxvzHjSBYqGAWMNNau94Y02CtfSZDTMpwz9hixyXRxsMoGRdJRh/gIoJJeOkMweS+Uo8rpbqWUhvfMsacYa1dDx6b9noAAAOtSURBVGCt/cAYcxnBhUBObwVxpVTXUmrjAWNMV2vthwQJBADGmKMIliDNd1wS21Qb8xxnrT0E3G2MWRqWbxMzb/SNLZW4XGObejHddNOtyDf8V9QpibhSqmuJtdFrRaVSiSulupZYG72Wt/WNS2KbamNh2tjo+bGXN85XbKnE5RqrCZwiIiIiIglpl3QFRERERETaKiXjIiIiIiIJUTIuIlJijDFlxpivJ12PbIwx3zTGdE26HiIiLZmScRGR0lNGcPGXRJlApv8j3yRY29jlNbXKl4i0KUrGRURKzz8DJxhj1htj5hljZhpj1hhjNhhjbgcwxgwyxmw1xvy7MWaTMeY/jDFjjTGrjDGvGGNGhc+bY4xZbIz5dXj/tNRGMrzuFmPMjwgumDHAGDPfGLPWGLM57Xk3A32B3xhjfhPe90Haa19tjPlJ+PNPjDE14fP+xRhzpDHmvnDb64wxE4rwnoqIJEI9ECIipee7wGnW2jOMMeOAqwkucW2AZcaYMcDrwInAROBGYA0wGagArgC+B1wZvt5ngS8ARwLrjDGPAacBJzXzuicD11trvw5gjJllrd1ljGkPPG2M+ay19ofGmCrgfGvtuzHaNAQYa609aIz5J+DX1tobjDFlwGpjzApr7V9zeM9ERFokJeMiIqVtXHhbF/7ejSCJfh3Ybq3dCGCM2Qw8ba21xpiNBJdTT/mltbYBaAh7p0cRJO3NvW6dtfYPafHXGGNuJPif8hngFKC5q4w2Z6kNrhKYatMVxphbwt87AwOBLY6vKSLS4ikZFxEpbQb4vrX2/0XuNGYQsD/trkNpvx8i+v3f+IITNsvr/jXt98HALcBIa+3ucOhJ52bqmr6dxs9J7/U2wFXW2peaeR0RkVZDY8ZFRErPXqB7+PMTwA3GmG4Axph+xphjHV9vgjGmszGmN3AewZCWuK/bgyCR3mOM6QNc0kw9Ad42xgwLJ31+OUN9ngBmGGNMuO3PO7ZHRKRkqGdcRKTEWGvfCydibgIeB/4TeDbMXT8AKoGDGV6isdXAYwRDQeZaa3cAO4wxw7K9rrX2BWPMOmAz8BqwKu3hBcDjxpi/WGvPJxjr/ijwBrCJYOhLU+YC/wpsCBPyPwGXObRHRKRkGGsbn50UEZG2whgzB/jAWntX0nUREWmLNExFRERERCQh6hkXEREREUmIesZFRERERBKiZFxEREREJCFKxkVEREREEqJkXEREREQkIUrGRUREREQSomRcRERERCQh/x/GhNZMIWbkagAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_forecast_near24hour_1instance_with_demand_temperature = pd.merge(df_forecast_near24hour_1instance_with_demand, df_temperature, left_on = \"date_time_forecast_rounded\", right_on = \"date_time\")\n", + "df_forecast_near24hour_1instance_with_demand_temperature[\"forecast_error_relative\"] = df_forecast_near24hour_1instance_with_demand_temperature.forecast_error/df_forecast_near24hour_1instance_with_demand_temperature.total_demand\n", + "\n", + "df_plot = df_forecast_near24hour_1instance_with_demand_temperature[[\"temperature\", \"forecast_error\", \"forecast_error_relative\"]].copy()\n", + "df_plot.temperature = df_plot.temperature.round()\n", + "\n", + "plt.figure(figsize = (12,7))\n", + "sns.boxplot(data=df_plot, x=\"temperature\", y=\"forecast_error\", palette = 'Blues', fliersize = 1)\n", + "plt.axhline(0, color='r', alpha = 0.2)\n", + "plt.xticks(rotation = 90);\n", + "plt.title(\"Accuracy of forecasting 24h into the future\")\n", + "\n", + "plt.figure(figsize = (12,7))\n", + "sns.boxplot(data=df_plot, x=\"temperature\", y=\"forecast_error_relative\", palette = 'Blues', fliersize = 1)\n", + "plt.axhline(0, color='r', alpha = 0.2)\n", + "plt.xticks(rotation = 90);\n", + "plt.ylabel(\"Forecast Error as Portion of Actual Demand\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/W2 Initial Exploration.ipynb b/src/W2 Initial Exploration.ipynb new file mode 100644 index 000000000..34cbce456 --- /dev/null +++ b/src/W2 Initial Exploration.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "pd.options.mode.chained_assignment = None " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "varname_to_hr = {\"forecast_demand_24h\": 24,\n", + " \"forecast_demand_18h\": 18,\n", + " \"forecast_demand_12h\": 12,\n", + " \"forecast_demand_6h\": 6}\n", + "\n", + "df_forecast_interval = pd.read_csv('data/forecast_intervals.csv')\n", + "df_forecast_interval.date_time_future = pd.to_datetime(df_forecast_interval.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_temperature = pd.read_csv('data/temperature_nsw.csv', names = ['location', 'date_time', 'temperature'], skiprows = 1)\n", + "df_temperature.date_time = pd.to_datetime(df_temperature.date_time, format = \"%d/%m/%Y %H:%M\")\n", + "\n", + "df_demand = pd.read_csv('data/totaldemand_nsw.csv', names = ['date_time', 'total_demand', 'region_id'], skiprows = 1)\n", + "df_demand.date_time = pd.to_datetime(df_demand.date_time, format = \"%d/%m/%Y %H:%M\")\n", + "\n", + "df_forecast_interval_long = df_forecast_interval.copy().melt(id_vars = \"date_time_future\", var_name = \"foreceast_interval\", value_name = \"demand_forecast\")\n", + "df_forecast_interval_long.foreceast_interval = df_forecast_interval_long.foreceast_interval.map(varname_to_hr)\n", + "df_forecast_interval_long[\"foreceast_interval_dt\"] = pd.to_timedelta(df_forecast_interval_long.foreceast_interval,'h')\n", + "df_forecast_interval_long[\"date_time_current\"] = df_forecast_interval_long.date_time_future - df_forecast_interval_long.foreceast_interval_dt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_forecast = pd.merge(df_forecast_interval_long, df_temperature, left_on = \"date_time_current\", right_on = \"date_time\")\n", + "df_forecast = df_forecast.drop(columns = [\"date_time\", \"location\"]).rename({\"temperature\": \"temperature_current\"}, axis = 1)\n", + "\n", + "df_forecast = pd.merge(df_forecast, df_temperature, left_on = \"date_time_future\", right_on = \"date_time\")\n", + "df_forecast = df_forecast.drop(columns = [\"date_time\", \"location\"]).rename({\"temperature\": \"temperature_future\"}, axis = 1)\n", + "\n", + "df_forecast = pd.merge(df_forecast, df_demand, left_on = \"date_time_future\", right_on = \"date_time\")\n", + "df_forecast = df_forecast.drop(columns = [\"date_time\", \"region_id\"])\n", + "\n", + "df_forecast[\"demand_error\"] = df_forecast.total_demand - df_forecast.demand_forecast\n", + "df_forecast[\"demand_error_relative\"] = (df_forecast.total_demand - df_forecast.demand_forecast)/df_forecast.total_demand" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "temp_cutoff_min = 20\n", + "\n", + "for forecast_interval in set(df_forecast.foreceast_interval):\n", + " df_plot = df_forecast.loc[df_forecast.foreceast_interval == forecast_interval]\n", + " df_plot.temperature_current, df_plot.temperature_future = df_plot.temperature_current.round(0), df_plot.temperature_future.round(0)\n", + "\n", + " plt.figure(figsize = (10,12))\n", + " plt.subplot(2,1,1)\n", + " sns.boxplot(data=df_plot, x=\"temperature_current\", y=\"demand_error_relative\", palette = 'Blues', fliersize = 1)\n", + " plt.axhline(0, color='r', alpha = 0.2)\n", + " plt.xticks(rotation = 90);\n", + " plt.title(\"Error for {}h forecasts\".format(forecast_interval))\n", + " plt.xlim(temp_cutoff_min, 45)\n", + "\n", + " plt.subplot(2,1,2)\n", + " sns.boxplot(data=df_plot, x=\"temperature_future\", y=\"demand_error_relative\", palette = 'Greens', fliersize = 1)\n", + " plt.axhline(0, color='r', alpha = 0.2)\n", + " plt.xticks(rotation = 90);\n", + " plt.xlim(temp_cutoff_min, 45)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/W3 Combining data.ipynb b/src/W3 Combining data.ipynb new file mode 100644 index 000000000..7d6548233 --- /dev/null +++ b/src/W3 Combining data.ipynb @@ -0,0 +1,639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Goal: Generate Generic code that merges/joins data from csvs into dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.options.mode.chained_assignment = None " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Forecast Data\n", + "## Reading data and formating data-time columns\n", + "df_forecast = pd.read_csv('data/forecastdemand_nsw.csv', names = ['id', 'region_id', 'period_id', 'forecast_demand', 'date_time_current', 'date_time_future'], skiprows = 1)\n", + "df_forecast.date_time_current = pd.to_datetime(df_forecast.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_forecast.date_time_future = pd.to_datetime(df_forecast.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "## Using 'period_id' to round 'current timee'\n", + "df_forecast[\"date_time_current_rounded\"] = df_forecast.period_id.apply(lambda x: pd.Timedelta(hours = x/2))\n", + "df_forecast.date_time_current_rounded = df_forecast.date_time_future - df_forecast.date_time_current_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Demand Data\n", + "## Reading data and formating data-time columns\n", + "df_demand = pd.read_csv('data/totaldemand_nsw.csv', names = ['date_time', 'total_demand', 'region_id'], skiprows = 1)\n", + "df_demand.date_time = pd.to_datetime(df_demand.date_time, format = \"%d/%m/%Y %H:%M\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Temperature Data\n", + "## Reading data and formating data-time columns\n", + "df_temperature = pd.read_csv('data/temperature_nsw.csv', names = ['location', 'date_time', 'temperature'], skiprows = 1)\n", + "df_temperature.date_time = pd.to_datetime(df_temperature.date_time, format = \"%d/%m/%Y %H:%M\")\n", + "\n", + "# Setting time intervals between data as 30minutes\n", + "df_temperature[\"date_time_30m\"] = df_temperature.date_time.dt.round('30T')\n", + "df_temperature[\"date_time_30m_interval\"] = abs(df_temperature.date_time - df_temperature.date_time_30m)\n", + "df_temperature = df_temperature.loc[df_temperature.groupby(\"date_time_30m\")[\"date_time_30m_interval\"].idxmin()]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Forecast temperature data\n", + "df_weather_forecast = pd.read_csv('data/forecast_temperatre.csv')\n", + "\n", + "df_weather_forecast = df_weather_forecast.rename({'forecast dt iso': 'date_time_current_utc', \n", + " 'slice dt iso': 'date_time_future_utc',\n", + " 'temperature': 'temperature_future_forecast',\n", + " 'humidity': 'humidity_future_forecast',\n", + " 'dew_point': 'dew_point_future_forecast',\n", + " 'wind_speed': 'wind_speed_future_forecast'}, axis = 1)\n", + "\n", + "df_weather_forecast[\"date_time_current_rounded\"] = pd.to_datetime(df_weather_forecast.date_time_current_utc, format = \"%Y-%m-%d %H:%M:%S +0000 UTC\") + pd.Timedelta(hours = 10)\n", + "df_weather_forecast[\"date_time_future\"] = pd.to_datetime(df_weather_forecast.date_time_future_utc, format = \"%Y-%m-%d %H:%M:%S +0000 UTC\") + pd.Timedelta(hours = 10)\n", + "\n", + "df_weather_forecast = df_weather_forecast[['date_time_current_rounded', 'date_time_future', 'temperature_future_forecast', 'humidity_future_forecast', 'dew_point_future_forecast', 'wind_speed_future_forecast']]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#Merging Datasets\n", + "df_all = pd.merge(df_forecast, df_demand[[\"date_time\", \"total_demand\"]], left_on = \"date_time_future\", right_on = \"date_time\").drop(columns = \"date_time\")\n", + "\n", + "df_all = pd.merge(df_all, df_temperature[[\"date_time_30m\", \"temperature\"]], left_on = \"date_time_future\", right_on = \"date_time_30m\")\n", + "df_all = df_all.drop(columns = [\"date_time_30m\", \"region_id\"]).rename({\"temperature\": \"temperature_future\"}, axis = 1)\n", + "\n", + "df_all = pd.merge(df_all, df_temperature[[\"date_time_30m\", \"temperature\"]], left_on = \"date_time_current_rounded\", right_on = \"date_time_30m\")\n", + "df_all = df_all.drop(columns = \"date_time_30m\").rename({\"temperature\": \"temperature_current\"}, axis = 1)\n", + "\n", + "df_all = pd.merge(df_all, df_weather_forecast, on = [\"date_time_current_rounded\", \"date_time_future\"], how = 'left')\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df_all.to_csv(\"data/combined_data.csv\", index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idperiod_idforecast_demanddate_time_currentdate_time_futuredate_time_current_roundedtotal_demandtemperature_futuretemperature_currenttemperature_future_forecasthumidity_future_forecastdew_point_future_forecastwind_speed_future_forecastforecast_interval
108340542021030937428679.122021-03-09 22:01:432021-03-10 19:00:002021-03-09 22:00:008589.6822.421.920.5969.3014.803.510 days 21:00:00
99278422020040325626711.892020-04-03 16:01:272020-04-04 23:00:002020-04-03 16:00:006567.7114.323.314.6360.096.841.211 days 07:00:00
100469762020051813189745.082020-05-18 10:00:552020-05-18 19:00:002020-05-18 10:00:009833.5215.616.314.4571.709.350.660 days 09:00:00
99411102020040825107812.372020-04-08 16:01:402020-04-08 21:00:002020-04-08 16:00:007491.5117.518.515.1688.2513.182.160 days 05:00:00
86373372018120601187833.572018-12-06 04:01:062018-12-06 13:00:002018-12-06 04:00:008057.1724.917.324.3747.9512.735.320 days 09:00:00
91360072019061113108010.472019-06-11 10:00:592019-06-11 15:00:002019-06-11 10:00:007713.2120.217.520.1152.4510.053.250 days 05:00:00
89955792019041925725674.612019-04-19 16:01:382019-04-21 04:00:002019-04-19 16:00:005706.9715.723.317.8694.8016.951.291 days 12:00:00
10826680202103070166315.222021-03-07 04:01:302021-03-07 07:00:002021-03-07 04:00:006317.8215.915.917.1277.8013.190.940 days 03:00:00
81724682018061425648550.032018-06-14 16:01:582018-06-16 00:00:002018-06-14 16:00:008621.627.717.910.6355.712.062.641 days 08:00:00
90475912019050901167625.932019-05-09 04:01:022019-05-09 12:00:002019-05-09 04:00:007523.1319.47.919.0034.743.252.480 days 08:00:00
\n", + "
" + ], + "text/plain": [ + " id period_id forecast_demand date_time_current \\\n", + "10834054 2021030937 42 8679.12 2021-03-09 22:01:43 \n", + "9927842 2020040325 62 6711.89 2020-04-03 16:01:27 \n", + "10046976 2020051813 18 9745.08 2020-05-18 10:00:55 \n", + "9941110 2020040825 10 7812.37 2020-04-08 16:01:40 \n", + "8637337 2018120601 18 7833.57 2018-12-06 04:01:06 \n", + "9136007 2019061113 10 8010.47 2019-06-11 10:00:59 \n", + "8995579 2019041925 72 5674.61 2019-04-19 16:01:38 \n", + "10826680 2021030701 6 6315.22 2021-03-07 04:01:30 \n", + "8172468 2018061425 64 8550.03 2018-06-14 16:01:58 \n", + "9047591 2019050901 16 7625.93 2019-05-09 04:01:02 \n", + "\n", + " date_time_future date_time_current_rounded total_demand \\\n", + "10834054 2021-03-10 19:00:00 2021-03-09 22:00:00 8589.68 \n", + "9927842 2020-04-04 23:00:00 2020-04-03 16:00:00 6567.71 \n", + "10046976 2020-05-18 19:00:00 2020-05-18 10:00:00 9833.52 \n", + "9941110 2020-04-08 21:00:00 2020-04-08 16:00:00 7491.51 \n", + "8637337 2018-12-06 13:00:00 2018-12-06 04:00:00 8057.17 \n", + "9136007 2019-06-11 15:00:00 2019-06-11 10:00:00 7713.21 \n", + "8995579 2019-04-21 04:00:00 2019-04-19 16:00:00 5706.97 \n", + "10826680 2021-03-07 07:00:00 2021-03-07 04:00:00 6317.82 \n", + "8172468 2018-06-16 00:00:00 2018-06-14 16:00:00 8621.62 \n", + "9047591 2019-05-09 12:00:00 2019-05-09 04:00:00 7523.13 \n", + "\n", + " temperature_future temperature_current \\\n", + "10834054 22.4 21.9 \n", + "9927842 14.3 23.3 \n", + "10046976 15.6 16.3 \n", + "9941110 17.5 18.5 \n", + "8637337 24.9 17.3 \n", + "9136007 20.2 17.5 \n", + "8995579 15.7 23.3 \n", + "10826680 15.9 15.9 \n", + "8172468 7.7 17.9 \n", + "9047591 19.4 7.9 \n", + "\n", + " temperature_future_forecast humidity_future_forecast \\\n", + "10834054 20.59 69.30 \n", + "9927842 14.63 60.09 \n", + "10046976 14.45 71.70 \n", + "9941110 15.16 88.25 \n", + "8637337 24.37 47.95 \n", + "9136007 20.11 52.45 \n", + "8995579 17.86 94.80 \n", + "10826680 17.12 77.80 \n", + "8172468 10.63 55.71 \n", + "9047591 19.00 34.74 \n", + "\n", + " dew_point_future_forecast wind_speed_future_forecast \\\n", + "10834054 14.80 3.51 \n", + "9927842 6.84 1.21 \n", + "10046976 9.35 0.66 \n", + "9941110 13.18 2.16 \n", + "8637337 12.73 5.32 \n", + "9136007 10.05 3.25 \n", + "8995579 16.95 1.29 \n", + "10826680 13.19 0.94 \n", + "8172468 2.06 2.64 \n", + "9047591 3.25 2.48 \n", + "\n", + " forecast_interval \n", + "10834054 0 days 21:00:00 \n", + "9927842 1 days 07:00:00 \n", + "10046976 0 days 09:00:00 \n", + "9941110 0 days 05:00:00 \n", + "8637337 0 days 09:00:00 \n", + "9136007 0 days 05:00:00 \n", + "8995579 1 days 12:00:00 \n", + "10826680 0 days 03:00:00 \n", + "8172468 1 days 08:00:00 \n", + "9047591 0 days 08:00:00 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_all.loc[df_all.temperature_future_forecast.notna()].sample(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
date_time_current_roundeddate_time_futuretemperature_future_forecasthumidity_future_forecastdew_point_future_forecastwind_speed_future_forecast
02017-10-07 10:00:002017-10-07 10:00:0019.7545.607.752.57
12017-10-07 10:00:002017-10-07 11:00:0020.8141.987.333.07
22017-10-07 10:00:002017-10-07 12:00:0021.3840.797.483.59
32017-10-07 10:00:002017-10-07 13:00:0021.5041.818.054.07
42017-10-07 10:00:002017-10-07 14:00:0021.2044.798.854.43
.....................
38820742025-03-31 04:00:002025-04-16 00:00:0018.8761.2811.252.18
38820752025-03-31 04:00:002025-04-16 01:00:0018.2062.6010.921.94
38820762025-03-31 04:00:002025-04-16 02:00:0017.6763.9410.741.84
38820772025-03-31 04:00:002025-04-16 03:00:0017.3864.8110.661.95
38820782025-03-31 04:00:002025-04-16 04:00:0017.4164.7010.652.35
\n", + "

3882079 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " date_time_current_rounded date_time_future \\\n", + "0 2017-10-07 10:00:00 2017-10-07 10:00:00 \n", + "1 2017-10-07 10:00:00 2017-10-07 11:00:00 \n", + "2 2017-10-07 10:00:00 2017-10-07 12:00:00 \n", + "3 2017-10-07 10:00:00 2017-10-07 13:00:00 \n", + "4 2017-10-07 10:00:00 2017-10-07 14:00:00 \n", + "... ... ... \n", + "3882074 2025-03-31 04:00:00 2025-04-16 00:00:00 \n", + "3882075 2025-03-31 04:00:00 2025-04-16 01:00:00 \n", + "3882076 2025-03-31 04:00:00 2025-04-16 02:00:00 \n", + "3882077 2025-03-31 04:00:00 2025-04-16 03:00:00 \n", + "3882078 2025-03-31 04:00:00 2025-04-16 04:00:00 \n", + "\n", + " temperature_future_forecast humidity_future_forecast \\\n", + "0 19.75 45.60 \n", + "1 20.81 41.98 \n", + "2 21.38 40.79 \n", + "3 21.50 41.81 \n", + "4 21.20 44.79 \n", + "... ... ... \n", + "3882074 18.87 61.28 \n", + "3882075 18.20 62.60 \n", + "3882076 17.67 63.94 \n", + "3882077 17.38 64.81 \n", + "3882078 17.41 64.70 \n", + "\n", + " dew_point_future_forecast wind_speed_future_forecast \n", + "0 7.75 2.57 \n", + "1 7.33 3.07 \n", + "2 7.48 3.59 \n", + "3 8.05 4.07 \n", + "4 8.85 4.43 \n", + "... ... ... \n", + "3882074 11.25 2.18 \n", + "3882075 10.92 1.94 \n", + "3882076 10.74 1.84 \n", + "3882077 10.66 1.95 \n", + "3882078 10.65 2.35 \n", + "\n", + "[3882079 rows x 6 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_weather_forecast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/W3 Date time expansion.ipynb b/src/W3 Date time expansion.ipynb new file mode 100644 index 000000000..dcac0399e --- /dev/null +++ b/src/W3 Date time expansion.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Goal: Date-time expansion\n", + "- Day of week as an integer, One hot encode \n", + "- Month of year \n", + "- hour of day" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "pd.options.mode.chained_assignment = None " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read and format data\n", + "path = 'data/combined_data.csv' # change path\n", + "\n", + "df_all = pd.read_csv(path)\n", + "df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded\n", + "df_all[\"demand_error\"] = df_all.total_demand - df_all.forecast_demand\n", + "df_all[\"demand_error_relative\"] = df_all.demand_error / df_all.total_demand" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idperiod_idforecast_demanddate_time_currentdate_time_futuredate_time_current_roundedtotal_demandtemperature_futuretemperature_currenttemperature_future_forecasthumidity_future_forecastdew_point_future_forecastwind_speed_future_forecastforecast_intervaldemand_errordemand_error_relative
43772662014071340339003.512014-07-13 23:31:162014-07-14 16:00:002014-07-13 23:30:009359.3815.15.1NaNNaNNaNNaN16:30:00355.870.038023
71042492017050820326871.512017-05-08 13:31:482017-05-09 05:30:002017-05-08 13:30:006910.189.617.7NaNNaNNaNNaN16:00:0038.670.005596
16630212011092713258679.842011-09-27 10:00:472011-09-27 22:30:002011-09-27 10:00:008567.7014.817.3NaNNaNNaNNaN12:30:00-112.14-0.013089
8655550201812124136777.462018-12-13 00:01:132018-12-13 01:30:002018-12-13 00:00:006817.5921.221.2NaNNaNNaNNaN01:30:0040.130.005886
107442492021020404188238.182021-02-04 05:31:102021-02-04 14:30:002021-02-04 05:30:008546.6926.315.9NaNNaNNaNNaN09:00:00308.510.036097
\n", + "
" + ], + "text/plain": [ + " id period_id forecast_demand date_time_current \\\n", + "4377266 2014071340 33 9003.51 2014-07-13 23:31:16 \n", + "7104249 2017050820 32 6871.51 2017-05-08 13:31:48 \n", + "1663021 2011092713 25 8679.84 2011-09-27 10:00:47 \n", + "8655550 2018121241 3 6777.46 2018-12-13 00:01:13 \n", + "10744249 2021020404 18 8238.18 2021-02-04 05:31:10 \n", + "\n", + " date_time_future date_time_current_rounded total_demand \\\n", + "4377266 2014-07-14 16:00:00 2014-07-13 23:30:00 9359.38 \n", + "7104249 2017-05-09 05:30:00 2017-05-08 13:30:00 6910.18 \n", + "1663021 2011-09-27 22:30:00 2011-09-27 10:00:00 8567.70 \n", + "8655550 2018-12-13 01:30:00 2018-12-13 00:00:00 6817.59 \n", + "10744249 2021-02-04 14:30:00 2021-02-04 05:30:00 8546.69 \n", + "\n", + " temperature_future temperature_current \\\n", + "4377266 15.1 5.1 \n", + "7104249 9.6 17.7 \n", + "1663021 14.8 17.3 \n", + "8655550 21.2 21.2 \n", + "10744249 26.3 15.9 \n", + "\n", + " temperature_future_forecast humidity_future_forecast \\\n", + "4377266 NaN NaN \n", + "7104249 NaN NaN \n", + "1663021 NaN NaN \n", + "8655550 NaN NaN \n", + "10744249 NaN NaN \n", + "\n", + " dew_point_future_forecast wind_speed_future_forecast \\\n", + "4377266 NaN NaN \n", + "7104249 NaN NaN \n", + "1663021 NaN NaN \n", + "8655550 NaN NaN \n", + "10744249 NaN NaN \n", + "\n", + " forecast_interval demand_error demand_error_relative \n", + "4377266 16:30:00 355.87 0.038023 \n", + "7104249 16:00:00 38.67 0.005596 \n", + "1663021 12:30:00 -112.14 -0.013089 \n", + "8655550 01:30:00 40.13 0.005886 \n", + "10744249 09:00:00 308.51 0.036097 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_all.sample(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Plots Time Decomposition\n", + "def time_decomposition_error_plots(df, x, time_interval,\n", + " show_outliers = True, forecast_interval = 12, show_relative_error_all = True, show_relative_error_interval = True):\n", + " if show_relative_error_all:\n", + " plt.figure(figsize = (12,6))\n", + " sns.boxplot(data = df, x = x, y = \"demand_error_relative\", palette = 'Blues', showfliers = show_outliers)\n", + " plt.grid(alpha = 0.5)\n", + " plt.axhline(0, color='r', alpha = 0.2)\n", + " plt.title(\"{} vs Relative Demand Error (all forecasts)\".format(time_interval));\n", + "\n", + " if show_relative_error_interval:\n", + " plt.figure(figsize = (12,6))\n", + " sns.boxplot(data = df.loc[df.period_id == forecast_interval*2], \n", + " x = x, y = \"demand_error_relative\", palette = 'Blues', showfliers = show_outliers)\n", + " plt.grid(alpha = 0.5)\n", + " plt.axhline(0, color='r', alpha = 0.2)\n", + " plt.title(\"{} vs Relative Demand Error ({}h forecasts)\".format(time_interval, forecast_interval));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Day of Week\n", + "Notes:\n", + "- Demand: Weekends have less demand than weekdays. Sunday has lowest median demand\n", + "- Error\n", + " - Median: Sundays are over-forecasted. \n", + " - Variance: Sunday, Monday, Saturday (in descending order) have highest variance.\n", + " - Outliers: Fridays have large variance (outlier errors).\n", + "\n", + "To improve the forecast, propose adding **isFriday** and **isSunday** as boolean variables." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df_weekday = df_all.copy()\n", + "df_weekday[\"date_time_future_weekday\"] = df_weekday.date_time_future.dt.day_name()\n", + "df_weekday[\"date_time_future_weekday_num\"] = df_weekday.date_time_future.dt.dayofweek\n", + "df_weekday = df_weekday.sort_values(\"date_time_future_weekday_num\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,6))\n", + "sns.boxplot(data = df_weekday.groupby(\"date_time_future\", as_index = False).first().sort_values(\"date_time_future_weekday_num\"), \n", + " x=\"date_time_future_weekday\", y = \"total_demand\", palette = 'Blues', showfliers = False)\n", + "plt.grid(alpha = 0.5)\n", + "plt.title(\"Weekday vs Total Demand\");" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "time_decomposition_error_plots(df = df_weekday, x = \"date_time_future_weekday\", time_interval = \"Weekday\",\n", + " show_outliers = False, forecast_interval = 12, show_relative_error_all = True, show_relative_error_interval = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Month of Year\n", + "Notes:\n", + "- Demand: Seasonality observed in data. Highest demand in winter.\n", + "- Error\n", + " - Median: November, December and January have positive forecast error (i.e. under-forecasted)\n", + " - Variance: Seasonality observed in variance.\n", + " - Outliers: February has negative-error outliers (i.e. over-forecasted). \n", + "- Propose adding month booleans. However, this is likely reflected in temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_month = df_all.copy()\n", + "df_month[\"date_time_future_month\"] = df_month.date_time_future.dt.month_name()\n", + "df_month[\"date_time_future_month_num\"] = df_month.date_time_future.dt.month\n", + "df_month = df_month.sort_values(\"date_time_future_month_num\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,6))\n", + "sns.boxplot(data = df_month.groupby(\"date_time_future\", as_index = False).first().sort_values(\"date_time_future_month_num\"), \n", + " x=\"date_time_future_month\", y = \"total_demand\", palette = 'Blues', showfliers = False)\n", + "plt.grid(alpha = 0.5)\n", + "plt.title(\"Month vs Total Demand\");\n", + "\n", + "time_decomposition_error_plots(df = df_month, x = \"date_time_future_month\", time_interval = \"Month\",\n", + " show_outliers = False, forecast_interval = 12, show_relative_error_all = True, show_relative_error_interval = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hour of Day\n", + "Notes:\n", + "- Demand: Median as expected considering a work-day.\n", + "- Error\n", + " - Median: Least accurate between 4am - 8am.\n", + " - Variance: Variance highest during work hours.\n", + " - Outliers: Negative outliers between 4pm-8pm.\n", + "- Propose adding boolean variable for 4am-8am." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df_hour = df_all.copy()\n", + "df_hour[\"date_time_future_hour\"] = df_hour.date_time_future.dt.hour\n", + "df_hour = df_hour.sort_values(\"date_time_future_hour\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,6))\n", + "sns.boxplot(data = df_hour.groupby(\"date_time_future\", as_index = False).first().sort_values(\"date_time_future_hour\"), \n", + " x=\"date_time_future_hour\", y = \"total_demand\", palette = 'Blues', showfliers = False)\n", + "plt.grid(alpha = 0.5)\n", + "plt.title(\"Hour vs Total Demand\");\n", + "\n", + "time_decomposition_error_plots(df = df_hour, x = \"date_time_future_hour\", time_interval = \"Hour\",\n", + " show_outliers = False, forecast_interval = 12, show_relative_error_all = True, show_relative_error_interval = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Year\n", + "Notes:\n", + "- Demand: \n", + "- Error\n", + " - Median: \n", + " - Variance: \n", + " - Outliers: \n", + "- " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df_year = df_all.copy()\n", + "df_year[\"date_time_future_year\"] = df_hour.date_time_future.dt.year\n", + "df_year = df_year.sort_values(\"date_time_future_year\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGECAYAAAB3U5XYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde5xcdX3w8c83NzYh93AJJKwbBI3RqmCKtVprvRVbFa3aB7V9wEcf6qO2Wmst6lNEVIrWqq2XWqqIN8RL9SlSqiLeakXlJshllduyhISEXEiySTbJZr/PH3Oiw7qbzCRnzuxMPu/Xa187c86Z7/l+d2Z3v/Ob3zknMhNJkiRJ1ZjS7gQkSZKkQ4kNuCRJklQhG3BJkiSpQjbgkiRJUoVswCVJkqQK2YBLkiRJFbIBlyQgIn4QEWce4GOPj4ihklPqeBHx2Yg4t9157E9EvDYi3tfgtr+sKSKeGRED+9j2dyLijogYiojnlpPt5BAR/xQRr2p3HlKnsgGXRER8LiIuGrPsdyNiQ0Qc0668mhERr4qIPUWzszkiboiI57RoX6si4ml772fmXZk5uwX7qa9pKCLujoiLIuLEsvdVtXFq2/t1VMV5HAa8FWioAW/Su4APZObszLy8BfFL1eSb0PcC50TEtBamJHUtG3BJAH8B/EFEPAsgInqAfwX+KjPXlLmjiJhaZrwx/qtohBcAHwe+GBFzWri/KuytaR7wTGA3cG1EPKq9aZXiv4rmtP5r3diNxmvyDqTxm+C190fATZl5f7PxGvAw4JYDeeBkb2wzcxVwJ9BVI/tSVWzAJZGZG4A/By6MiMOBtwN3ZubFABExJSLeGhF3RsT6iLg0IhbUrftyRNwfEQ9GxHfrm8PiI/uPRMTXI2Ib8Dv1+46IP4mIH41Z9tcR8ZXi9nMj4raI2FqMPP9lA/WMAp8BZgMn1MV9ckT8qMjzpxHx1PEeHxEnRsR3ik8A1kfEZyJiXrHu88CxwH8WI7ZvjIgTIiIbrKcnIt4fEfdGxNqI+Gjxhmd/Ne3JzDsz88+Aq6k9R/utqxjVPK9Yvy0i/l9ELIqIz0fEloj4cUT01m3/4eLnvCUiromI365b967icZ8tno+bI+LkuvVPKPa/tfg5Hba/uiZS5PDXEfEzYPs+lj06Ir5X1P6ziPjDuhj7fO0VngN8r+4x+3w9N5H/ANDLr14nUyNiaURcHhEbI+L2iPhfddu/KyK+UPx8twJ/sq/fu+IxTy2e183F6+lPi+XPr3seBiPib+seMysiLile2w9GxE8i4oiIeA/wJOBjRb4fLPb/TxGxrtjHTRGxoq7M7wJ/iKTmZaZffvnlF5kJ8GXgMmAD0Fu3/E3AfwNLgB7gE8BninVTgDOBOcW6DwPX1j32s8Amav/cpwCHjdnnbGAIOL5u2Q3Ai4vbDwC/XdxeCJw8Qe6vAr5b3J4GvB7YCRxRLDuuqOv3izxOBdYDi4r1PwDOLG4/AngGMAM4qqj9fXX7WgU8re7+CbU/pw3V82Hgq9RG6ecCVwDv3F9NY5afBdzXRF2/AI4v9tkP/Bz4veLndAnwr3Wx/7T4OU8D/ga4b+9zRm1KxY5iX1OBvwd+UKw7rPi5/AUwHTid2mj9uc3UNuZnfB2wFJg53rLi+bkbeHOxz2cWP/sTGnnt1T03L6y738jr+dzi9jOBgf3UUP86+W/gQ0Xck4vn6Xfrfra7gOcVOcxk3793y4CtwB8Xz9URwOOLdU8HHlPEeVyxn+cW614L/L8i/lRgJTB77O9Acf8PgZ9Q+/RlCrACWFy3/o+Bn7T775ZffnXilyPgkuq9lto/7/Myc7Bu+Z8Bb83M+zJzGDgX+OOImJKZo5l5cWZurVv3hKiNpO/11cy8uth2Z/0OM3MIuJxaw0ZELKfWLO6dM7sbWBERczJzY2Zev4/8nxIRD1JrEv8OeFlmri/W/U/gssz8RpHH14EbqTWsD5GZv8jMqzJzV9amRHwA+N19/uQaqCciplBrPN+QmZsyc0uR5+mNxK6zmlqT3Ghdn8jaPPVNwDeAX2TmdzJzBPgScFJd/p8pfs4j1Ob5zqXuUwTge8W+9lD7lOHxxfInAwl8KDN3Z+al1JrbfXlKMQq79+vnY9b/Y2auyswdEyx7MrUm/O+LfX4L+E8e+vOc8LVXmE+tkd1bfyOv56ZFxDLgFODszBwuXsefpPaGZ68fZObXihx2sI/fO+BPgK9n5hczcyQz12fmT4savp2ZNxdxbgQu5Vev393UmvUTsvapyrXFa3Y8u6k9/8uLuLfmQ6fqbKX285PUJBtwSb+UmWupjZaNnbfaC3xtb6ME/Ixas3VU8dH6eyPirojYAtxRPOaIusffu59dXwK8tLj9cuArRcMB8ELg+cBgMR3gifuI84PMnE+tOb0CeErduocBL61v+IDfojad5CEiYnFEfDEi7itqunhMPfszUT2LqY0U31iXw+XURtmbsQTY2ERda+tu7xjn/i8PII2IN0dEf0RspjZ6fDgPrb2+AdterKfY36rMzLr19+ynjh9k5vy6r0eOWT/e66Z+2bHA4Dj7XLKfGPU2URvtBmrzxBt4PR+IY4H1mbmtiVwn/L2j9snHnePtKCKeVPyuPFA8j6+qy/9i4FvUjo+4LyIuiAnmm2fmN4GPAf8MrI2Ij8VDj6mYAzy4v8Il/TobcEmNWAU8a0yz1FOMhv1P4A+ojZzP41ejpVH3+GTfvg4siYjfoNa4XvLLB2b+ODOfT63puJzaaN4+ZeZW4P8Ar4yIxxaL7wU+OaaGwzPz78cJ8R5q01d+IzPnUpuSUEY9a6lNM3hkXQ7zMnPe/moa4wXAfx1AXfsUEb8HvBF4EbWRzQXUpnTEvh5XWENtaki93vE2bMJ4P+f6ZauB4yKiPr9eatNm9hWj3k3Uphzt1cjr+UCsBo4YM5K+v1z39Xt3L/DwCfZ1KfBvwHHFa+vje/MvPtU5NzMfRe0N6gupvUkcb/9k5gcz82RqU1pWUHt97PUoap+2SGqSDbikRnwMOD+Kg/Ui4qiIeH6xbg61ZnUDMAt4d7PBM3MXtYbh/dRGY79d7GdmRLwsIuZm5m5qH3nvaTDmA8BFwN4D0D4DvDAinlWMcvZExO9FxK+NgBc1bQM2R8Rx1Obi1ltLbVpJU/UU0zY+DnwwIo6MmqUR8ez91VPkfHxEfJRa4/TOA6hrf+YAI9Q+BZlObcpDo1MvfgBMiYjXRcS0iHgJtXnOrfRDavn+VURMj4inU2uev9hEjCt46PSig349jycz7waupfZ7dFhEPB54BfC5fTxsX793nwVOjYgXFT/vIyLicXU1bMzM4Yj4Leqm5ETE0yPiMcU0li3Uppns/Z16yOs6Ik4pvqZR+33YxUN//36X2pQfSU2yAZfUiPdTG9W9KmpnaPgh8JvFuk9SG91bTW3qyg8PcB+XUDuo7QtFo7rXGcA9xXSAV/LQObP78wHg+RHx6MwcoDba97fUDuwcBP6K8f8Ovp3afN3N1A5K/bcx688H3lFMDXhDk/X8FbWpBz8p4n8T2Nd5vX8nahf52UKtkZ8FrMzMWwCarGt/rqA2PeF2YKDYZ0OnoSzmV78Q+N/UpnX8EbWD/fbld+LXzwN+0n4eM3afzwNOo/am4Z+ozfv/RaMxihwfGxGLi/tlvZ7H8z+oPdf3Uzvg+a2Z+Z19bD/h713R0D+P2oGyG4Hrgd8oHvd/gL8rHvNWHvqG5FjgK9Se21uoPd+fL9Z9kF9NZ3o/tU9BPkFtmskAtdfCBwAiYklRy9cO4OcgHfLioVPnJEk6tETEa6idtWbsJx2aQET8I3BLZl7Y7lykTmQDLkmSJFXIKSiSJElShWzAJUmSpArZgEuSJEkVsgGXJEmSKjTu1a+62RFHHJF9fX2V7W/Xrl3MmDGjsv1VqZtrA+vrdNbXubq5NrC+Tmd9navq2q677rr1mXnkeOsOuQa8r6+Pa6+9trL9DQwMUGXDX6Vurg2sr9NZX+fq5trA+jqd9XWuqmuLiHsmWucUFEmSJKlCNuCSJElShWzAJUmSpArZgEuSJEkVsgGXJEmSKmQDLkmSJFXIBlySJEmqkA24JEmSVCEbcEmSJKlCNuCSJElShWzAJUmSpArZgEuSJEkVmtbuBCRJkqRmXXDBBfT39ze07eDgIHv27GHZsmUNbb98+XLOPvvsg0lvn2zAJUmS1NW2b9/O6Ohou9P4JRtwSWqzZkZxYPKN5EhSOzTzd+3MM89keHiYiy++uHUJNcEGXJI6zGQbyZEkNccGXJLarNnR6ck2krM/nTxPU5JawQZckjRpOLov6VBgAy5JaqlOnqcpSa3gecAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUobY34BFxakT8PCLuiIhfO1dVRBwWEV8o1v84Ivrq1j02Iq6OiFsi4mcR0VNl7pIkSVKz2tqAR8RU4CPAc4AVwEsjYsWYzV4JbMrME4APAO8pHjsN+Czw6sx8NPA0YHdFqUuSJEkHpN0X4jkFuCMz7wKIiEuB04Bb67Y5DTi3uP1l4MMREcCzgZsy80aAzNxQVdKSquflzCVJ3aLdDfgS4N66+6uAJ060TWaORMRmYBHwCCAj4hvAkcClmfne8XYSEWcBZwEsWbKEgYGBMmvYpw0buvd9QTfXBtY32WzZsoXh4eGGth0aGiIzG95+y5Ytlf5dOFjDw8OMjIx0VM6N6uba9uq0371mWV9n69b6JtvflnY34DHOsmxwm2nAU4DfBLYDV0XEdZl51a9tnHkhcCHAypUrs6+v72ByblrV+6tSN9cG1jeZnH/++Q1vu/dy5pdeemkLM2qfnp4ehoeHO+r5a1Q311bP+jqb9XWeyfa3pd0HYa4Cjqu7vxRYPdE2xbzvecDGYvn3MnN9Zm4HrgBObnnGkiRJ0kFo9wj4NcCJEbEMuA84HXjZmG0uA84ArgZeDHw7M/dOPXlzRMwCdgG/S+0gTUmSKuPxCZKa1dYGvJjT/TrgG8BU4KLMvCUizgOuzczLgE8An4mIO6iNfJ9ePHZTRLyfWhOfwBWZ+R9tKUSSpAZs376d0dHRdqchqc3aPQJOZl5BbfpI/bJz6m4PAy+Z4LGfpXYqQkmS2qKZEeq9xydcfPHFrUtI0qTX7jngkiRJ0iHFBlySJEmqUNunoHSSZg60gc472MYDiSRJh5pu/t/X7X1LJ7MBb6FuPtimm2uTJGk83f6/r9vrm0xswJvQ7Lu8TjvYxgOJJEmHmm7+39ftfUsncw64JEmSVCFHwCVJ0oS6eY601C424FKX8J+kpHZzDrHUGBtw6RDkP0lJjermOdJSu9iAS13Cf5KSJHUGD8KUJEmSKuQIuA4ZzpGWJEmTgQ24NA7nSEuSpFaxAdchwznSkiRpMnAOuCRJklQhG3BJkiSpQjbgkiRJUoVswCVJkqQK2YBLkiRJFbIBlyRJkipkAy5JkiRVyAZckiRJqpANuCRJklQhG3BJkiSpQjbgkiRJUoVswCVJkqQK2YBLkiRJFbIBlyRJkipkAy5JkiRVyAZckiRJqpANuCRJklQhG3BJkiSpQjbgkiRJUoVswCVJkqQK2YBLkiRJFWp7Ax4Rp0bEzyPijog4e5z1h0XEF4r1P46IvjHreyNiKCLeVFXOkiRJ0oFqawMeEVOBjwDPAVYAL42IFWM2eyWwKTNPAD4AvGfM+g8A/9nqXCVJkqQytHsE/BTgjsy8KzN3AZcCp43Z5jTgU8XtLwPPiIgAiIgXAHcBt1SUryRJknRQ2t2ALwHurbu/qlg27jaZOQJsBhZFxOHA3wDvqCBPSZIkqRTT2rz/GGdZNrjNO4APZOZQMSA+8U4izgLOAliyZAkDAwPNZ3oAhoeHGRkZqWx/Verm2sD6Op31da5urg2sr9NZX+eabLW1uwFfBRxXd38psHqCbVZFxDRgHrAReCLw4oh4LzAfGI2I4cz88NidZOaFwIUAK1euzL6+vrLrGFdPTw/Dw8NUtb8qdXNtYH2dzvo6VzfXBtbX6ayvc0222trdgF8DnBgRy4D7gNOBl43Z5jLgDOBq4MXAtzMzgd/Zu0FEnAsMjdd8S5IkSZNJWxvwzByJiNcB3wCmAhdl5i0RcR5wbWZeBnwC+ExE3EFt5Pv09mUsSZIkHZx2j4CTmVcAV4xZdk7d7WHgJfuJcW5LkpMkSZJK1u6zoEiSJEmHFBtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRVquAGPiEdExFURcXNx/7ER8X9bl5okSZLUfZoZAf9X4C3AboDMvAk4vRVJSZIkSd2qmQZ8Vmb+ZMyykTKTkSRJkrrdtCa2XR8RDwcSICJeDKxpSVaS1OEuuOAC+vv7WxK7v7+fnp4ezjzzzNJjL1++nLPPPrv0uJKkX2mmAX8tcCGwPCLuA+4GXt6SrCSpw/X393Pjzbey4Jje0mPvmTKdnsPnMLhhqNS4m9YMNrxtq95gtPLNBfgGQ9Lk0EwDfk9mPjMiDgemZObWViUlSd1gwTG9POvP3tqS2PNHh3hwyuxSY175L+c3vG1/fz8//dktzD5qaak57GIa82bN5o61m0uNCzC0blXpMSXpQDTTgN8dEV8HvgB8u6wEIuJU4B+BqcDHM/OCMesPAz4NPAHYAPyPzByIiGcBFwAzgF3AX2dmaXlJkvZt9lFLOfllf1V63GOnD7N6d0/pca+/5B9KjylJB6KZgzAfCXyL2lSUuyPiwxHxlIPZeURMBT4CPAdYAbw0IlaM2eyVwKbMPAH4APCeYvl64HmZ+RvAGcBnDiYXSZIkqQoNN+CZuSMzv5iZfwScBMwFvneQ+z8FuCMz78rMXcClwGljtjkN+FRx+8vAMyIiMvOGzFxdLL8F6ClGyyVJkqRJq6krYUbE70bER4HrgR7gjw9y/0uAe+vuryqWjbtNZo4Am4FFY7Z5EXBDZu48yHwkSZKklmp4DnhE3A38FPgitfnW20rYf4yzLJvZJiIeTW1ayrMn3EnEWcBZAEuWLGFgYKDpRA/E8PAwIyMjle2vSt1cG1hfp5sM9fX19TH3iJ3MHy33TCV7zcqdMFpuzMc88kQWzj6soZ9bX18fsxbu4Njpw+UmAcyfurv0mAAjj3oER82d2dbXxWR4bbaS9XW2yVDf17/+de6///7S4w4MDDB9+nTOPffc0mMDLF68mFNPPbXh7Zs5CPNxmbml+ZT2aRVwXN39pcDqCbZZFRHTgHnARoCIWAp8FfifmXnnRDvJzAupnUKRlStXZl9fX1n571NPTw/Dw8NUtb8qdXNtYH2dbjLUNzAwwOCGIY552ktas4NRSj8Lys0/v53eRbMb+rkNDAxwx9rNTDvpeaXmsFcrDsK86bZfcMLR89r6upgMr81Wsr7ONhnq+9GPfsRPrvspe6bPKzXulJFtLD5qEZ+/7DulxgWYunszpzzh8bz61a9u+DH7bcAj4s2Z+V7g3RExdnSazPyL5tJ8iGuAEyNiGXAftUvbv2zMNpdRO8jyauDFwLczMyNiPvAfwFsy878PIgdJkiRNEnumz2PLEb9detzHrDiK/lvXlR537vofNv2YRkbAbyu+X9t09P3IzJGIeB3wDWqnIbwoM2+JiPOAazPzMuATwGci4g5qI9+nFw9/HXAC8LcR8bfFsmdnZvk/WUml80qRkqRD1X4b8Mz8WnFze2Z+qX5dRBz0Z6uZeQVwxZhl59TdHgZ+bT+Z+S7gXQe7f0nt0d/fz89uuZWjjusrP/i0GcyaPZe1W7aXGnbdvQOlxpMkHZqamQP+FuBLDSyTpIYcdVwfL3/TeS2JPX3Hg+yeOb/UmJ973zn730hdoVWf0PjpjCRobA74c4A/AJZExD/VrZoLjLQqMUmS2qW/v5/rb7qZnkXHlhp31+gU5sw8nFvv21hq3OENY89fIGkya2QEfDW1+d/PB66rW74V+MtWJCVJUrv1LDqWvue9pvS4J8zewx1DU0uNOfC1j5YaT1JrNTIH/Ebgxoi4JDNbc3JWSZIk6RDRzBzwvoj4O2AFtatgApCZx5eelSRJktSlmmnAPwm8HfgA8HvAKxj/KpWSJElt4SlO1QmaacBnZuZVERGZeQ9wbkT8F7WmXJIkqe36+/u59oafEbOPLD127oLD583iutvLvVR6Dj1QajxNfs004MMRMQW4vbh4zn3AUa1JS5IktUq3n2YxZh/JtJNO3/+GB2DZkhlsvG9XqTFHbri01Hia/JppwN8AzAL+Angn8HRql4iXJEkdpL+/n+tu/BnT5i0uNe6ekWB2zyxuHCh3RHdkc7kjzlK7NdyAZ+Y1xc0havO/JUlSh5o2bzELnlr+v/MVRwQ3r89SY276/idLjSe1WyMX4vkaMOFvUmY+v9SMJEmSpC7WyAj4+1qehSRJknSIaORCPN/bezsiZgK9mfnzlmYlSZKkX+NpFrtDw3PAI+J51EbDZwDLIuLxwHlOQZFap9vPVCBJak5/fz/XXH8TozMXlh47do4ya85MfnzbqlLjTtmxsdR43aCZs6CcC5wCfBcgM38aEX2lZyTpl/r7+7n51ttY8rByLzg7dfphzJ4zl03bdpYa97577io1niTp143OXMjw8c9tSezFD5/D3XduLTVmz12XlxqvGzTTgI9k5uYIL37ZSTpxBBUcRa235GHH8+fnvLf0uLu3rGf63CNKjfmh895cajxJkrpRMw34zRHxMmBqRJxI7XzgP2xNWtXp9rlU/f393HZbP8sefmKp+z+sZyZz585leNeeUuMC3H3n7aXHlCRJmiyaacD/HHgbsBO4BPgG8K5WJFWl/v5+buvv54QTH1l67J6Zs5g3dy6795R7PtQ7bm/uGNhlDz+Rd7//o6XmALBx3X0sPGpJ6XHf9sbXlB5TkiRpsmioAY+IqcA7MvOvqTXhXeWEEx/JBz9yYUti37/6XhYfe1ypMd/w2rNKjSdJkqTqNNSAZ+aeiHhCq5ORmtWJc9yd3y5J0qGtmSkoN0TEZcCXgG17F2bmV0rPSmpQf38/t952G73LTig17vTDepgzdx5Dw7tLjTt49x2lxpMkSZ2nmQZ8IbABeHrdsgRswNVWvctO4G3n/2PpcYc23s/shYtLjfnut76+1HhSuwwODrJ181auv+QfSo99c4yyK6eUHnfrulUM7txcelxJalbDDXhmvmJf6yPiLZn5dwefkiR1vsHBQTZt2cqV/3J+S+JPYw8jTC015qY198C2OaXGlCT9umZGwPfnJYANuCQdAnp7e9m1djMnv+yvSo997PRhVu/uKT3u9Zf8A71Hzys9riQ1q8wG3Cv0SFKht7cXNgzxrD97a0vizx8d4sEps0uNeeW/nE/vonJjSpJ+XZmT7Mo92bUkSZLUhcpswB0BlyRJkvajoQY8IqZGxF/uZ7MvlZCPJEmS1NWauRDPacAH9rFNaw71l9SVBgcH2bx1iM+975yWxJ8yOsLolDIPc4F19w6wc45zpCVJB6eZ/07/HREfBr7AQy/Ec33pWUmSJEldqpkG/LeL7+fVLUseemEeSWpIb28va7ds5+VvOm//Gx+A6TseZPfM+aXG/Nz7zuHoubNKjanJaXBwkOEHtzDwtY+WHnvtVNixp9yYwxtWM7hnqNygklqmmQvx/F4rE5EkSZIOBQ034BExD3g78NRi0feA8zLT6/pKkrpKb28vQ1M30ve815Qe+4TZe7hjqNyrmA587aP0LllYakxJrdPMaQgvArYCf1x8bQE+2YqkJEmSpG7VzBzwh2fmi+ruvyMiflp2QpIkSTo0DQ4OMnX3Zuau/2Hpse+5djpzt+8uPe7U3ZsZHBxs6jHNjIDviIin7L0TEU8GdjS1t3FExKkR8fOIuCMizh5n/WER8YVi/Y8joq9u3VuK5T+PiN8/2FwkSZKkVmtmBPzVwKeLueAAm4AzDmbnETEV+AjwLGAVcE1EXJaZt9Zt9kpgU2aeEBGnA+8B/kdErABOBx4NHAt8KyIekZklH1suSZI6xeDgIDn0ICM3XNqS+LfcOoWRnaOlxsyhdQwO7io1Zqfq7e3lvgf3sOWI397/xk167Iqj+MGt60qPO3f9D+nt7W3qMQ014BExBXhkZj4uIuYCZOaW5lP8NacAd2TmXcV+LgVOA+ob8NOAc4vbXwY+HBFRLL80M3cCd0fEHUW8q0vIS5KkrjU4OMjIg5vZ9P3yD+W6fnqwbXeWGnPkwfsZHDzoD92lSaPRK2GORsTrgC+W1HjvtQS4t+7+KuCJE22TmSMRsRlYVCz/0ZjHLtnvHoeH4Re/+OXdozdvZmQUpt5x+4Hkv18z1q5h6vbhUmMu3rKFaVN4SB0TOXrzZnbtHmX6neXXd9iGtUzfur30uMds2cKM6VMarm/OrhEOu/vO0vPY8+ADHLZ52/43bMIxW7cwa+e0hmqDWn0zd+xi1sBdpeYBsHtoE9Nnl/nrDMdu3cLckRkNP3fThoaZM3h3qTnsNW3nVkYO21RqzCVbt7Iodzdc3+jQdhbed0+pOew1Z3Q7U6aUe07ypUNbOXranobr27ltiCPXNDfvsRELpu9i9+4Zpcc9btsQR2+m4fqGtg1xzLpVpeexaGiUbdubmQG6f6Pbhjh689SG/7bM2rWLw0b3MG13+aOu0xMOGyk35tTRPczatauh+n5z3jxueXA3U5c9rdwkCo85ejo3ry13HvGeW/+DR8+b1/Brs2/HdnZuKX8kF2Dxhm0s21Lu/77Ddmzn6M2bG69veDtD2zaWmgPAMZvg+BbEnT3ceH17NTMF5cqIeBO/fiXMg6kkxlk29m3zRNs08thagIizgLMAeo8+mlWrfvUHdenSpewZTR5Yu6ahhJs1tLX8szQet3QJU6fEQ+qYyDXlWtUAAB+5SURBVNKlS9k9MsqDG9aWnseOoa2lxwQ47rilTJ82peH6du3ew/YHHyg9j507yr+oRd9xxzFj+tSGaoNafTt2jbB7qNxGEmB013Z2l1zisof1MnPGtIafu/nDu5i2szWvo6m7yx8te3hfL7N7ZjRc3+wFu5gzWv6bVICZuQvK/RScE5f1Mf/wxuubOX+Yo6aX38DNnbIbppcelj0P72PR7J6G65sxdyHzZ5X8QwYWzig/5twTl3HU3FkN/235/ZNO4v5N2+jpO6n0XI6aFazbXu4I+PDADSxecHjDz13MnMfUo1vwIgKOmVPuKSQB9ow+nCWL5jRcX86Yw8jCw0vPA+C4heW/+Z02+wSWHjWv8fqmHc7wrPJPq/mwI1vzM+vZ/kiWLl7Q8O8fNNeA/6/i+2vrliVwfBMxxloFHFd3fymweoJtVkXENGAesLHBx9aSzLwQuBBg5cqVufTpv7p453Wf/jS79yQvf/JTx3voQdu1+l4WHnvc/jdswjWXfJbpU4M/f/r+L0J63ac/zfCuPZx2ylP2u22zdq67j8OP2v+HDs36yaWX0DNjasP1DQ3v5ukn/VbpeYxuvJ+pCxeXGvPqL32e2T3TG6oNavVt2raTkx/zhFLzANi9ZT3T5x5Rasz//soXWHD4YQ0/d2u3bOe4Ex9bag57teJKmN//9y9z9NxZDdc3uGGIacc9qtQc9po/OsSDU2aXGvM7V3yV3tHZDdd3x9rNnPz0E0rNAeDY6cOs3t1Tetzr7/93Tjh6XsP13bp6I31PaG5eZyNach7w1ZezIhY29bflxlUPsOD4Y0rNA+Axs4Obh8ttwDet+jqPm3Zkw8/ddffcz7SFTys1h72ecMQMrltf7hvPkXvu5wkzaLi+Hw+sZnjKyaXmsNeTFs3h6gfLHRjpGVjNE2dOabi+q++8ly1HlNs7ATxlweH8YE25o/sAc9ffy5MOn9bw7x80eBaUYg74n2TmsjFfB9N8A1wDnBgRyyJiBrWDKi8bs81l/OpgzxcD387MLJafXpwlZRlwIvCTg8xHkiRJaqlm5oC/D3hSmTsv5nS/DvgGMBW4KDNviYjzgGsz8zLgE8BnioMsN1Jr0im2+yK1AzZHgNd6BhRJkiRNds1MQflmRLwI+EoxAl2KzLwCuGLMsnPqbg8DL5ngse8G3l1WLpIkSVKrNdOAvxE4HNgTETuoHQSZmTm3JZlJYnBwkC1D2/jQeW8uPXbu2U1MLfcgpfvuuYuts1tzkIskqfZ/YcqOTfTcdXlL4t+5eho9w+WexmbKjg0MDpZ/8HEna7gBz8w5rUxEkiRJOhQ03IAXF795ObAsM98ZEccBx2SmBz5KLdLb28umbTv583PeW3rsVpwF5UPnvZkFhx9WakxJ0q/09vayZtsUho9/bkvin/TwOVx9Z8lnQbnrcnp7l5Yas9M1cyWAj1I7CPNlxf0hapeRlyRJktSgZuaAPzEzT46IGwAyc1Nx6kBJkiRJDWqmAd8dEVMprjYZEUdS+nXYqjc4OMi27dt5w2vPakn8XTuHmXFYuReUuOP2n3P4rMYuQT04OMjQtu287Y2vKTUHgN27dzJ9evnTDe6683ZmH17uJbYlSZImi2Ya8H8CvgocFRHvpnZRnP/bkqykBg0ODrJ1aBvvfuvrS4+9Z/cupk4v90Oee+66gzmeJUSSpENaM2dB+VxEXAc8g9opCF+QmbftXR8RCzJzUwtybKne3l5270k++JELWxL//tX3srjkS9G/4bVnMX1qNLRtb28vw7v28O73f7TUHAA2rruPhS24FP3b3vgaemaUe5lmSZKkyaKZEXAysx/on2D1VcDJB52R1ITe3l6GhnfztvP/sfTYQxvvZ/bCxaXGfPdbX8/snnLPvS21y9C6VVx/yT+UGnP7pgc4cuF8tmX5vydD61bB0fNKjytJzWqqAd+PxoZkJekQsWnNIFf+y/mlx926YS2LFsxn15Ryj8HYtGaQ3kUrGtp2+fLlpe57r/5Na9i5fYgT+vrKD370vJblLUnNKLMBL+3y9JLU6VrZ6PU/sIrhbVvp61tUatzeRSsazvvss88udd97nXnmmQwPD3PxxRe3JL4kTQZlNuCSpEKrGlSwSZWkTtfMhXj2xykokiRJ0n7sdwQ8Ihbua31mbixuPqOUjCRJkqQu1sgUlOuoze8OoBfYVNyeDwwCy+AhjbgkSZKkCey3Ac/MZQAR8THgssy8orj/HOCZrU1PUjdbd+8An3vfOaXH3bTufhbOn0/OKPcqtOvuHeDoRzd2lhBJkibSzEGYv5mZr957JzP/MyLe2YKcJB0CWnmWkE2rd7F9aAt9ffucQde0ox/d+FlCJEmaSDMN+PqI+L/AZ6lNSfkTYENLspLU9TxLiCa74Q2rGfhauVcR3rV5PZsWLWDzSLlX+x3esBqWlPuGU1LrNNOAvxR4O/DV4v73i2WSJHWVll1oaOs6du/YxoqyLzS0ZKGfzkgdpOEGvDjI8vUtzEWSpEnBCw1JaqWGG/CIeATwJqCv/nGZ+fTy05IkSZK6UzNTUL4EfAz4OLCnNelIkiQdnBx6gJEbLi0/7o4HufvIhYwMjZYbd+gBYHGpMTvZ1N2bmbv+h6XGnDKyjfWji5i7cbjUuFDLt1nNNOAjmfnPTe9BkiSpIq2cC9/f/yB7dm7nCSf2lRx5sXP4Cy07/qK/nxzZyZMeu6wl8ZvNu5kG/GsR8RpqB2Hu3LvQC/BIktR5Rjbfz6bvf7LUmHuGNnLrEQvYNBylxh3ZfD9wZEPbeoalznaoHH/RTAN+RvH9r+uWJXB8eelIkqRWa90o4wZGhrfzuLLP8sKRjhCrqzRzFpTWjNlLkqRKHSqjjNJk1cwIOBHxGGAF8MvrO2fmp8tOSpIkSepWzZyG8O3A06g14FcAzwF+ANiAS5IkSQ2a0sS2LwaeAdyfma8AHgcc1pKsJEmSpC7VTAO+IzNHgZGImAuswwMwJUmSpKY0Mwf82oiYD/wrcB0wBPykJVlJkiRJXaqZs6C8prj5sYj4OjA3M29qTVqS9rrvnrv40HlvLjXm+vtXs2D+PKb2HF5q3PvuuYsFKx5VakxJkrpNs2dBeSzQt/dxEXFCZn6lBXlJonXn6l27eydDW7fQt2hhqXEXrHiU5+qVJGk/mjkLykXAY4FbgNFicQI24FKLeK5eSdJYU3ZspOeuy0uPGzu3cP/6RfRs2V1q3Ck7NgJLS43Z6ZoZAf+tzFzRskwkSZK0T638lLG/v5/R3Tt44qP6So681E9Hx2imAb86IlZk5q0ty0aSJEkTatUno+Cno1Vq5jSEn6LWhP88Im6KiJ9FxAEfhBkRCyPiyoi4vfi+YILtzii2uT0iziiWzYqI/4iI/oi4JSIuONA8JEmSpCo104BfBPwpcCrwPOC5xfcDdTZwVWaeCFxV3H+IiFgIvB14InAK8Pa6Rv19mbkcOAl4ckQ85yBykSRJkirRzBSUwcy8rMR9n0bt0vZQG13/LvA3Y7b5feDKzNwIEBFXAqdm5ueB7wBk5q6IuB5n90uSJKkDNNOA90fEJcDXgJ17Fx7EaQiPzsw1RYw1EXHUONssAe6tu7+qWPZLxcWBngf840Q7ioizgLMAlixZwsDAwC/X9fX1sWc0uX/1vRM8+uBseXBj6TGPX9bH1CnxkDom0tfXx5o193PRh/++1Bx+dtONzJw1kxNOeESpcQHmzTmcY45Z3ER9a/jiJyZ8+g/IzT+7iVkzZ3L8CSeWGnfRvNkcc8wxDdXWSsPDw4yMjLQ9j1axvs7VzbWB9XU66+tck622ZhrwmdQa72fXLdvnaQgj4lvA4nFWva3BfcY4y7Iu/jTg88A/ZeZdEwXJzAuBCwFWrlyZfX19v1w3MDDAbf393HX3QIMpNe6+Vfcyb+5cZs+dV2rcO27/OY9avpz6OibS09PDzp3D3LdqsNQc1q1bS09PDzN7ekqNu1dPT08T9e1kzX3lvoF6YG99M8uvr9HaWqmnp4fh4eG259Eq1te5urk2sL5OZ32da7LV1syVMF/RbPDMfOZE6yJibUQcU4x+HwOsG2ezVfxqmgrUppl8t+7+hcDtmfnBZnPbq5WnxRnesR1ylAUL5pca91HLlzecd7efR7rb65MkSd2nmQvxPAL4Z2pTRx5TXBXz+Zn5rgPc92XAGcAFxfd/H2ebbwDn1x14+WzgLUU+7wLmAa86wP0Dns5HkiRJ1WrmLCj/Sq353Q2QmTcBpx/Evi8AnhURtwPPKu4TESsj4uPFPjYC7wSuKb7Oy8yNEbGU2jSWFcD1EfHTiDioRlySJEmqQjNzwGdl5k8iHjIte+RAd5yZG4BnjLP8WupGtTPzImqnQKzfZhXjzw+XJEmSJrVmRsDXR8TDKQ6CjIgXA2takpUkSZLUpZoZAX8ttYMel0fEfcDdwMtbkpUkSZLUpfbbgEfEG+vuXkHtAjhTgG3Ai4D3tyY1SZIkqfs0MgI+p/j+SOA3qZ2tJKhdlv77LcpLkiRJ6kr7bcAz8x0AEfFN4OTM3FrcPxf4UkuzkyRJkrpMMwdh9gK76u7vAvpKzUaSJEnqcs0chPkZ4CcR8VVqZ0J5IfCplmQlSZIkdalmLkX/7oj4T+B3ikWvyMwbWpOWJEmS1J2aGQEnM68Hrm9RLpIkSVLXa2YOuCRJkqSDZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVahtDXhELIyIKyPi9uL7ggm2O6PY5vaIOGOc9ZdFxM2tz1iSJEk6eO0cAT8buCozTwSuKu4/REQsBN4OPBE4BXh7faMeEX8EDFWTriRJknTw2tmAnwZ8qrj9KeAF42zz+8CVmbkxMzcBVwKnAkTEbOCNwLsqyFWSJEkqxbQ27vvozFwDkJlrIuKocbZZAtxbd39VsQzgncA/ANv3t6OIOAs4C2DJkiUMDAwcRNqNGx4eZmRkpLL9VambawPr63TW17m6uTawvk5nfZ1rstXW0gY8Ir4FLB5n1dsaDTHOsoyIxwMnZOZfRkTf/oJk5oXAhQArV67Mvr79PqQUPT09DA8PU9X+qtTNtYH1dTrr61zdXBtYX6ezvs412WpraQOemc+caF1ErI2IY4rR72OAdeNstgp4Wt39pcB3gScBT4iIAWo1HBUR383MpyFJkiRNYu2cA34ZsPesJmcA/z7ONt8Anh0RC4qDL58NfCMz/zkzj83MPuApwC9sviVJktQJ2tmAXwA8KyJuB55V3CciVkbExwEycyO1ud7XFF/nFcskSZKkjtS2gzAzcwPwjHGWXwu8qu7+RcBF+4gzADymBSlKkiRJpfNKmJIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSaqQDbgkSZJUIRtwSZIkqUI24JIkSVKFbMAlSZKkCtmAS5IkSRWyAZckSZIqZAMuSZIkVcgGXJIkSarQtHYnIEnqbhdccAH9/f0Nbdvf38/o6ChnnnlmQ9svX76cs88++yCyk6Tq2YBLkiaNWbNmsWfPnnanIUktZQMuSW3WzAgxdN4ocbP7HhgYoK+vrzXJSNIkYAMuSR3GUWJJ6mw24JI6QjfPIz6QfTtKLEmdywZcUtdxhFhV6uY3h5JawwZcUkdwHrG6gW8OJYENuCRJB8U3h5Ka5YV4JEmSpAo5Ai5JkibU7XPcu70+TU424JIkqRTdPse92+tTdWzAJUnShLp9jnu316fJyTngkiRJUoVswCVJkqQK2YBLkiRJFbIBlyRJkipkAy5JkiRVyAZckiRJqpANuCRJklQhG3BJkiSpQjbgkiRJUoXa1oBHxMKIuDIibi++L5hguzOKbW6PiDPqls+IiAsj4hcR0R8RL6oue0mSJOnAtPNS9GcDV2XmBRFxdnH/b+o3iIiFwNuBlUAC10XEZZm5CXgbsC4zHxERU4CF1aYvTS4XXHAB/f39DW3b39/P6OgoZ555ZkPbL1++vOnLNUuSpPG1swE/DXhacftTwHcZ04ADvw9cmZkbASLiSuBU4PPA/wKWA2TmKLC+5RlLXWLWrFns2bOn3WlIknTAOnngqZ0N+NGZuQYgM9dExFHjbLMEuLfu/ipgSUTML+6/MyKeBtwJvC4z17YyYWkya/YPxcDAAH19fa1JRpKkSWSyDTy1tAGPiG8Bi8dZ9bZGQ4yzLKnlvRT478x8Y0S8EXgf8KcT5HEWcBbAkiVLGBgYaHD3B2d4eJiRkZHK9lelbq4Nur8+gA0bNrQ7hZayvs7VzbWB9XW6bq6v0/73nX766U1tv2HDBhYtWtTw9q38ObS0Ac/MZ060LiLWRsQxxej3McC6cTZbxa+mqUCt6f4usAHYDny1WP4l4JX7yONC4EKAlStXZlWjfj09PQwPD3flKGM31wbdX99e1tfZurm+bq4NrK/TdWt9h8L/vslSWzunoFwGnAFcUHz/93G2+QZwft0ZUp4NvCUzMyK+Rq05/zbwDODWlmcsSZLUIZqZIw2Tb550N2tnA34B8MWIeCUwCLwEICJWAq/OzFdl5saIeCdwTfGY8/YekEntgM3PRMQHgQeAV1SbviRJUveYbPOku1nbGvDM3EBt5Hrs8muBV9Xdvwi4aJzt7gGe2socJUmSOtWBjE57gH41vBKmJEmSVCEbcEmSJKlC7ZwDLlWqk0/YL0mSuocNuDQOD0SRJEmtYgOuQ4ZXipQkSZOBc8AlSZKkCtmAS5IkSRVyCop+yYMUJUmSWs8GXAfEgxQlSZIOjA14E5oZIYbOGyX2IEVJkqTWswFvIUeJJUmSNJYNeBMOZHTaUWJJkiTV8ywokiRJUoVswCVJkqQK2YBLkiRJFbIBlyRJkipkAy5JkiRVyAZckiRJqpANuCRJklQhG3BJkiSpQjbgkiRJUoVswCVJkqQK2YBLkiRJFbIBlyRJkipkAy5JkiRVKDKz3TlUKiIeAO6pcJdHAOsr3F+Vurk2sL5OZ32dq5trA+vrdNbXuaqu7WGZeeR4Kw65BrxqEXFtZq5sdx6t0M21gfV1OuvrXN1cG1hfp7O+zjWZanMKiiRJklQhG3BJkiSpQjbgrXdhuxNooW6uDayv01lf5+rm2sD6Op31da5JU5tzwCVJkqQKOQIuSZIkVcgGvEkRcVxEfCcibouIWyLi9cXyhRFxZUTcXnxfUCxfHhFXR8TOiHjTmFinRsTPI+KOiDi7HfWMyafM2i6KiHURcXM7ahlPWfVNFKfdSqyvJyJ+EhE3FnHe0a6a6pX5+izWT42IGyLi8qprGU/Jv38DEfGziPhpRFzbjnrG5FNmbfMj4ssR0V/Ee1I7ahqTU1m/e48snrO9X1si4g3tqqsurzKfv78sYtwcEZ+PiJ521DQmpzLre31R2y2T4bmDA6rv5RFxU/H1w4h4XF2sTu9b9lVbtX1LZvrVxBdwDHBycXsO8AtgBfBe4Oxi+dnAe4rbRwG/CbwbeFNdnKnAncDxwAzgRmBFN9RWrHsqcDJwc7ufsxY8d+PG6aL6Aphd3J4O/Bj4rW6pry7eG4FLgMvbXVvZ9QEDwBHtrqlFtX0KeFVxewYwv5vqq4s5Fbif2nmEu6I+YAlwNzCzuP9F4Mwuqu8xwM3ALGAa8C3gxA6s77eBBcXt5wA/rntNdnrfMm5txf1K+xZHwJuUmWsy8/ri9lbgNmp/VE6j9o+B4vsLim3WZeY1wO4xoU4B7sjMuzJzF3BpEaNtSqyNzPw+sLGKvBtVVn37iNNWJdaXmTlU3J1efLX9YJEyX58RsRT4Q+DjFaTekDLrm2zKqi0i5lL7J/mJYrtdmflgJUXsQ4ueu2cAd2ZmlReOG1fJ9U0DZkbENGqN6uoWp79fJdb3KOBHmbk9M0eA7wEvrKCEfTqA+n6YmZuK5T8Clha3u6Fvmai2yvsWG/CDEBF9wEnURgiPzsw1UHtBUHuHvC9LgHvr7q9iEjRxex1kbZNeWfWNiTNpHGx9UZue8VNgHXBlZnZVfcAHgTcDoy1K8aCUUF8C34yI6yLirFbleSAOsrbjgQeAT0Zt+tDHI+LwFqbbtBL/dp4OfL7s/A7WwdSXmfcB7wMGgTXA5sz8ZivzbdZBPn83A0+NiEURMQv4A+C41mXbvAOo75XAfxa3u61vqa+tcjbgBygiZgP/BrwhM7ccSIhxlrV9lBFKqW1SK6u+yfpzKiOvzNyTmY+nNjpwSkQ8pswcD8bB1hcRzwXWZeZ1pSdXgpJeV0/OzJOpfcT62oh4amkJHoQSaptG7SPif87Mk4Bt1D5enhRK/NsyA3g+8KWycitDCb97C6iNTC4DjgUOj4g/KTfLA3ew9WXmbcB7gCuBr1ObojFSapIHodn6IuL3qDWpf7N30TibdWTfMk5tlbMBPwARMZ3aE/25zPxKsXhtRBxTrD+G2sjhvqzioe+MlzIJPoorqbZJq6z6JojTdmU/f8XH+98FTi051QNSUn1PBp4fEQPUPkJ9ekR8tkUpN6Ws5y8zVxff1wFfpfbRcVuV+HdzVd0nMl+m1pC3Xcm/e88Brs/MteVnemBKqu+ZwN2Z+UBm7ga+Qm1ObtuV+Lv3icw8OTOfSm06w+2tyrkZzdYXEY+lNkXvtMzcUCzuir5lgtoqZwPepIgIavMPb8vM99etugw4o7h9BvDv+wl1DXBiRCwrRjtOL2K0TYm1TUpl1bePOG1VYn1HRsT84vZMav80+8vPuDll1ZeZb8nMpZnZR+337tuZ2fZRuBKfv8MjYs7e28CzqX003jYlPnf3A/dGxCOLRc8Abi053aa14G/nS5lE009KrG8Q+K2ImFXEfAa1ObttVebzFxFHFd97gT9iEjyPzdZX5P4V4E8z8xd123d837KP2qqXbT46t9O+gKdQ+8jlJuCnxdcfAIuAq6i9270KWFhsv5jau8YtwIPF7bnFuj+gdsTuncDbuqy2z1Ob47e7WP7KbqlvojhdVN9jgRuKODcD57S7trJfn3Uxn8bkOQtKWc/f8dQ++r4RuKUL/7Y8Hri2iPX/KM5o0EX1zQI2APPaXVeL6nsHtTf0NwOfAQ7rsvr+i9qbwhuBZ7S7tgOs7+PAprptr62L1el9y75qq7Rv8UqYkiRJUoWcgiJJkiRVyAZckiRJqpANuCRJklQhG3BJkiSpQjbgkiRJUoVswCVJkqQK2YBLUgtExLkR8aZ9rH9BRKw4wNgPeWxEnBcRzzyQWA3u7y8i4raI+Nw+tpkfEa9pVQ6S1E1swCWpPV4AHFADPvaxmXlOZn6rlKzG9xpqF5t6+T62mV9s15SImHrAWZUkIqa1OwdJhxYbcEkqSUS8LSJ+HhHfAh5ZLPvfEXFNRNwYEf9WXIb7t4HnA38fET+NiIcXX1+PiOsi4r8iYvkE+xjvsRdHxIuL9QMRcX5EXB0R10bEyRHxjYi4MyJeXRfnr4u8boqId+yjpo9Ru7rmZRHxl2NH9iPi5ojoAy4AHl7k9PcR8bSIuLxuuw9HxJl1OZ4TET8AXtJE7XMi4u6ImF7cn1vEmj5RjIh4XkT8OCJuiIhvRcTRxfJzI+LCiPgm8Ol9Pa+SVDbf9UtSCSLiCcDpwEnU/rZeD1wHfCUz/7XY5l3ULm/8oYi4DLg8M79crLsKeHVm3h4RTwQ+Cjx97H4y84fjPHbsZvdm5pMi4gPAxcCTgR5ql6b/WEQ8GzgROAUIas31U/9/e/cTYlUZxnH8+6vIgjFNIZBaFO1CKGwToWgI0h/CWghtIvoHuWgVRSDBbKRF4MZVEdSmP2ZD4c4iCE2iTWm2iDYWEW0MpKEyip4W5x29TXdm7tzunDH4fmC4d8457/O+7139OPc5M1V1bMh8TyW5G7irqs4mmV7gI3ge2FxVt7U17VjiIztfVVuXuffZJB8D99H9G/qHgJmq+iPJKwvU+AS4o6oqyRPAc8AzreTtwNaq+m2JtUrSRBnAJWkytgHvVdWvAC0kA2xuwXs9MAUcnT8wyRRwJ3B4IEyv+Q9rmZv7NDBVVbPAbJLzSdYDu9rPF+26KbpA/q8AvoIOwVh7f5UuRL8PPAo8uUSNG4BDSTYBVwJnBmodMXxLWg0GcEmanBpy7HXggao61Vowdgy55jLg3Nzd4wn4vb3+NfB+7vcr6O56v1hVL49R+0/+2b541ZjX/dJel7X3qjqR5MYk24HLq+qrJNcsUuMgcKCqjrS78tND1iBJvbIHXJIm4xjwYJKrk6wF7m/H1wI/tr7lwYcYZ9s5qupn4EySPQDp3LrIXBfGjuko8Fi7c0yS65NcN+LYb4EtbdwW4KYF1vQdcEuSNUnWATuHFRtj79D1bL8FvDZCjXXAD+39IyPuUZJWlAFckiagqj6na6s4CcwAx9upF4DPgA+BrweGvA082x4OvJkunD+e5BRdr/buRaabP3a5a/0AeBP4NMlp4F1GD/QzwIYkJ4G9wDet5k/AifZQ5ktV9T3wDvAl8AYX212GWc7eafWupQvhS9WYpmtNOQ6cHXGPkrSiUjXsG1NJki5N7S++7K6qh1d7LZI0DnvAJUn/G0kOAvcA9672WiRpXAZwSbpEJdkH7Jl3+HBV7V+BuTYCHw05tbO1l/Rqkb0/3fdaJGnSbEGRJEmSeuRDmJIkSVKPDOCSJElSjwzgkiRJUo8M4JIkSVKPDOCSJElSj/4GXXQUQaQYSdYAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,6))\n", + "sns.boxplot(data = df_year.groupby(\"date_time_future\", as_index = False).first().sort_values(\"date_time_future_year\"), \n", + " x=\"date_time_future_year\", y = \"total_demand\", palette = 'Blues', showfliers = False)\n", + "plt.grid(alpha = 0.5)\n", + "plt.title(\"Year vs Total Demand\");\n", + "\n", + "time_decomposition_error_plots(df = df_year, x = \"date_time_future_year\", time_interval = \"Year\",\n", + " show_outliers = False, forecast_interval = 12, show_relative_error_all = True, show_relative_error_interval = False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/W4 Modelling.ipynb b/src/W4 Modelling.ipynb new file mode 100644 index 000000000..363a1a252 --- /dev/null +++ b/src/W4 Modelling.ipynb @@ -0,0 +1,336 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import statsmodels.api as sm\n", + "import warnings\n", + "\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from matplotlib.pyplot import figure\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "from statsmodels.graphics.api import qqplot\n", + "\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read and format data\n", + "path = 'data/combined_data_new.csv'\n", + "df_all = pd.read_csv(path)\n", + "df_all = df_all.loc[df_all.Temperature.notna()]\n", + "\n", + "df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded\n", + "df_all[\"forecast_error\"] = df_all.total_demand - df_all.forecast_demand\n", + "df_all[\"forecast_error_relative\"] = df_all.forecast_error/df_all.total_demand\n", + "\n", + "df_all[\"date_time_future_month\"] = df_all.date_time_future.dt.month\n", + "df_all[\"date_time_future_year\"] = df_all.date_time_future.dt.year\n", + "df_all[\"date_time_future_weekday\"] = df_all.date_time_future.dt.dayofweek\n", + "df_all[\"date_time_future_hour\"] = df_all.date_time_future.dt.hour\n", + "#df_all[\"date_time_future_yearTime\"] = df_all.date_time_future_year.apply(lambda x: pd.DateOffset(years=x-2000))\n", + "\n", + "df_all[\"week_day_name\"] = df_all.date_time_future.dt.day_name()\n", + "\n", + "df_all[\"isSaturday\"] = df_all.week_day_name.apply(lambda x: 1 if x == 'Saturday' else 0)\n", + "df_all[\"isSunday\"] = df_all.week_day_name.apply(lambda x: 1 if x == 'Sunday' else 0)\n", + "\n", + "df_all[\"isDecember\"] = df_all.date_time_future_month.apply(lambda x: 1 if x == 12 else 0)\n", + "df_all[\"isJanuary\"] = df_all.date_time_future_month.apply(lambda x: 1 if x == 1 else 0)\n", + "df_all[\"isFebruary\"] = df_all.date_time_future_month.apply(lambda x: 1 if x == 2 else 0)\n", + "df_all[\"isNovember\"] = df_all.date_time_future_month.apply(lambda x: 1 if x == 11 else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Regression\n", + "Forecast interval = 12h\n", + "\n", + "Check: https://realpython.com/linear-regression-in-python/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Existing forecast model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "delta = 24\n", + "\n", + "df_lag = df_all.loc[df_all.period_id == delta].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "df_lag_temp = df_lag.copy()[[\"forecast_error\", \"forecast_error_relative\", \"date_time_future\"]].rename({\"forecast_error\" : \"forecast_error_24h_ago\", \n", + " \"forecast_error_relative\": \"forecast_error_relative_24h_ago\",\n", + " \"date_time_future\": \"date_time_future_24h_ago\"}, axis = 1)\n", + "df_lag[\"date_time_current_24h_ago\"] = df_lag.date_time_current - pd.DateOffset(hours = 24)\n", + "df_lag[\"date_time_future_24h_ago\"] = df_lag.date_time_future - pd.DateOffset(hours = 24)\n", + "\n", + "df_lag = df_lag.loc[df_lag.date_time_future_24h_ago >= min(df_lag.date_time_future)]\n", + "df_lag = pd.merge(df_lag, df_lag_temp, on = \"date_time_future_24h_ago\", how = 'left')\n", + "df_lag = df_lag.loc[df_lag.forecast_error_relative_24h_ago.notna()]\n", + "\n", + "train_test_split = 0.7\n", + "split_int = int(train_test_split * len(df_lag))\n", + "df_lag_train, df_lag_test = df_lag[:split_int], df_lag[split_int:]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Existing model MSE = 55159\n", + "Existing model MAPE = 2.19%\n" + ] + } + ], + "source": [ + "mse = mean_squared_error(df_lag_test.forecast_demand, df_lag_test.total_demand)\n", + "mape = mean_absolute_percentage_error(df_lag_test.forecast_demand, df_lag_test.total_demand)\n", + "\n", + "print(f\"Existing model MSE = {round(mse)}\")\n", + "print(f\"Existing model MAPE = {round(100*mape,2)}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Regression\n", + "- Forecast Error from 24h ago" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.113\n", + "Model: OLS Adj. R-squared: 0.113\n", + "Method: Least Squares F-statistic: 2696.\n", + "Date: Mon, 21 Apr 2025 Prob (F-statistic): 0.00\n", + "Time: 18:58:17 Log-Likelihood: -1.4233e+05\n", + "No. Observations: 21064 AIC: 2.847e+05\n", + "Df Residuals: 21062 BIC: 2.847e+05\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==========================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "const 10.5613 1.438 7.346 0.000 7.743 13.379\n", + "forecast_error_24h_ago 0.3369 0.006 51.927 0.000 0.324 0.350\n", + "==============================================================================\n", + "Omnibus: 2737.590 Durbin-Watson: 0.201\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 23155.543\n", + "Skew: -0.344 Prob(JB): 0.00\n", + "Kurtosis: 8.090 Cond. No. 222.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\n", + "New model MSE = 49348\n", + "New model MAPE = 2.06%\n" + ] + } + ], + "source": [ + "x_columns = [\"forecast_error_24h_ago\"]\n", + "x = sm.add_constant(df_lag_train[x_columns])\n", + "x = sm.add_constant(x)\n", + "y = np.array(df_lag_train.forecast_error)\n", + "\n", + "model = sm.OLS(y, x)\n", + "results = model.fit()\n", + "print(results.summary())\n", + "\n", + "df_lag_test[\"lm_forecast_error_pred\"] = results.predict(sm.add_constant(df_lag_test[x_columns]))\n", + "df_lag_test[\"lm_forecast_demand_new\"] = df_lag_test.forecast_demand + df_lag_test.lm_forecast_error_pred\n", + "\n", + "mse_lm1 = mean_squared_error(df_lag_test.lm_forecast_demand_new, df_lag_test.total_demand)\n", + "mape_lm1 = mean_absolute_percentage_error(df_lag_test.lm_forecast_demand_new, df_lag_test.total_demand)\n", + "\n", + "print(f\"\\nNew model MSE = {round(mse_lm1)}\")\n", + "print(f\"New model MAPE = {round(100*mape_lm1,3)}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Regression\n", + "- Forecast Error from 24h ago\n", + "- Forecast temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.124\n", + "Model: OLS Adj. R-squared: 0.124\n", + "Method: Least Squares F-statistic: 298.6\n", + "Date: Mon, 21 Apr 2025 Prob (F-statistic): 0.00\n", + "Time: 18:58:14 Log-Likelihood: -1.4220e+05\n", + "No. Observations: 21064 AIC: 2.844e+05\n", + "Df Residuals: 21053 BIC: 2.845e+05\n", + "Df Model: 10 \n", + "Covariance Type: nonrobust \n", + "==========================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "const -56.9012 10.488 -5.425 0.000 -77.458 -36.344\n", + "forecast_error_24h_ago 0.3306 0.006 51.021 0.000 0.318 0.343\n", + "Temperature -1.1963 0.296 -4.040 0.000 -1.777 -0.616\n", + "Humidity 0.9220 0.094 9.811 0.000 0.738 1.106\n", + "Wind_speed 6.7613 0.927 7.296 0.000 4.945 8.578\n", + "Rain -3.8983 2.807 -1.389 0.165 -9.399 1.603\n", + "isSaturday 29.1366 4.143 7.033 0.000 21.016 37.257\n", + "isSunday 8.2777 4.149 1.995 0.046 0.145 16.410\n", + "isDecember 20.6354 4.986 4.139 0.000 10.863 30.408\n", + "isJanuary 13.2394 5.233 2.530 0.011 2.982 23.497\n", + "isNovember 10.3352 4.832 2.139 0.032 0.865 19.805\n", + "==============================================================================\n", + "Omnibus: 2701.773 Durbin-Watson: 0.203\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 23691.264\n", + "Skew: -0.315 Prob(JB): 0.00\n", + "Kurtosis: 8.157 Cond. No. 1.67e+03\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 1.67e+03. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\n", + "New model MSE = 49718\n", + "New model MAPE = 2.08%\n" + ] + } + ], + "source": [ + "x_columns = [\"forecast_error_24h_ago\", \"Temperature\", \"Humidity\", \"Wind_speed\", \"Rain\", \"isSaturday\", \"isSunday\", \"isDecember\", \"isJanuary\", \"isNovember\"]\n", + "x = sm.add_constant(df_lag_train[x_columns])\n", + "x = sm.add_constant(x)\n", + "y = np.array(df_lag_train.forecast_error)\n", + "\n", + "model = sm.OLS(y, x)\n", + "results = model.fit()\n", + "print(results.summary())\n", + "\n", + "df_lag_test[\"lm2_forecast_error_pred\"] = results.predict(sm.add_constant(df_lag_test[x_columns]))\n", + "df_lag_test[\"lm2_forecast_demand_new\"] = df_lag_test.forecast_demand + df_lag_test.lm2_forecast_error_pred\n", + "\n", + "mse_lm2 = mean_squared_error(df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand)\n", + "mape_lm2 = mean_absolute_percentage_error(df_lag_test.lm2_forecast_demand_new, df_lag_test.total_demand)\n", + "\n", + "print(f\"\\nNew model MSE = {round(mse_lm2)}\")\n", + "print(f\"New model MAPE = {round(100*mape_lm2,2)}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model 1 MSE = 49348\n", + "Model 1 MAPE = 2.06%\n", + "\n", + "Model 2 MSE = 49718\n", + "Model 2 MAPE = 2.078%\n" + ] + } + ], + "source": [ + "print(f\"\\nModel 1 MSE = {round(mse_lm1)}\")\n", + "print(f\"Model 1 MAPE = {round(100*mape_lm1,3)}%\")\n", + "print(f\"\\nModel 2 MSE = {round(mse_lm2)}\")\n", + "print(f\"Model 2 MAPE = {round(100*mape_lm2,3)}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_lag_test[[\"date_time_future\", \"total_demand\", \"forecast_demand\", \"lm2_forecast_demand_new\"]].rename({\"lm2_forecast_demand_new\":\"lm_prediction\"}, axis = 1).to_csv(\"data/results_LM.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/W4 Timeseries.ipynb b/src/W4 Timeseries.ipynb new file mode 100644 index 000000000..fd65125e9 --- /dev/null +++ b/src/W4 Timeseries.ipynb @@ -0,0 +1,685 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import statsmodels.api as sm\n", + "\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from matplotlib.pyplot import figure" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read and format data\n", + "path = 'data/combined_data.csv' #change path\n", + "\n", + "df_all = pd.read_csv(path)\n", + "df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded\n", + "df_all[\"forecast_error\"] = df_all.total_demand - df_all.forecast_demand" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#df_all[\"forecast_interval\"] = df_all.period_id/2\n", + "#df_all.forecast_interval = df_all.forecast_interval.apply(lambda x: pd.Timedelta(hours=x))\n", + "df_all[\"forecast_error_relative\"] = df_all.forecast_error/df_all.total_demand\n", + "\n", + "df_all[\"date_time_future_month\"] = df_all.date_time_future.dt.month\n", + "df_all[\"date_time_future_year\"] = df_all.date_time_future.dt.year\n", + "df_all[\"date_time_future_weekday\"] = df_all.date_time_future.dt.dayofweek\n", + "df_all[\"date_time_future_yearTime\"] = df_all.date_time_future_year.apply(lambda x: pd.DateOffset(years=x-2000))\n", + "#df_all.date_time_future_yearTime = df_all.date_time_future - df_all.date_time_future_yearTime" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation = 0.17154013709423804\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "delta = 24\n", + "\n", + "df_all_delta = df_all.loc[df_all.period_id == delta].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + "x = df_all_delta.forecast_error_relative[delta:len(df_all_delta)]\n", + "y = df_all_delta.forecast_error_relative[0:len(df_all_delta)-delta]\n", + "\n", + "plt.figure(figsize = (12, 9))\n", + "plt.plot(np.array(x), np.array(y), '.', alpha = 0.3)\n", + "plt.plot(0,0, 'r.')\n", + "plt.xlim(-0.1, 0.1)\n", + "plt.ylim(-0.1, 0.1)\n", + "plt.grid(alpha = 0.5)\n", + "plt.xlabel('Relative Forecast Error at Time = t')\n", + "plt.ylabel('Relative Forecast Error at Time = t - {}h'.format(delta/2))\n", + "plt.title('Error of Current vs Previous Lag | Forecast Interval = {}h '.format(delta/2))\n", + "print(\"Correlation = {}\".format(np.corrcoef(x, y)[0,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(2,2, figsize = (18, 18))\n", + "\n", + "for i, delta in enumerate([12, 24, 36, 48]):\n", + " df_all_delta = df_all.loc[df_all.period_id == delta].sort_values(\"date_time_future\").reset_index(drop = True)\n", + " delta_24h_later = 48 - delta\n", + " previous_lag = 48\n", + "\n", + " x = df_all_delta.forecast_error_relative[previous_lag:len(df_all_delta)]\n", + " y = df_all_delta.forecast_error_relative[0:len(df_all_delta)-previous_lag]\n", + "\n", + " #plt.figure(figsize = (12, 9))\n", + " plt.subplot(2,2,i+1)\n", + " plt.plot(np.array(x), np.array(y), '.', alpha = 0.3)\n", + " #plt.plot(0,0, 'r.')\n", + " plt.xlim(-0.15, 0.15)\n", + " plt.ylim(-0.15, 0.15)\n", + " plt.grid(alpha = 0.5)\n", + " plt.xlabel('Relative Forecast Error at Time = t')\n", + " plt.ylabel('Relative Forecast Error at Time = t - 24h')\n", + " plt.title('Error of Current vs Lag 24 ago | Forecast Interval = {}h\\nCorrelation = {} '.format(round(delta/2), round(np.corrcoef(x, y)[0,1],3)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "periods = []\n", + "corr_timestep_1 = []\n", + "corr_timestep_2 = []\n", + "corr_timestep_3 = []\n", + "\n", + "for period_id in set(df_all.period_id):\n", + " periods = np.concatenate([periods, [period_id/2]]) \n", + " \n", + " df_all_delta = df_all.loc[df_all.period_id == period_id].sort_values(\"date_time_future\").reset_index(drop = True)\n", + " \n", + " try:\n", + " first_lag_time = df_all_delta.date_time_future.iloc[0] + pd.DateOffset(hours = period_id/2)\n", + " first_lag_time_index = df_all_delta.loc[df_all_delta.date_time_future == first_lag_time].index[0]\n", + "\n", + " second_lag_time = df_all_delta.date_time_future.iloc[0] + pd.DateOffset(hours = 2*period_id/2)\n", + " second_lag_time_index = df_all_delta.loc[df_all_delta.date_time_future == second_lag_time].index[0]\n", + " \n", + " third_lag_time = df_all_delta.date_time_future.iloc[0] + pd.DateOffset(hours = 3*period_id/2)\n", + " third_lag_time_index = df_all_delta.loc[df_all_delta.date_time_future == third_lag_time].index[0]\n", + " \n", + " except: pass\n", + " \n", + " x1 = df_all_delta.forecast_error_relative[first_lag_time_index : len(df_all_delta)]\n", + " y1 = df_all_delta.forecast_error_relative[0 : len(df_all_delta) - first_lag_time_index]\n", + " \n", + " x2 = df_all_delta.forecast_error_relative[second_lag_time_index : len(df_all_delta)]\n", + " y2 = df_all_delta.forecast_error_relative[0 : len(df_all_delta) - second_lag_time_index]\n", + " \n", + " x3 = df_all_delta.forecast_error_relative[third_lag_time_index : len(df_all_delta)]\n", + " y3 = df_all_delta.forecast_error_relative[0 : len(df_all_delta) - third_lag_time_index]\n", + " \n", + " corr_timestep_1 = np.concatenate([corr_timestep_1, [np.corrcoef(x1, y1)[0,1]]])\n", + " corr_timestep_2 = np.concatenate([corr_timestep_2, [np.corrcoef(x2, y2)[0,1]]])\n", + " corr_timestep_3 = np.concatenate([corr_timestep_3, [np.corrcoef(x3, y3)[0,1]]])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Correlation')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAFzCAYAAAB2A95GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3iUVfbA8e9N7yEJaST03iEgNSAoCAqCioBYkF0LNiysLvqzYVtdVhAbKmDFiiIrqBRFUHoJCT30klDSCySkzv398SYsJSQzyUxmJpzP89xnmJm3HFDj4b73nqO01gghhBBCiNrlYu8AhBBCCCGuRJKECSGEEELYgSRhQgghhBB2IEmYEEIIIYQdSBImhBBCCGEHkoQJIYQQQtiBm70DsFT9+vV1kyZN7B2GEEI4LpPJeHWRv2cLYW9xcXHpWuvQir5zuiSsSZMmbNmyxd5hCCGEEEJUSSl19HLfyV+ThBCirpk1yxhCCIcmSZgQQtQ18+cbQwjh0CQJE0IIIYSwA6dbEyaEEEKI6ikuLiY5OZmCggJ7h1LneHl5ER0djbu7u9nnSBImhBBCXCGSk5Px9/enSZMmKKXsHU6dobUmIyOD5ORkmjZtavZ58jhSCCGEuEIUFBQQEhIiCZiVKaUICQmxeIZRZsKEEKKuWbXK3hEIByYJmG1U589VZsKEEEIIUWv8/Pzsev9FixbxxhtvWHTO999/T/v27XFxcbFqrVKZCRNCiLrmzTeN1yeftG8cQjigESNGMGLECIvO6dChAz/++CMTJ060aiwyEyaEEHXNzz8bQwgnsXjxYnr27EnXrl0ZNGgQKSkpAKSlpTF48GBiYmKYOHEijRs3Jj09vUb3+uyzz3jkkUcsOqdt27a0bt26RvetiMyECSGEEFeglxbvYveJXKtes12DAF68sb3F58XGxrJhwwaUUsydO5dp06Yxffp0XnrpJa655hqeeeYZli5dyuzZsys8f+zYsezdu/eSzydPnsz48eMtjqe2SBJ2kfQzhWxPzqZH0xD8POWPRwghhLC15ORkxo4dy8mTJykqKjpX5mHNmjUsXLgQgKFDhxIUFFTh+d99912txWpNkmVcJO5oFhPnxfHzpFg6RAXaOxwhhBDCJqozY2UrkyZNYvLkyYwYMYJVq1YxdepUwKi/ZQ5rzIQNGTKElJQUunfvzty5c82OvSYkCbtIsK8HAFn5RXaORAghqsnb294RCGGRnJwcoqKiAPj888/PfR4bG8v8+fOZMmUKy5cvJysrq8LzrTETtmzZshpfw1KyMP8iQT5Gu4HMPEnChBBOaskSYwjhgPLz84mOjj43ZsyYwdSpUxk9ejT9+vWjfv3654598cUXWb58OTExMSxZsoTIyEj8/f1rPeaFCxcSHR3N+vXrGTZsGEOGDLHKdWUm7CJBPmUzYZKECSGEEFZnMpkq/HzkyJGXfBYYGMiyZctwc3Nj/fr1rFy5Ek9Pzxrdf8KECUyYMMGic26++WZuvvnmGt23IpKEXSTQ2x2lIDO/2N6hCCFE9bzyivH6/PP2jUOIGjp27BhjxozBZDLh4eHBnDlz7B2SVUkSdhE3VxcCvd3JljVhQghntWKF8SpJmHByLVu2JD4+3t5h2IysCatAsI+HrAkTQgghhE1JElaBIF8P2R0phBBCCJuSJKwCQT4eZObJmjAhhBBC2I4kYRUI8nGX3ZFCCOcVEmIMIYRDkySsAsG+HmTmF5ldqVcIIRzKggXGEMIB+fn52fX+ixYt4o033rDonKeeeoo2bdrQqVMnbr75ZrKzs60SiyRhFQjy9aCoxMTZ4lJ7hyKEEEIIKxoxYgRPP/20RecMHjyYnTt3sn37dlq1asXrr79ulVgkCatAcFnBVtkhKYRwSs88YwwhnMTixYvp2bMnXbt2ZdCgQaSkpACQlpbG4MGDiYmJYeLEiTRu3Jj09PQa3euzzz7jkUceseic6667Djc3o6pXr169SE5OrlEM5aROWAWCyvtH5hUTXXHDdiGEcFzr19s7AuEMljwNp3ZY95oRHeF6yx71gdEjcsOGDSilmDt3LtOmTWP69Om89NJLXHPNNTzzzDMsXbqU2bNnV3i+NRp4m+uTTz5h7NixVrmWJGEVCPYt6x8pZSqEEEIIm0tOTmbs2LGcPHmSoqIimjZtCsCaNWtYuHAhAEOHDiUoqOKZEWs08DbHa6+9hpubG3fccYdVridJWAXqSf9IIYQQdV01ZqxsZdKkSUyePJkRI0awatUqpk6dCmD2BjlrzIQNGTKElJQUunfvzty5cy/5/vPPP+fnn39mxYoVKKXMumZVJAmrgKwJE0IIIWpPTk4OUVFRgJHslIuNjWX+/PlMmTKF5cuXk5WVVeH51pgJW7Zs2WW/W7p0Kf/+97/5888/8fHxqfG9ytl0Yb5SaqhSaq9S6oBS6pKtCEqpCUqpNKVUQtm415bxmCvA2x0XhfSPFEI4p+hoYwjhgPLz84mOjj43ZsyYwdSpUxk9ejT9+vWjfv3654598cUXWb58OTExMSxZsoTIyEj8/f1rPeZHHnmE06dPM3jwYLp06cIDDzxglevabCZMKeUKvA8MBpKBzUqpRVrr3Rcd+p3W2rJtCjbm6qKo5+Mha8KEEM7pyy/tHYEQl2UymSr8fOTIkZd8FhgYyLJly3Bzc2P9+vWsXLkST0/PGt1/woQJTJgwwaJzDhw4UKN7Xo4tH0f2AA5orQ8BKKW+BUYCFydhDsmomi+ti4QQQgh7OXbsGGPGjMFkMuHh4cGcOXPsHZJV2TIJiwKSznufDPSs4LhRSqn+wD7gCa110sUHKKXuB+4HaNSokQ1CvVSwr4esCRNCOKfHHzdeZ860bxxC1FDLli2Jj4+3dxg2Y8s1YRVtHbh4m8NioInWuhPwO/D5paeA1nq21rq71rp7aGiolcOsWD0fD7LkcaQQwhklJBhDCOHQbJmEJQMNz3sfDZw4/wCtdYbWurDs7Rygmw3jsUiwj8yECSGEEMJ2bJmEbQZaKqWaKqU8gNuARecfoJSKPO/tCGCPDeOxSJCvB9n5xdLEWwghhBA2YbM1YVrrEqXUI8AywBX4RGu9Syn1MrBFa70IeFQpNQIoATKBCbaKx1LBvu4UlZrIKyrFz1PKqQkhhBDCumyaXWitfwV+veizF8779TOAQ3aZDTqvar4kYUIIp9Kqlb0jEOKy/Pz8OHPmjN3uv2jRInbv3s3TT19SvvSynn/+eX766SdcXFwICwvjs88+o0GDBjWOxabFWp1ZsK9UzRdCOKnZs40hhLjEiBEjLErAAJ566im2b99OQkICw4cP5+WXX7ZKLJKEXUZQeRImOySFEEIIm1q8eDE9e/aka9euDBo0iJSUFADS0tIYPHgwMTExTJw4kcaNG5Oenl6je3322Wc88ohlNeIDAgLO/TovL096R9pakDTxFkI4q/vvN15lNkxU4t+b/k1iZqJVr9kmuA1Tekyx+LzY2Fg2bNiAUoq5c+cybdo0pk+fzksvvcQ111zDM888w9KlS5l9mX+nrdHAuyrPPvssX3zxBYGBgaxcudIq15Qk7DKkibcQwmnt22fvCISwSHJyMmPHjuXkyZMUFRXRtGlTANasWcPChQsBGDp0KEFBQRWeb40G3lV57bXXeO2113j99dd57733eOmll2p8TUnCLsPfyw1XF0V2vrQuEkIIUfdUZ8bKViZNmsTkyZMZMWIEq1atYurUqQBml4myxkzYkCFDSElJoXv37sydO/eyx91+++0MGzZMkjBbcnFRBPm4y5owIYQQwsZycnKIiooC4PPP/9c8JzY2lvnz5zNlyhSWL19OVlZWhedbYyZs2bJll/1u//79tGzZEjB2V7Zp06bG9wNJwioV5OMha8KEEEIIK8rPzyc6Ovrc+8mTJzN16lRGjx5NVFQUvXr14vDhwwC8+OKLjBs3ju+++46rr76ayMhI/P39az3mp59+mr179+Li4kLjxo358MMPrXJdScIqESRNvIUQzqhLF3tHIMRlmUymCj8fOXLkJZ8FBgaybNky3NzcWL9+PStXrsTT07NG958wYQITJkyw6JwFCxbU6J6XI0lYJYJ83DmcnmfvMIQQwjIzZ9o7AiGs4tixY4wZMwaTyYSHhwdz5syxd0hWJUlYJYJ9PYg7mm3vMIQQQogrUsuWLYmPj7d3GDYjxVorEeTjQXZ+kTTxFkI4lzvvNIYQwqHJTFglgn09KDFpTheWEODlbu9whBDCPMnJ9o5ACGEGmQmrhFTNF0IIIYStSBJWCWniLYQQQghbkSSsEuVNvLOkYKsQQghhFUlJSQwcOJC2bdvSvn173n777Qu+f/PNN1FKnWvUPXXqVN588017hGpzsiasEkE+xjqwzDxpXSSEcCK9e9s7AiEuy83NjenTpxMTE8Pp06fp1q0bgwcPpl27diQlJfHbb7/RqFEje4dZK2QmrBLlM2HZMhMmhHAmr79uDCEcUGRkJDExMQD4+/vTtm1bjh8/DsATTzzBtGnTUEpdcM7u3bsZMGAAzZo145133qn1mG1FZsIq4e/phpuLkjVhQggh6qYBAy79bMwYeOghyM+HG2649PsJE4yRng633nrhd6tWWXT7I0eOEB8fT8+ePVm0aBFRUVF07tz5kuMSExNZuXIlp0+fpnXr1jz44IO4uzt/1QJJwiqhlCLI10PWhAkhnMuoUcarjVqtCGENZ86cYdSoUcycORM3Nzdee+01li9fXuGxw4YNw9PTE09PT8LCwkhJSbmg/6SzkiSsCsE+0j9SCOFkMjLsHYFwFpXNXPn4VP59/foWz3yVKy4uZtSoUdxxxx3ccsst7Nixg8OHD5+bBUtOTiYmJoZNmzYBXNAv0tXVlZKSkmrd19FIElaFIF93smRhvhBCCGEVWmvuuece2rZty+TJkwHo2LEjqamp545p0qQJW7ZsoX79+vYKs1bIwvwqBPl4kCmPI4UQQgirWLt2LfPmzeOPP/6gS5cudOnShV9//dXeYdmFzIRVIcjXQ3ZHCiGEEFYSGxtbZU/mI0eOnPv11KlTL/hu586dNojKPiQJq0KwjwdZ+cWYTBoXF1X1CUIIYW/XXmvvCIQQZpAkrApBvh6UmjSnC0oI9HH+7bBCiCvA88/bOwIhhBlkTVgVgn3LqubLI0khhBBCWJEkYVUI8pEm3kIIJ3P99cYQogJVrccS1VOdP1dJwqpQnoRlSRImhHAWZ88aQ4iLeHl5kZGRIYmYlWmtycjIwMvLy6LzZE1YFYLL+kdK1XwhhBDOLjo6muTkZNLS0uwdSp3j5eVlcRV/ScKqECRJmBBCiDrC3d2dpk2b2jsMUUYeR1bB18MVD1cXMqVqvhBCCCGsSGbCqmA08XaXNWFCCOcxfLi9IxBCmEGSMDNI6yIhhFN58kl7RyCEMIM8jjRDsK+HzIQJIYQQwqokCTNDkI+HLMwXQjiPAQOMIYRwaJKEmSHI152sfFmYL4QQQgjrkSTMDME+HmTnF1FqkuJ2QgghhLAOScLMEOTrgUlD7lmZDRNCCCGEdUgSZobyqvmyQ1IIIYQQ1iIlKsxwQf/IUDsHI4QQVRkzxt4RCCHMIEmYGc4lYbI4XwjhDB56yN4RCCHMII8jzRDk6w4gtcKEEM4hP98YQgiHJjNhZpA1YUIIp3LDDcbrqlV2DUMIUTmZCTODt7srnm4uMhMmhBBCCKuRJMwMSimCfT3IlCRMCCGEEFZi0yRMKTVUKbVXKXVAKfV0JcfdqpTSSqnutoynJqR1kRBCCCGsyWZJmFLKFXgfuB5oB4xTSrWr4Dh/4FFgo61isYZgXw/ZHSmEEEIIq7HlwvwewAGt9SEApdS3wEhg90XHvQJMA560YSw1Vs/HnRMnzto7DCGEqNqECfaOQAhhBlsmYVFA0nnvk4Ge5x+glOoKNNRa/6yUumwSppS6H7gfoFGjRjYItWrBvh6yO1II4RwkCRPCKdhyTZiq4LNzHbCVUi7AW8A/qrqQ1nq21rq71rp7aKh9StYH+XiQc7aYklKTXe4vhBBmS083hhDCodkyCUsGGp73Pho4cd57f6ADsEopdQToBSxy1MX5wb4eaA050sRbCOHobr3VGEIIh2bLJGwz0FIp1VQp5QHcBiwq/1JrnaO1rq+1bqK1bgJsAEZorbfYMKZqC/Itb10kjySFEEIIUXM2S8K01iXAI8AyYA8wX2u9Syn1slJqhK3uayvBZf0jM/NkJkwIIYQQNWfTtkVa61+BXy/67IXLHDvAlrHUVD2fsv6RMhMmhBBCCCuQivlmKu8fKa2LhBBCCGEN0sDbTEE+0sRbCOEkHnzQ3hEIIcwgSZiZvD1c8XZ3lZkwIYTjGzvW3hEIIcwgjyMtYDTxloX5QggHl5RkDCGEQ5OZMAsE+3qQkVdo7zCEEKJyd91lvK5aZdcwhBCVk5kwC4QHeHIqp8DeYQghhBCiDpAkzALhAV6k5EoSJoQQQoiakyTMAhEBXmTlF1NQXGrvUIQQQgjh5CQJs0BEoBcAqbmyLkwIIYQQNSML8y1QnoSdyi2gUYiPnaMRQojL+Mc/7B2BEMIMkoRZICLASMJO5py1cyRCCFGJG2+0dwRCCDPI40gLhJfNhMnifCGEQ9u71xhCCIcmM2EW8Pd0w8fDlVM5siZMCOHAJk40XqVOmBAOTWbCLKCUIiJQylQIIYQQouYkCbNQRIAXpyQJE0IIIUQNSRJmoYgAL6maL4QQQogakyTMQuFljyNNJm3vUIQQQgjhxGRhvoUiArwoMWky8ooI9fe0dzhCCHGp556zdwRCCDNIEmahiPPKVEgSJoRwSIMG2TsCIYQZ5HGkhcoLtsq6MCGEw0pIMIYQwqHJTJiFymfCTsoOSSGEo3r8ceNV6oQJ4dBkJsxC9f08cXVRpMhMmBBCCCFqQJIwC7m6KML8PaVWmBBCCCFqRJKwaggPkKr5QgghhKgZScKqQQq2CiEcmUlDqdQyFMLhycL8aogI9GLtgXR7hyGEEJdYdzCdee1HUWrSvH6mkBA/KaUjhKOSmbBqCA/w4nRhCXmFJfYORQghADhTWMKzC3dw+5yN7G7agVX1W/L4dwkyIyaEA5MkrBoiy8pUyOJ8IYQj+GtfGkPe+ouvNx3jvn5NWd7DjfebnGX1/nTe++OAvcMTQlyGPI6shvCygq0pOQU0D/WzczRCiCtVbkExr/28h++2JNE81JcfHuhDt8ZBMGAAg4BbHpzJzBX76N4kiL4t6ts7XCHERWQmrBoiZCZMCGFHWmuW7DjJdTP+4vu4JB64ujm/PNrPSMDKKODVmzvQItSPx76Nlx3dQjggScKqobx10UnZISmEqGWH0/O4+9PNPPjVVur5uLPwob48fX0bvNxdLznWx8OND+6MIa+wlElfx1NSarJDxEKIy5EkrBq8PVwJ8HKTv1kKIWpNQXEpM37bx5C3/mLr0SxeGN6OnyfF0rlhvUrPaxHmz+u3dGTTkUzeXL6vlqIVQphD1oRVU2Sgt9QKE0LUipWJqby4aBfHMvMZ2aUBz97QlrCyGXlz3NQ1ik1HMvnwz4Nc1SSIa9uG2zBaIYS5JAmrpvBAqZovLFCUD6YS8AqwdyTCiaSdLuTFRTv5dccpmof68vW9PeljzgL7mTMv+eiF4e3YlpTN5Pnb+OXRWKKDfGwQsRDCEvI4spoiAqR/pDDTmTT4oDd8MhRMpfaORjgBrTUL45MZ/Naf/L4nlaeGtGbJY/3NS8AAunQxxnm83F2ZdUcMpSbNk99vwyT1w4SwO0nCqikiwIu004Wy0FVUrigPvh4D2UmQugu2fWvviISDO5lzlns+38IT322jaX1ffn00locHtsDDzYIf17//boyLNA7x5fnhbdlwKJPP1h2xXtBCiGqRJKyawgO9MGlIO1No71CEozKVwoJ74UQ8jPkCGsTAqtehWGZQxaW01ny3+RjXzfiLdQfTeX54O354oA8twvwtv9irrxqjAmO6N+SaNmH8e2kiB1LP1DBqIURNSBJWTeeq5svifFERrWHJFNj7K1w/DdoOh0EvQk4SbPnE3tEJB5OUmc/4TzYxZcEO2jUIYOlj/bkntimuLsrq91JK8cYtHfH2cOUf32+T2Xwh7EiSsGo6VzVf1oWJiqx7FzbPgT6ToOf9xmfNBhhj9ZtQeNp+sQmHUVJqYu7qQ1xXVnbilZHt+ea+XjSp72vT+4YFePHqTR3YlpTNh38etOm9hBCXJ0lYNUnBVtt6Z+s7fLjtQ3uHUT07f4Tfnof2N8Ogly/87toXID8D1r9vn9iEw9h9IpdbPljHq7/soU/zEH6bfDV39W6Ciw1mvyoyvFMDhneK5O0V+9l1IqdW7imEuJAkYdUU7OuBh6uL7JC0gYyzGXy661O+2P0FJaYSe4djmaPrYOFEaNQHbvoQXC76TyyqG7QbacyU5aXbJ0ZhVwXFpfx7aSI3vreGE9lneXdcV+be3Z0G9bxrPZZXRnagno8Hk7/bRmGJ7NwVorZJElZNSinCAjxJkZkwq1t0cBElphJOF51me9p2e4djvpzj8M04qNcYbvsK3C9TTHPgc1CcD6un1258wu42Hspg6My/+GDVQW7pGsXvk6/mxs4NUMrKs18ffWSMKgT5ejBtVCf2ppzmrd/2WzcGIUSVJAmrgchAL5kJszKtNT/u/5E2wW1wVa6sOb7G3iGZb907RkmK278Dn+DLHxfaCrrcAZvnQvax2otP2E1BcSmv/Lyb2+ZswKThq3t78p/Rnann42GbG7ZubQwzDGwTxm1XNWT2XweJO5ppm3iEEBWSJKwGwgO8SMmVEhXWFJcSx5HcI9zZ9k66hHVh9fHV9g7JPHkZEPc5dBoDIc2rPn7A04CCVW/YPDRhX9uTsxn+7ho+XnOYu3o1Zunj/ehrbtHV6lq82Bhmem54OxrU8+bJ77dTUCyPJYWoLWYlYUopT6XU7Uqp/1NKvVA+bB2co4sI8OJkzlm0lsrT1rJg/wL83P0Y3HgwsVGxJGYmkpafZu+wqrZpNpSchb6PmXd8YDT0uA+2fQOpibaNTdhFcamJt37bx82z1nGmoIR59/Tg5ZEd8PGohW5x06cbw0x+nm786+aOHE7PY/Zfh2wYmBDifObOhP0EjARKgLzzRqWUUkOVUnuVUgeUUk9X8P0DSqkdSqkEpdQapVQ7S4K3t4hALwqKTeSedbLF4w4qpzCH347+xrBmw/Bx96FfVD8Ax38kWZQHmz6C1sMg1LxHQADETgZ3X/jjFdvFJuxif8ppbpm1jrdX7Gdk5wYse6I//VqG2jusSvVvFcqwjpG8v/IAxzLy7R2OEFcEc5OwaK31WK31NK319PJR2QlKKVfgfeB6oB0wroIk62utdUetdRdgGjDD0t+APZXXCpN1Ydbx86GfKSwtZFTLUQC0CmpFmE+Y4z+S3PoFnM2C2MctO883BPo+Cok/w4kE28QmapXJpJm7+hDD3l3D8eyzfHhnDDPGdiHQ293eoZnl+eHtcHNRTF28S2b4hagF5iZh65RSHS28dg/ggNb6kNa6CPgWYzbtHK117nlvfQGn+q/+XNV8ScJqTGvNgv0LaBfSjrYhbQFjB2psVCwbTmyg2FRs5wgvo7QY1r1nlKRo2MPy83tOBDcvI5ETTi05K59xczbw6i976N8ylGWP92doh0h7h2WRiEAvnhjcij8SU/ltd4q9wxGizjM3CYsF4soeLW4ve4RYVe2AKCDpvPfJZZ9dQCn1sFLqIMZM2KMVXUgpdb9SaotSaktamuOsDzpXNV/KVNTYjvQd7M/af24WrFy/qH6cLj7NttRtdoqsCjt+gNxkiH2ieud7BUKb4bBzAZTIJg9npLXm+y1JDJ25ml0ncpl2ayfmjO9GqL+nvUOrlrv7NKFNhD8vLd5NfpEstRDClsxNwq4HWgLXATcCw8teK1NR4ZtLZrq01u9rrZsDU4DnKrqQ1nq21rq71rp7aKjjrKsIl6r5VrNg/wK83by5oekNF3zeK7IXbsrNMdeFmUyw9m0Iaw8tB1f/Ol3GQUE27F1ivdhErUg/U8jEeXE89cN22jcIYMlj/RjTvaH1635Zat48Y1SDu6sLr9zUgePZZ3n3jwNWDkwIcT6zkjCt9VGgHkbidSNQr+yzyiQDDc97Hw2cqOT4b4GbzInHUXi4uRDi6yGPI2sorziPJYeXMKTJEPw8/C74zs/Dj67hXR1zXdj+ZZC2x9gRWZP/6TYbCP6Rxk5J4TSW7jzF0Jl/sWpfGs8Na8s39/WiYbCPvcMyNGxojGq6qkkwt3aLZu7qQxxIlT6nQtiKuSUqHgO+AsLKxpdKqUlVnLYZaKmUaqqU8gBuAxZddN2W570dBjhdyeaIQC9p4l1DSw4v4WzJ2UseRZaLjYplX9Y+UvIcbI3KmpkQ2Ag63FKz67i4QqexsP83OJNqndiEzaTmFvDgl3E88GUc4QFe/Dwplnv7Nau1no9m+e47Y9TAM9e3wcfDjef/K4v0hbAVcx9H3gP01Fq/oLV+AegF3FfZCVrrEuARYBmwB5ivtd6llHpZKTWi7LBHlFK7lFIJwGTg7mr9LuwoIsCLU/I4skYW7FtAi3ot6BzaucLvy0tVrD2xtjbDqtzR9ZC0Afo8Aq5W2PnW5XbQpbB9fs2vJWxCa813m48xaMaf/JGYypShbfjvw31pFe5v79Au9cEHxqiBED9PnhrSmvWHMli0rbKHGEKI6jI3CVPA+WWUS6l4zdcFtNa/aq1baa2ba61fK/vsBa31orJfP6a1bq+17qK1Hqi13mXpb8DewmUmrEb2Zu5lZ8ZORrUcddl1NC3qtSDcJ5zVyQ70SHLtTPAJga53Wed6oa2hQYw8knRQR9LzuH3ORqYs2EHbyACWPt6fBwc0x921bjcdGdejEZ2jA3n1lz3kFjjoDmUhnJi5P0E+BTYqpaYqpaYCG4CPbRaVE4kI8CIjr4jCEmn1UR0/7PsBDxcPbmx++X0eSin6Rfdj/cn1jlGqImU37FsKPSaChxXXAHW5HVJ2wkknalpex5WaNB/+eZAhM/9i5/EcXr+lI9/c14um9X3tHVqtcHVRvHpTR9LPFDJj+T57hyNEnWPuwvwZwN+ATKkYahMAACAASURBVCAL+JvWeqYtA3MWEWU7JFOlh6TFzpac5ZdDv3Bt42sJ9Ays9NjYqFjyivNISHWAoqbr3gF3H6PtkDV1GAUu7jIb5iBScwu4c+5G3liSyNWtQvn9H1czrkcjx1r7VQs6RgdyR89GfLH+CLtO5Ng7HCHqlEqTMKVUQNlrMHAE+BKYBxwt++yKFyEFW6ttw4kNnC4+zc0tbq7y2F6RvXBzcbP/Lsnck7Dje+MxpI+V/xPwCYbW1xvrwkodYMbvCrb2QDo3vLOG+KQspt3aiY/u6nauJM2V6Knr2hDk48Hz/92JySSL9IWwlqpmwr4ue40Dtpw3yt9f8c4lYbI432LxqfG4u7gTEx5T5bG+7r50C+tm/3Vhm+eAqRR6PWCb63e5HfLT4cDvtrm+qFSpSTPjt33c+fFG6vm4s+iRWMeo+2WpH34whpUE+rjz9PVt2Hosmx/ikq12XSGudJUmYVrr4WWvTbXWzc4bTbXWzWonRMd2rmq+zIRZLD41nnYh7fB0Na+yeL/ofhzIPsCpvFM2juwyivJhyyfQZhgE2+hf/xaDwKc+JHxlm+uLyyp//PjOiv3c0jWaRY846M5Hc9SvbwwrGhUTTffGQbyxNJHs/CKrXluIK5W5dcJWmPPZlSjAyw1vd1epmm+hwtJCdmXsomtYV7PPiY2KBbDfI8lt3xiNuns/bLt7uLpDpzGwdynkZ9ruPuIC5z9+/M+tnZg+pjM+Hm72Dqv6PvvMGFbk4qJ45aYO5Jwt5j/L9lr12kJcqapaE+ZVtvarvlIqSCkVXDaaAA1qI0BHp5QiItBL1oRZaHfGbopNxXQJ62L2Oc0Cm9HAtwFrku3Qwshkgg0fQGQXaNTbtvfqPA5MxUY/SWFTpSbNWxc9fhzdvfqV5h2GDZIwgLaRAdzduwlfbzrGtqRsq19fiCtNVTNhEzHWf7Upey0fPwHv2zY05xER4CVNvC0UnxoPQJdQ85MwpRSxUbFsOLmB4tpeuH7gd8jYb8yC2Xp9UGQnCO8ICV9XfayottTTBdz18UbeXrGfm7tGOffjx1r0xOCWhPp58vxPOymVRfpC1EhVa8Le1lo3BZ48by1YU611Z631e7UUo8OTmTDLxafG0zigMSHeIRadFxsVS35JPglptVyqYv174N8A2tVSe9Mu4+DEVkiTxz62sO5gOsPeWcPWY8buxxljujj348da5O/lzrPD2rI9OYdvNh2zdzhCODVz64S9q5TqoJQao5QaXz5sHZyzCA/wIjW3ULZum0lrTUJqgkXrwcp1i+iGQhGXEmeDyC7j1E44/KdRF8zNo3bu2XE0KFeZDbOyUpPmnRX7uXPuRgK83PjpYWP3o7DMiM4N6N0shP8s20vGGamRKER1mbsw/0Xg3bIxEJgGjKj0pCtIRIAnRaUmMmXHkFkO5x4muzC7WklYgEcALYNasjVlqw0iu4wNHxjFWbtNqL17+oVBy+tg27dQWlJ7963DsvKKmPDpJmb8to8RnRuw6JFYWkfI48fqUErxyk3tyS8q4fUlifYORwinZW7boluBa4FTWuu/AZ0B8+oKXAEiAr0BOJF91s6ROIfyqveWLMo/X0xYDNvStlFiqoXk5Ewq7Jhv1O+ydnHWqnS9E86ckpphVnAg9Qw3zVrLxsOZvHFLR94a2wVfzzr8+PHXX41hQy3C/LmvXzN+iEtm9f40m95LiLrK3CTsrNbaBJSUVdFPBaROWJkWYX4A7E85Y+dInEN8ajz1POvRNKBptc6PCY8hvySfvVm1sF5q81woLYKeD9r+XhdrNQR8wyB+Xu3fuw75a18aN89aS15hCd/c14vbejRyvuKrlvLxMYaNPXptS5qF+vL0gh2cKZQZWyEsZW4StkUpVQ+Yg7E7ciuwyWZROZkmIT54uLmwL+W0vUNxCgmpCXQJ7VLt/xHGhBkV9m3+SLK4ADZ/DK2GQv0Wtr1XRVzdofNtRrPwM6m1f/864PN1R/jbZ5uJqufNfx/uS7fGQfYOqXbMmmUMG/Nyd+U/t3biRM5Zpi2Vx5JCWMrchfkPaa2ztdYfAoOBu8seSwrAzdWFFqF+JJ6SJKwqmQWZHMk9Uu1HkQDhvuFE+UXZPgnbMd9oIWTL4qxV6XoXmEqkqbeFiktNPP/fnby4aBcDW4ex4ME+RAfZfmbIYcyfb4xa0K1xMH/r05Qv1h9lw6GMWrmnsJN9y6FAmrhbU1XFWmMuHkAw4Fb2a1GmTYQ/eyUJq1L5erDqLMo/X7fwbmxN3YrWNtqRqjWsn2XU62rSzzb3MEdoK2jYE7bOM2ISVcrJL+Zvn25m3oajTLy6GR/d1a1ur/9yAE8NaU3jEB+mLNjO2aJSe4cjbCFtH3w9GlbPsHckdUpVM2HTKxlv2jY059Iqwp9TuQXk5NdyEVEnk5CagLuLO+3rt6/RdbqGdT03q2YTB1ZA2h7o/ZDti7NWpetdRqHYpI32jcMJHM8+y6gP17HxcAb/ubUTz1zfFleXOr7+ywF4e7jyxi2dOJqRz5vLpbZdnbRvqfG6Z5H8hdCKqirWOrCScU1tBekMyre675V1YZWytGn35cSEx5y7ntVpDX9Ng4Bo6HCr9a9vqfY3g4efMRsmLmvvqdOMmrWOlJwCvvh7z7rRfsiJ9G4ewp29GvHJ2sPEHc2ydzjC2vYtM14zD0HqbvvGUoeYWyfMRyn1nFJqdtn7lkqp4bYNzbm0DpckrCrVadp9OU0DmhLsFWyboq1H1hizTrGP115x1sp4+hmJ2K6FUCj/flVk0+FMRn+4DpPWzH+gN72bW9aJQVjH09e3pUGgN//8YRsFxfJYsiJFJSbeXbGfZxfuoKTUZO9wzHM2C46tN2blUbBnsb0jqjPM3R35KVAE9Cl7nwy8apOInFRkoBf+Xm7sPZVr71AcVnWadl+OUoquYV1tszh/9ZvgF27U6XIUMeOhOM9IxMQFlu06xZ0fb6S+vyc/PtSHtpEB9g7J/latMkYt8/N04/VbOnIwLY+3V+yv9fs7uu3J2Yx4bw3Tf9vHVxuP8YadCt2aTJoNhzLILzKzrMiBFaBLIeZuaNQLdi+ybYBXEHOTsOZa62lAMYDW+iwgCy3Oo5Sidbg/+05JrbDLqU7T7srEhMWQfCaZ1Hwrlm9I2gyHVkGfSeDubb3r1lT0VVC/lTySvMhXG4/y4JdxtIsM4IcHrrAdkA6qf6tQxnSP5qM/D7L1mDyWBCgoLuWNJYnc9P5asvKLmDu+OxP6NGHumsMsiEuu1Vg2Hc7k5llruW32BsbN2Ui2OZ1e9i0Fn/oQFQNtR0DqLsg4aPtgrwDmJmFFSilvQAMopZoD0jDsIq0j/Ek8lWu7HXtOrrpNuy+nfF2YVWfDVr8J3sHQzcEqsChlPApI3iRNvTH6j878fR/PLtzJ1a1C+fq+ngT7OsCjY0fx5pvGsJNnh7UjKsibB7+MIzW3wG5xOIItRzK54e3VfPjnQUZ3a8jyJ65mULtwnh3Wlt7NQnhm4Q62JWXbPI5DaWeYOG8LYz5aT0puIQ8PbM6eE7mMm7OR9Mr6f5aWwP7fjDZqLq7Q9kbj8z0yG2YN5iZhLwJLgYZKqa+AFcA/bRaVk2oT4U9uQQkpuZKfXkxrzbbUbVabBQNoE9wGbzdvtqZaKQk7uc34G1/vh4x1WI6m8zhwcYOtX9g7ErsqKC7lH/O3MfP3/dzaLZrZ47vj4yElKC7w88/GsJNAb3fmjO/O6YIS7p8Xd8WsDysqMXE0I4+1B9KZvzmJKT9sZ/RH6yksMTHvnh78+9ZOBHq7A+Du6sL7d8QQ5u/JxHlxpJ62TbKamVfE1EW7uO6tv1izP50nr2vFyicH8NSQNsy9uzuH088w9qP1nMq5zP2TN0FBNrQearyv1xAadJVHklZS5U8uZZQ1TwRuAXphPIZ8TGudbuPYnE6rssX5iadyiQj0snM0juVI7hGyCrOssii/nJuLG51DO1tvJmz1dPAMhB73W+d61uYXalTv3/YtXPuiY2waqGVppwu5f94W4o9lM3lwKyZd06LutyByUm0iApg+ujMPfrWV5/67k//c2qlO/bPSWrP7ZC6Ltp0g7kgWx7PPciq34ILqDW4uivG9GvPPoW0qrFUX7OvB7Lu6M+qDdTz45Va+vq8nnm6uVokv40wh8zYc5eM1h8krLGFcj0Y8PqgVof7/25nev1Uon/+tB3//bDNjPlrPV/f2pGHwRY/09y4BF3doNvB/n7UdASteguwkIykT1VZlEqa11kqp/2qtuwG/1EJMTutcmYpTpxnQOszO0TgWaxVpvVhMeAwfJHxAblEuAR41WJCdmmj8za7/k+AVaL0ArS1mPCT+bMzYtRth72hq1a4TOdz3+RYy84v44I4Yru8Yae+QRBWu7xjJo9e25J0V+2nfIIC/9a1ev1hHcijtDIu2nWDxthMcTMvD1UXRtWE9+jSvT3SQd9nwITrIm4hAL9xdK3/g1K5BAG+O7szDX2/lxZ928fotHWuUrB5MO8Pc1Yf5cWsyhSUmBrUNZ8rQ1rQsmyS4WM9mIXx1Xy/Gf7zxXCLWLPS8JwH7lkGTvuB13s/X8iQs8Wfo9SBxRzM5nl3AiM4Nqh33lcrcOfwNSqmrtNabbRqNk6vn40F4gKeUqajAuabdgdb9IRwTFoNGk5CaQP/o/tW/0JoZ4O5jn0bdlmh+LfhHQvyXV1QStmzXKR7/NoFAb3d+eKAPHaIcOFEWF3j82pYknszl1V/20Crcn74t6ts7JIvl5BfzfVwSC+OPs+tELkpBjybB/D22Kdd3iKzxesRhnSLZc7IF7608QPsGAdzVu4lF52ut2Xg4kzl/HWJFYioebi6MionintimtAirOPk6X5eG9fj2/t7c9fFGxny0ga/u7WlMKmQegvS90P2iNbL1W0BYO0y7F/HvrAHM/usQWht9lDtF17vg0ITUBFoHt8bbzYE2OjkQc5OwgcBEpdRRIA/jkaTWWneyWWROqnVEgLQvqkB8anyNmnZfTqfQTrgpN7ambK1+EpZ5CHZ8b/SI9HXw+lKubsbasLUzIfcEBNTtv3lqrZm16iD/WbaXzg3rMeeuboQFyKP+Knk7zv/wXFwUM8Z24ZZZa3n4660sejiWRiHOsYs1KTOfj9ccZv6WJPKLSukcHchzw9oyvFMDqy85mTy4FXtO5vLS4t0cTMujb4v69GgafG4N2cUKS0rZejSbtQfSWZGYyp6TuQT7evDYtS25q3dj6vtZVhC7XYMAvpvYizvmbmTk+2u47apGPO7/B/UAWg255Pi06OsI2fo2C/bFM6Z7B37fk8K/ft3DN/f1OvdzPv1sOuOXjGdS10nc1+k+S/9IrgjmJmHX2zSKOqR1uB+fH8qg1KSlXUqZrIIsjuQe4aYWN1n92t5u3rQLaVezyvlr3jLWPPSeZL3AbClmvBHz5rlw7Qv2jsZmiktNPL1gBwu2JjOicwOm3doJL3frrJep85YssXcEF/DzdGPO+O6MfH8t936xmR8f6oufA/fzjD+WxZzVh1i68xQuSjGiSwPujW1Guwa2q0Hn4qJ467YuPDl/G99uPsZn647goqBjVCC9m9enb4sQgnw8WHcwnTUHMth0OIOCYhOuLorO0YG8dnMHRsVE1+i/kRZh/vz4UF/e+m0fX244yiC3b2np3ZjTJWG0KDum1KSZu/oQP2+MZLG7Zl5sGm2Hd+KL9Ud44addrNqbxsA2xnKc3Rm70Wg2ntooSdhlmLMw3wX4RWvdoRbicXqtIwIoKjFxJCOP5qEOuMPODmy1HqxcTHgMX+35isLSQsvbIWUnQcI3xnS7f7hN4rO64KbQdjhs/hhiJzvmTs4aOltUysNfb+WPxFQeH9SSx65tWacWdV+JGof48v7tMYz/ZBOPfhPP+7fH4O3hGEl1YUkpiSdPsz05m58STrDlaBb+Xm7c3785E/o0qbWNVgFe7swe353CklISjmWz9mAG6w+mM3f1IT788391uVqG+XHbVY2IbVGfns2C8feqeLasOqLqefPm6M5M7h9J+IeJfHr2el5760+Gto9gzFUN+WDVQTYdzmRIu26UZjalbdYq4DHG9WjEp2uP8PqSPfRvFYqri2JvplFOZ1vqNopLi3F3tV6cdYU5C/NNSqltSqlGWutjtRGUMytvX7Tv1GlJwsrEp8ZbpWn35XQN68pnuz5jZ/pOuoV3s+zkde8AGvo8apPYbKbPo0brkPgvodcD9o7GqnLyi7nn883EHcvi1Zs6cGevxvYOyfm88orx+vzz9o3jIn1b1Oflke157r87uW32euaM726Xx8tH0vPYfCST7ck5bE/OZs/J0xSVtRBqGOzNC8PbMeaqhnabrfN0c6VnsxB6NguBwa3IKyxh05FMcs8W06tZCOG18GfWIGM96BJGj7uX7GORfL7+CEt2nsLP0403R3dmVEwU6vcRsP59OJuFu3cQ/xzSmge/2soPcUmMvaoRiZlGR4CC0gJ2ZeyySreUusbcf8MigV1KqU0Ya8IA0FpfOSuDzdQy3A8XBYmnTsvurTIJaQlWadp9OTFh/yvaalESlnsC4j431lg52zbrhj2gYS/Y8D5cda+xVqwOSMkt4O5PNnEw7QzvjYthWCf5b6haVqwwXh0sCQO4o2djQv08eezbBG56fy1z777Kpo/5zne6oJhpS/cyb8NRwHhM2jEqkL/FNqFzdD06N6xHg0Avh5t19fV0Y2Bt77jftwy8AglsFcuTbd2YeHUz/khMpVvjoP91pmg7Eta+DXuXQpdxDO0QQddG9Zjx2z5u7NyAxMxEYsJi2Jq6lbiUOEnCKmDuT+6XbBpFHeLl7kqTEF9ZnF+mqLSIXem7uL3t7Ta7Rz2vejQPbE5cahz3YcG6gzVvGf3Q+j9ls9hsqs8k+O4Oo3J1h1vsHU2NHU7P466PN5KVV8SnE3oQ29L5dtEJ81zXPoLvH+jNvZ9v4dYP1/HObV0Z1M62ywH+SEzh2YU7OZVbwN/7NuX2ng1pVt8PF1m7eylTqZGEtRh87i94/l7ujOwSdeFxDbpCQJTxM6jLOJRS/N8NbRn94Xo+/Gs3x04fY0TzEeQU5rAlZQv3dLzHDr8Zx2ZWxXyt9Z8YBVv9y8aess9EBVqF+7NPylQAsCdzD0WmIqtWyq9ITHgM21K3UWoyszJ3znGI+wy63AFBTvq4q/X1ENwc1r0LTt4qa+fxHEZ/uI78olK+ub+XJGBXgA5Rgfz0SF9ahPlx37wtzF19yCYt3zLOFPLYt/H8/bMt+Hu5seDBPrxwYztahPlLAnY5x7dCfrrxM6YyLi5GG6MDK6DQ6Jt8VZNgrmsXzieb1gFGZ5Nu4d2IT403/+fzFcSsJEwpNQbYBIwGxgAblVK32jIwZ9Y6wp8jGXlXTKuOypQvyu8c1tmm9+ka1pUzxWfYn73fvBPWzABtgn7/sGlcNuXiCn0egRNb4ehae0dTbSsTU7lt9gY83Vz5/oHel9QZEnVXeIAX393fm6HtI3j1lz3838Id5BeVWOXaWmt+SjjO4Lf+4tcdJ3l8UEt+ntSPmEZBVrl+nbZvKShXaH5N1ce2HQGlhbB/+bmP/jm0DcVuRmPyNsFt6B7RnbziPBKzEqu8XEFxKU99v41ftp+sdvjOxNzekc8CV2mt79Zajwd6AI632MBBtI7wx6ThQOoZe4did9vSthHtF019b9vObJSvBYtLiav64Jxko/9i1zuddxasXOdx4BNizIY5Ga01H/55kL9/vplGwT788GBv2cxiLSEhxnAC3h6uvH97DA8PbM43m5Lo/urvTJ6fwJr96ZSaLJ8Zy8kvZt6Go9z0/loe+zaBhsE+/DypH48PaoWHm7n/y7vC7VsGjXqBT3DVxzbqBb6hxkahMi3C/GgRnYMu8SUv3/fcut24U1X/fP7oz0N8H5fMw19vZeqiXRSVmKr923AG5q4Jc9Fap573PgPzE7grTnn7osRTp6/oyt5aa+JT4+kV2cvm92rg14AI3wjiUuK4o+0dlR+8eobx+M6ZZ8HKuXsbvS5XvQ5peyG0tb0jMktBcSlTFmznp4QTDOsUyX9u7SRNuK1pwQJ7R2ARFxfFU0PacE2bMH6IS+bn7Sf5cetxIgK8GNm1Abd0jT73c7UiJaUmVu9P54e4ZH7bnUJRqYk2Ef68elMHxvVoJDUbLZGdBCk7YPDL5h3v4gpthhs9bU+fAv8IADx9T0FOA95cvpdZd3SjoX9DtqRsYXz78Ze91NGMPN5fdYAbOkYQEeDNJ2sPsz05m/fviCEy0HEKEFuTuT/1liqllgHflL0fC/xqm5CcX+NgHzzcXK74dWEn8k6Qfjbd5uvByvWM6MkfSX9QbCrG3eUy9Wiyk/43C1avUa3EZXNX3WtsMlj/Hoxw/Bmxkzlnuf+LOHaeyOGpIa15aEBzh9uNJuyjW+NgujUO5sUb27NiTyoL45P5ePVhPvrzEJGBXgR6u+Pn6Yafl5vx6umGUvD7nlTSThcS7OvBHb0aMSommvYNAuTfK0toDen7YcMs432roeaf22cSxM+DVW/AjTMpNhVzJPcgnUJv4Nctp9h0OJPu4d35I+kPTNqEi7p0DkdrzdRFu3B3UbwwvD0RgV7ENK7HlB+2M/ydNbwzrqtTtryqSqVJmFKqBRCutX5KKXULEIvRsmg98FUtxOeU3FxdaBnmR+IVvkOyfD1YbW1LHthwID8d/In4lHh6RPao+KA1M4zXujALVs63vrHBIH4eDHzOoYvObjmSyQNfbqWguJQ5d3W3+Y64K9Yzzxivr79u3ziqycvdlWGdIhnWKZKMM4Us3naC7ck5nCks4UxhCZl5RRzLyOdMYQlni0vp1SyEW7tFM7B1mDxytMTZLDj0Jxz8wxg5ScbnLa+D+q3Mv05Ic+j+d6OAdO+HOeRiothUzK0de3LsoDcPf72Vh2/syMLChRzIPkCroEuvvXx3Civ3pvHcsLbniuMO79SANhEBPPhlHHd9vJHJg1vx0IAWdWpDRVUzYTOB/wPQWv8I/AiglOpe9t2NNo3OibUO92fdwQx7h2FXCakJ+Lj50KJei6oPtoLeDXrj4eLByqSVFSdh2cdg6zyIucv56oJVpffDsOUT2DQbrnW85ZqlJs0X64/wr1/3EFXPm2/u60nL8KobC4tqWr/e3hFYTYifJxP6NrV3GHVLQS7MHw+H/zQ2KHkGQNP+0G+ysRg/qInl1+z/T0j4Gn6fyt6rxgLQJbw9n0wIZdSsdXy5yg0CjXW7Fydh+UUlvLx4N63D/bm7z4X3bhHmx38f7sszP+7gzeX72JtyhnfH2ab7ij1U9VeGJlrr7Rd/qLXeAjSxSUR1ROsIf07lFpCTX2zvUOxmW9o2OoV2wtWldlqT+Lj70DOyJyuTVla81X11HZwFKxfSHNoMM/pJFuVVfXwt2nQ4k+HvruGlxbvp26I+Pz0cKwmYEBaKT41nzvY5mLQVFqov+aeRgMU+AX9fBv88BLd9ZcxmVScBA/ALhb6PQeLP7DmyEi9XLxoHNKZVuD+z7ozh8CkP3HUwm09uueTU9/44wPHss7xyUwfcXS9NS3w93Xj7ti78vW9TFm87QdrpwurF6ICqSsIq641QN1fJHf4LZg+EzMM1ukyrc4vzc60RldPJK85jb9beWq+QPKDhAI6fOc7B7IMXfpF9zGjxEzMeAqNrNaZa0+dRKMiGeMdYKXAqp4DHvo1nzEfryT1bzKw7Yvh0wlUE+kj/OCEsNWPLDN6Jf4eZW2fW7EI7foBt3xgzV9e+YOxutFZPx94Pg184e4/9Raugluf+At6vZSivjOxIfm5j/kraiMn0v0TyQOoZ5qw+xC0xUfRoevndmEqpcx004o5mWideB1BVErZZKXVJCXKl1D2AGbUAnJCpxKi7dPpUjS7TpiwJu1IX5+9I34FJm2ptUX65q6OvBmBV8qoLv/jrTVDKmG6vqxr1hIY9Ye1MKD5rtzAKS0r5YNVBrpm+iiU7T/HoNS34ffLV3NAxUhZKC1ENKXkpJKQlEOEbwac7P+W7xO/MOm9Xxi6WHl76vw+yk+DnyRB9lW06hXj4oq9+mkRdQGsXnwu+ur1nI/pEXUWhzuGtP426hlprXvhpJ97urjxzfdsqL98xKhBPNxc2Hc6yfux2UlUS9jjwN6XUKqXU9LLxJ3Av8Jjtw7MD31DjNS+tRpeJCPAiwMvtil2cn5CagELRMbRjrd433Dec9iHtWZW06n8fZh2BhK/q9ixYuWtfhNzjRlPdWqa1ZunOkwyduZp/L02kb4v6/P7E1Uy+rjXeHrXzSFqUiY42hqgTfj/2OwAfXPsB/aP7869N/+LPpMqb1izcv5C7fr2Lp/56ip8O/GS0Ilo40WjVdsscm/WbPdFqEKddXWhzLA5KLyy8+9y1wwH4aOPvrNiTwuLtJ1l3MIOnhrQm1L/q3sIebi50aViPzUfqzkxYpf8UtNYpQB+l1ECgQ9nHv2it/7B5ZPbiU7YFNj+9RpdRStE64sptX5SQlkDzes0J8KidxrznG9BwALMSZpF+Nt0oErvyX+DiBrF1eBasXJO+Rs2eNW8ZSadf7TT9XXcwnX8v3cu2pGxahPnx+d97cHWr0Fq5t6jAl1/aOwJhRcuPLKdFvRa0CGrBf/r/hwlLJ/DUX0/x6dBPaR/S/oJjS0wlvLnlTb7a8xW9Inuhteal9S/R+PA6uhxdCzd9CMG22+iQmGN0LWmTmQwJX0K3Cee+axrYhGCvEApDkpn0TTw+Hq50jArk9p7mF83u0TSY91ce4ExhCX6ezl9b0NzekSu11u+WjbqbgIFRfRwgr2ZJGBiL8xNPnbZJPzRHZtImtqdur/X1YOUGNhyIRrM6eTWc3A7b50PPByAwquqT64JBL0FJgZF82tjO4znc9fFGbp+zkbTcAv5zayeWPd5fEjAhrCQtP4341Hiua3IdYGxAmjVoEOAhJwAAIABJREFUFkGeQTyy4hFOnDlx7tjsgmwe+P0BvtrzFXe2vZMPBn3A9AHTifQM5rGkxZxsOww632bTeBMzE3FRLrQM6wIrX79go5BSiqsiuuMTeIRAbzcy8op45aYOFhXTvapJMCYN8cfqxiNJmxZUUUoNVUrtVUodUEo9XcH3k5VSu5VS25VSK5RS9u8h4+YBXvVq/DgSjDIVpwtKOJVbYIXAnMeh7EOcLj5d6+vByrUKakWkbyQrk1bC71PBKxBiH7dLLHZRvwV0vwe2fg6pe2xyiwOpZ3jk660Mf3cNO47n8Nywtvzx5ABGd28o1ckdweOPG0M4vRXHVqDRXNf4unOf1feuz6xBsygsKeSh3x8ityiXA1kHGPfLOLambOWVvq8wpccU3FzcCMSVd1MzKFYuTPI6S36JbdeLJmYm0iSgCd6DX4Uzp/5X/LVMt/BupJ1N5Z27mvD533rQpaFlvWK7NqqHi4LNh+vGI0mbJWFKKVfgfeB6oB0wTinV7qLD4oHuWutOwA/ANFvFYxHfUOskYRHGo7grbV1YQlrtFmm9mFKKAQ0HsP74GgoO/QH9nwTvK6xp79VTwMMflluvZlhJqYmlO09x59yNDJrxJyv2pDLpmhb89c+B3NuvGV7usu7LYSQkGEM4veVHl9MssBnN6zW/4PPm9Zozc+BMjp4+yr3L7uWOX++goLSAT4d+yk0tbvrfgcv+j2bph5nWeRL7cw7x3NrnrFPm4jISMxNpE9zG2CjUZjisefuCJ0vdw7sDcKJgF/2rMWPu7+VOuwYBbD4iM2FV6QEc0Fof0loXAd8CI88/oOwxZ37Z2w2AY6wk9Q21zuPIslpI+660JCw1gSDPIBr5268t0ICoqykwFbMxJBquumSDb93nG2Iknwd+Myph10Da6ULeXbGfftNW8sCXcRxKO8NTQ1qzespA/nFdawK8pOSEEJbYl7SOpZvfrvK49LPpxKXEMbjx4Aq/7xHZg5f7vMyezD00C2zGt8O+pXNoZ+PL3JNGG6Gtn0PsE8TGTOQf3f7Bb0d/44NtH1jzt3NOdkE2p/JOGUkYGBuFis4YlfTLNK/XnEDPQLakXFov7JzjWyFl1yUL+8t1bxxMfFJWnWjubctVbVFA0nnvk4GelRx/D7Ckoi+UUvf/P3vnHd5U+f7h+6RtOtJB96a0UAqU1VK2FGSDTJEhylbQnyigCCgKCg4UEfiCgmxRARUUFJC9Ny2UTYEWyii0dO+R5Pz+eMsolJaRNG3JfV3nCiTvOedJm+Y85xmfBxgOULlyKVzYVY6QcOmZD2NnZYabrQXnbj5fWmEnbp+gnks9g8oRNEy6gUqrZadPMC3NipO7q8A0HiHEWzd/Am/tFYN2HxOtVuZQdCIrj15j0+mb5GtkWvg78Xm3QFrXcMG0CEFFI0aMPB4zt49iP9n4ZKRQ88XJj1y346qYtXinHqwoulbtSi3HWnjbeKNU5wrV+pO/C81LWQvV2kErMcZqQK0BXEy5yPwT86laqSodqzzBfMjHIDI5EuCeE+ZcHSo3hbProNV4ABSSgmCXYMLjHqFylRwDi9oI200twa02uNcHj/ri0bkGjXwdWHbgCmdiUwmqXL6zHPp0woq6AhdZoS5J0utACNCyqNdlWV4ALAAICQnRf5W7yhlidDP2o7GfAzvOx5OTr3ku0jXJOclcSbtSOBxe2qjzMNv5FS9YK9mdff2RA2MrPKbm0PYzWD3knkRHCVxPzmJN+A3+DL/G9eRsbC1MGdi0Cq81royfs7XeTTZipKKTFb2TI3I2skLiu8hfWYQSqdUEoWP4AFtjtlLFtgr+lfyLPWbVxKuw7Us4vxHU2VDJB1qMhbp9wOnevpIk8WmTT4lJi+HTfZ/iofKgrnNdnb2380nngfucMIBa3WDTBEiMEpM9ECnJndd2EpcZh6vqgfmxZ/4SDljn74Ro+s0IIS57dKF43cGPhv02AHD0SlK5d8L0eWW6Dtw/oM8LiH1wkSRJbYGJQDdZlsvGLAKVM2QlCl2VZ+SVBl6k5ajZdi5OB4aVfU7cPgEYrh4MEOH35Mu0qtGHhOwEziScMZwthiawJ3g1gh1fQG5GkUty8jX8cyKWAYsP0+LbnczcdoEqjir+92oQRya25dMutYwOWHmjenWxGSlzHNk/jTyFRBuvlhyxtGD3kdmweSI80EWfnJPM0VtHaefT7tFZBVmG3d/CLz1E2UH9/jB0C4w6Aa0nFnLA7qA0UTKz1UycLJ0YsXUEp26f0tl7O590HlcrV+wt7nOMahaMmD677u5TDdwaABQdDTv9F3iGQKM3oeNXMGQjTLgGI8Og2xxIuYbzjg+p4mD5WKKtMYmZbDp985nelz7RpxN2FPCXJMlXkiQl0A/45/4FkiQFAT8hHLB4PdryZKicARmynr37ollVJzzsLFgdfv3Z7SoHRMRHYCqZPqRdU2rkpsPub6BKC1o0eBsTyUR0ST6vSBJ0+BIy4uDA/wBRYH/8ajI/7LzEa4sOUe/zLby38jjRtzN5r7U/e8e9yK9vNKZbPY/nInpbIVmwQGxGyhaxx9mTEomVZMpXod9SxbYKMzyrkH/oB/jn3UI3/juu7kAjax5ZD4Ysw5ZPYOeXUO9V+CASunwvCuJLKAVxtHRkacelVDKvxPCtwzl5+6ER0U/F3aL8+7HzEk7VfU5YDfsaqMxUD9eFJVyEWyehdq/CzysUwqEMHghtJ8P59Yy02094TBJabfHJsU/XneGdFcfJznv2oIo+0JsTJsuyGhgJbAbOAX/IsnxGkqQpkiR1K1g2HbAG/pQkKUKSpH8ecbjSRVUg2KqDDkkThcTLwV7suXCbW6kVX6oi4nYENR1rYmFqoDqsgz+I31vbz7GzqESwa/DDI4yeN7wbkVmtK+q9s/lw8UaCpmyl548HmL45ksSMPF5r7MOvwxqzd9yLjGlXHW8Hq5KPacSIkSdG3jODPSoVzTybY2VmxQchH3BFk8mfQT3g+C+wZhio8wCRivS28X7YqQHhrP0zEg7OhUYjoPuPovzgCXBTubG041LsLewZvnX43SzG05KjzuFy6uWi7a3VTaQVk2MAMFGYEOQSxJFbRwp3ap7+C5AgsJhylibvgN+L9Iibi0P2FaJuFx3hB4i6ncGeC7fRaGXOltHabL0WysiyvFGW5eqyLFeVZfnLgucmybL8T8G/28qy7CrLcv2CrVvxRywlrHTnhAH0auCFVoa/j9/QyfHKKvnafE4nnL7XnVPaZMTD/v9Bre7gJcLdrbxacTH5IjcyKvbP/kHy1FoOXErgi/VnaT1jFx3OtCFPI/Pqja/oWteFOa8GEfZJWzaNDmVS11q84O+EwqjvVXEYPlxsRsoOtyO5EL2ZOBMFoZXbAGLWbSO3RszLjiatzadw5m9Y1Z/U5GgO3zxcdCpSnSvqPI//KqRoOn0jIkVPgZvKjSUdluBo4ciIrSOIiH96WZNLKZfQyJqinbCaBZf2c/fiLJ19OxOTFsOS00vEE7IMp9eATzOw9Xj0iRQK6DkfSWnFHLO5hEc9es7z8gNX7uoWnr6R+sTvqTR4DquVH4M78yOfcXTRHXydVDSsYs+f4dcqtHp+ZFIkuZpcw9WD7f5WKMW3uddt9KL3iwCFZ0lWUG6l5vBH2DXe+e0YDaZupf+iwyw/GIOXvRVvdGlFVtuvCNae4iuXnXSt54GT9ZPdORspR1y4IDYjZYe937PbWsgWtfBqAYhC+bEhY0nNTWWhpQRdZ8OlbexY0gK1rKZ9ejqk3lfKkpcJK18Vqb0OX8GLH5eYeiyJO47YnRqxp3XE7hTlBzgEPPyigy+41YWz95ywLn5daO/TnrnH54ooXPxZSIiE2i+XfDIbNxQ951FLEYN72DdFLknPyWd1+HW61/PAUaUss05Y+R+8pA/uDvHWjRMGokB//JpTHL+WQnA57+Z4FHf+eA2ilJ8cA+HLRM2A4z1RQ29bb6raVWXXtV28VvO10rdLj2TnaTh8OZG9FxPYe/E2F+JEWN7FxpyX6rrTuoYLzas5obozX00eBjf3ihoS35Z3o4VGjBjRM8lX4NSf7PGvTW1bTzHTtoCajjXpVrUbv537jT491uH99gG27hqNZ9Ytau2aAbtmgGcDIXx6YTNcPwLd5kLwAJ2Z56pyZXH7xbyx5Q1GbB3B/HbzCXIJeqJjnE86j7WZNV7Wj5D7rNVNNAilxYKtB5IkMbnZZE4nnGb8nvH8adsQG8kEanYvev8HkAI6stOuJy8m/QkX+4J/4dq5NeHXyczTMKhZFRIz8zhVRp0wYySsKCztQVLoLB0J8FJdDyzNTCp0gX7E7Qg8VB4PtxyXBru/Fb+zluMeeqmVdyvCboWRnlf+RXPj03P45VAMAxYfpt6ULQxeepRfDsXgamvBx51r8N+oFhz+uA3TetWlfaDbPQcMxB1z11lg7SZqT3LL/8/DiJFywf7ZJJmacjI/hVCv0IdefjfoXUwVpswKn0WavRcH827Trs4gpJFhIrIvy7D9c7gRDr2X6dQBu4OrypXFHRbjYuXCyO0jSc19MqflfNJ5AhwCHt3JWaugzuvcv3efslXa8k3oN9zKvMWUK2uRq7QA68dX0b8aPIHzWm80f78lylEK0Gpllh+Mob53Jep5V6K2py0X4zPIyS97xflGJ6woFApRF6ZDJ8za3JROtd3490Rsmfwg6IKI+AjquRigHizhIpxYAQ3fKLKWoHXl1qhlNVuubCl923RAXFoOy/Zfps9PB2n81XY+XXuaG8nZDGjiw89DG3FiUnt+GdaY4aFVqeluW7xIrqU99FoIKTGw8WGH1YgRIzom7SYc/5V9AS8iIxPq/bAT5qpyZXDgYLbEbGF2+GzUWrXoinTyhxbvw/CdMOYMjIoQNa96wsXKhRmtZpCel87Ckwsfez+NVsOF5AvUdKj56EVO/uBcs1BKEoSc0Tt+PdmkhLWeTyarElzVnXfz34WcdFj7NmhFkf+ei7eJTshkcLMqANTxtEOjlcvkCEGjE/YoVE46TUeCSEmm56jZfObRhYTllVuZt4jLijNMKnLnV0JZ+YUxRb5cx6kO/vb+/B75e7mpybuWlMWivdG8Mu8Ajb/azmf/niUlK49RbfzZMiaU7R+05NMutWhZ3RlL5RPKSPg0g9APheN6arV+3oARw+JjD95GbbcywcG5oNWwx84BZ0vnRzoqgwMH42zpzB8X/sBN5UYdpzqFF9h5iU3PVLevTreq3VhxfsVjNzRdTb9Ktjq76Hqw+6nVDWL2F4paAQzNyKVxdi5f39pFdEr0Y9ta092GWDMfNniMhEvb4OQqAH4+cAVnG3M613EHoLanHUCZTEkanbBHoQcnrImfI56VLCtkSvJY3DGA0o+E3TotFJabvPXIMLYkSfQL6Me5pHOcStCdMKEukWWZczfTmLXtAp1m76XFtzv5YsM5MnLVfNCuOtveD2XLmJaMblud6q42zz4SKnScEHFd//7dtvGnsdlIGSP1OqzsD747oOpeOL/B0BY932QlQdhS8mu/zP7bEbTwavHI6R1WZla8G/QuQPECraXAyKCRKCQFc4/Pfaz1kUliXFGxkTAoiOLJcH79vee0WkzOruUrm9pYmFoybs84cjWPp9tuaqIg2MeeH9Nbgr0vnF7DlYRMdl24Tf9GlVGaip+1ZyVLKlmZcfq60QkrP6icdZqOBFAoJHo18GLfpQRiU7J1emxDcyz+GFamVgTYl3AnpGt2fgnmdtDs3WKXveT3ElamVvwe+XspGfZ4XIhL56uN52g5fRedZu9l9vaLqJQmTOxck90ftmLT6FDebeNPNRcb3Z7YxFSkJZHhr+GPHJRbFBeTL9J/Q3+GbB5CvjZft3YZeTq0Gjg0D35oLJTT234GHsHw1wi4beySNBiH5kF+JhG1OpKRn1FkPdj9dKvajbEhYxlUa1ApGVg0bio3Xqv5GhuiN9zteiyO3dd3Y6Yww8/Or/iFLrXAoWrhlOS1w5B2A5c6/fjihS+ITI7k+7DvH9vWhlUciIzPILdqR7i8h1X7zmIiSbzW+N6caUmSqONpx+lYoxNWflA56zwSBvBKsBdyBdQMC48Lp75LfUwVpdhwez0cIjcKB8yy+I5TlZmKrlW7sunyJlJyUkrJwKLJydewJvw6veYdoP3MPSzdfxk/ZxVfv1yHIx+3ZfXbzXgz1A8fR5V+DbGvAl1mwrVDYspACai1ahadWkSf9X24knaF8Lhwfjrxk35tNFIyscdhYWsxn69yU3jnMMwPh+32QsBzVX/IKZtClRWalKtw5Ceo0YXdGVcwU5jR1L1psbuYKEwYFDjIMM1NDzCszjBszW2ZGT6z2HUrzq1gffR6BtYaiJmJWfEHlSSRkry8595EmjN/gakFBHQi1CuU12u+zorzK9h0ZdNj2RlSxR5ZhjO2zUGTR/zxjXSu446LbWHB8NqedlyISydXXbZqso1O2KNQOUFuqhDG0yGVHa1o7OvAn2EVRzMsNTeVSymXCHYJLt0T75gKVo4iFfkY9A3oS542j3VR60perAcib6Xz2T9naPTlNj748wTJmXlM7FyTwx+3ZdmQRrzaqDLONqWs3VXnFaj/Ouz5tkCtumiiUqIYsHEAs4/Npk3lNmzouYHuVbuz8NTCZxJ4NPIM5KTCpo+EA5Z+U3TNvfYn2PvA9esQlwR9xBxV/h5xt2jZSCmQFgs/F8xMbDOJPTf20NCtIVZm5Wcaha3SluF1hnMg9gAHYg8UuebAjQN8c/QbWnm3uptKLZFa3UHWiBtojVoI1Pq3B3MR7R/TYAxBLkF8tPcj9l7fW+LhgrztMTOR2JrhS66ZHc21RxhUUJB/P7U97MjXyESWseJ8o07Yo7irmp8Adp46PfQrDbz4cPVJwmOSCanioNNjG4Lj8ccBCHYNJitPTWxKDrEp2dxMzeZGSg43U7IxUUh4O1jhZW+Jt4MVlR2scFQpn77u4co+iN4J7b+4+8dbEv72/gS7BPN75O8MqDXgkbUZz4JGKxObks3lhEyuJGZyOeHeFpOYhdJEQac6brzaqDKNfR0MWvdxly7fQ1IU/P0W2HmDd8O7L2m0GpafXc7c43OxMrNiesvpdKzSEYAJjSYQFhfGhL0TWNNtDSozPUfujAi0GqGWvn0KZCVCyFBoMwksKz28tsoLQtTzv3GwZzq0Gl/69j5vZMTDz90gMxEGruWauRWXUy/TN6CvoS17YvrV6MeK8yuYFT6LJu5NCn1nRqdE88HuD/Cv5M83Lb7BRPGYDULu9aFSZZGStPMSZT/3zYpUmiiZ22Yub2x+gzG7xjCv7TwaujV85OEslSbU9rTj8JVU9hBMO9MwbLwebkqpU1Ccf/pGGnW9ivhbMRBGJ+xR3K+ar2MnrHMddyb/c4bV4dfLlRMWl5bDgj3RnLuZRq5aS55aS65aQ7L5WrAyYehPcaRnby60jySBs7U5WlkmISOv0GuWZiZ42VviaK3EQVWwWSmxL/i3RyVLqrvaYGf5QIhbloXon7WbkKV4AvoG9GX83vEcij1EM89mRa7JyddwNSmL68lZpOeoyc7TkJWnITtfQ1aemqw8DZm5atJz1KTl5IvHbPGYmp2P+r6BslZKE6o4qqjtYceAJj68HOyFg0r5RDbrHVNz6PsbLGoDK/vBm9vBvgqpuam8s/0dTtw+QZvKbfikySeFRCatldZ83eJrBm8azLQj05jafKoB38RzQsxB2DQebp4A7ybw+mrwKEFUs9FwkbLc9RW414OAjqVj6/NIZiIs7w5pN+D1v8ArhD3nfgMg1LP4erCyiNJEycigkXy09yM2Xt5IF78uACTnJPPO9ncwNzFnTus5TxbhkyQxxujwT6C0AqW1iITdh63Slp/a/cSQTUN4Z/s7LGy/sNhxeA2rOLBgTzRrFPVop9wp6syqvFBojbeDJbYWpmWuQ9LohD2Ku6r5ui3OB1CZm9K5jjvrT97k0y61CgtqlkHi03L4cVcUK45cRauVqetlh6XSBDtLM8xNFZzUXsFC8qN1/Sq42FrgWckSdzsLPCpZ4mprcbdDJStPzfXkbK4mZnEtOYtrSdncSMkiKTOPyFvpJGflk5yVx4NZWnc7C6q72hDgZkOAqw1BeWH4XT2IttN3KMwsH+s95ORrSM9RU03VFFulPT8eW05WalUyctXEpeVwJTGTKwlZxCRmcjMt5yEb7mCikLAyM0FlboqtpSk2FmY4qJRUcVRhY2GKnaUZlR2sqOKkwtdJhYuNedmIdpWEylGksha1gRV9kYds4vMjUzmTeIZpLabR2bdzke8jyCWIN+q8wYKTCwj1ChXaRkZ0T8o12DpJ1M/YekKvxSJ68DifLUkStX/xZ+GvN+HNHUKzyYhuyU6BX3pAYhS89gf4iPqv3dd242vni7ett4ENfDo6+3Zm+ZnlzDk2h3Y+7VCgYMyuMcRnxbOk4xLcrd2f/KC1ugvpjjN/Q50+whl7AHsLexa2X8igTYN4e9vbLOmwpOi5lNxzws5YNECWlEiR/z3khEmSRG1PuzI3vqhsX/0Nieq+dKQeeK1xZVaHX+erjef4smedkncwAPHpOczfFc1vh2NQa2V6BXsy8kV/Kjve+4PJVmfTbEUMA2sNZEyD2sUez0ppSnVXG6q7Pjp9qNHKpGXnk5iZx7WkLM7fSudCXDqRt9I5GJVInkbDP8rPuC450W6dK/Y7tuNia4GrrTmuthYoJInkrDySMvNIzsojOTOfpMw8su8TyFU61yPVcTdvHt6GrBZhaUeVEh9HK5pUdaSKowofR5EytbEww0ppgpXSBEulCUoTRflwqp4GJ3/o+yv80pO1f/ZiK7d5v8H7vOT3UrG7vVXvLfbf2M/nBz+nnnM9XKxcSsng5wBNPuyfDXu+A2QxsLn5KFCWkPpt+kABuJml+N0uaCUK9d/YDha2+rL6+SM3HX57BeLPwasrwa8VAJn5mYTFhZXrkWkKScGYBmMYvnU4q86v4lLKJcLjwvmmxTfFRqeKxTMEbNxFLWMxsyKdrZxZ1H4RgzYNYsTWESztsBS/Sg93YIb42KM0VfByU3+kuFAhzdL+i4duUup42rF0/xXy1Nq7wQFDY3TCHoUeI2EAQZXtGRHqx097onmhmhOd6jzF3YSeiEvLYeGeaH49HEO+RqZnkCfvtq5WZLfeqdunUMtqGrjqZg6hiULCXiVSktVcrHmxxr0Lulqj5faRP3DffJn9gZ8zxKY6cWm5xKfncDkhk4NRiWhl7qY2XWwsCHC1xUFlRiUrJbYWplhbmJKHN1+e3M3r7W4ypFZ3nGzMsbUooavnecE3lJj2k/n63E80VjoyqNbAEncxU5gxrcU0+qzvwyf7PmF+u/l6qbd77og7I1TAb56Aml1FbVelyiXvB/D11w8/V6myKN5f3h3+eVf8u6LeUBSHLMO5f6BysycakfNI8rJgRV+4cQz6LC80w/BQ7CHytfklSlOUdZp6NKWZRzNmhc9CLat5q95bdPbr/PQHVCigTm84+TtUbV3sUg9rDxa2W8jgTYN5c8ubLOu0DG+bwlFFe5WS7e+3xN3OAo51hg3vw+1IcCkcOavtaUeeRsuFuPS7Aq6GxuiEPQpzGzBR6s0JA/igfQCHohMZv+Ykdbzs8LI3bOfMpfgMFuyJ4u/jN9BoZXoEefJea3+qOD36rjs8PhwJifou+lfKN5XVuB/5Gpxr0vzlkTQ3edqPrxd7k0LZF7eBSS+MKrmt+jkiX5PP+IQDmJmY82X0aRQHf4Dm75W4XxW7KowNGcvUQ1NZeX5lub7zNziafNg3S8iGWNhB758hsIduju0bKor4t30GRxZA4xG6OW55Yt/3oqnBxh1eWXo3bfhUpN6APwZC7DHotQhqdin08p4be7AxsymV70d9M6bBGPqt70d7n/a8Xe/tZz9gm0kQOlbUpJZAFbsqLGi/gKGbh/LmljdZ1H4RXjaFpwd4OxRcPwM6CScsckORThjAmdjUMuOEGW9XH4UkFWiFJertFEpTBXNeDUYrw+hVEag1hmkhD49J5s3lYbT9fjfrImJ5tVFldo19ke/71C/WAQOhlO9v74+tshRSG0cXQfIVEWZ+agdM0DegLwnZCWy/tl03tlUQfjzxI2cSz/B56DRcA7qKOqT9s3lkkdx99K7em5ZeLfk+7Hsup14uBWsrILdOi7q8nV+I6Nc7h5/OAevVS2xF0WwUVO8EmyfC9bBns7e8cXEbbJ8K/h1EinbZS3BgzmN9vh/i8l5Y0BJunxeO8n0dflpZy5mEM+y+tptmns0wU5T/G70aDjX47+X/+Db0W91Euk3MxE3GY1Ldvjo/tfuJ9Lx0hmwewtW0q0UvtPUQzSqR/z30ko+DFTbmZas43+iEFYdKt0O8i6KyoxVf9qxNWEwys7df1Ou5HmT3hdv0nn+AXvMOcPRKEu+18efAhNZM6V67UN3Xo1Br1Zy4faJ09MGyk0VkwO9FqNbmmQ/X3LM5ntae/BH5hw6MqxgcvXWUxacW08u/F22rtIee84UjsHWSqCPKTi52f0mS+KzZZ5gqTPkh4odSsrqCoM6F3d+Kmq20WJHW6r30Xm3qk5KYKLaiUCig5zywdYc/Bt0TzazoJEbBmqHgWlukYofvghovwZZP4PfXRWF9Mai1arSyVjhsB38QaV2LSqLRoVY3ErMT+TfqXybsnUCr31vRb0M/knOT6VmtZ2m8u1LB3dr98aUo9ECgYyBLOiwhV53L4E2DiU59xJzJgM7iBiM9rtDTCoVELQ9bTt0oO+LFxnRkcehhdFFRdK/vyb6LCczdeYlmVZ1oWtVRr+eLup3BF+vPsjPyNp6VLJnctRZ9QryfuEvzfNJ5stXZOqsHK5Y93wlxyvZTdVLHopAU9Anow8zwmUSlRFG1UlUdGFl+Sc1N5aO9H+Fj68O4huPEk2aWwhk4PF9cqH5XQg/WAAAgAElEQVQKFXf8no92up0snXit5mssPLWQ4XWHU92+eim9g3LMhS1C7T4pSkRTOk0X3ar6xNJe/C6XdBBjq/r/IZyzikpuhnC0JAX0+/VeN16f5WK00NZPRVSrz3Ih4/EAV1KvMGjTIJJyklChwFqdh42vH9YO1bE+MYvE7ETOJZ0DwMHCgeaezWnu2Zym7k1xtNTz7/I5I8AhgCUdlvDGljcYsmkIi9ovwt/+gW7fgM5ipN2FTdCg8AioOp52LD8UQ75Gi5mJ4T/zhregLKOn0UVF8Vm3QHydVIz+/ThJmXkl7/AUpGbn88X6s3SYuYewK8lM7FyTnWNbMaS571PJZITHhQNCpFWvJF0W9Sv1XwM33XWS9qjWAzOFGavOr9LZMcsjsizz+cHPScxOZFqLaYU1fyQJmrwNQzYJxfUlHeDIwmLTN4MCB2FtZs28iHmlYH05JjFKFHSv6C1+zq+tgVeW6N8Bu4NnMHT8Gi5thX0zSuechkCWYd3/ibThK0vEuK47SBI0/T8YvFHU4i1qJzQIj/8m0llXDyPfvsAX+yeTr85hRJ4ZPVNTaVqpJlW8X8BcaUVyTvLd4duruqxiZ5+dfN3ia7r4dTE6YHqimn01lnZciqlkytDNQzmXeK7wAtdAsKtcZEqyjpcdeWotl+IzSsna4jFGworDylFEwmRZ711EKnNT5rwaRM8fDjBu9QkWDgzRmRSCRivzR9g1vtscSVJWHn1DvPmgfcAzj8g5FncML2sv/UsSbJ8CClNoPVGnh3WwcKCLXxfWXFzDwMCBD3XcPC+si1rH1pitjA4eTaBTYNGLvBvCW3vF+JuNY+HqQeg6u8hpBXbmdgysNZAfT/zIucRz1HSsqed3UM7IzYC9M4ROkokS2k2Bxm+DqQFEfEOGwdVDsPMr8GoEfi1L3wZ9s38WnF0nfs6P6sSr3BhG7BFTI/ZML/TSemsrDjs78WlCEn3UZsKRK6Gjz4j+8bXzZVnHZQzbMoxhW4axoN0CajsVyCRJkijQP/Yz5GUWknQJ9BB1aKdupFLT3fAyLcZIWHGonEGdLX6JpUCghx0fda7BtnPxTP7nzDN76vFpOfxyKIauc/bx0V+n8HNW8e/IF5jWq+4zO2CyLHM8/rj+o2DXjgqBymbvioJLHfNO/XcwVZiWOKS2onIz4ybTjkwjxDWEwYGDi19s5QCv/i66ms78DfOaiYtbEVGx12u9jo3Shh8jftSP4eURjRoiVsDchqJDr3YveDdc6H7p2gFr00ZsJSFJ0GUWOPrDmmGQdlO3dugIjVZDruYp5vhe2gbbPofAl6FZCV2+KicxgWDCVXgvAt7cQUqf5Ux3r0xdC1deaThGOGpGB6zM4G3rzbKOy7BV2vLmljeJTIq892KNzqDOgehdhfbxc1KhUppwpowU5xudsOK4f3RRKTG4WRVeaeDF8oMxtP1+Nx1m7mH2totcin+8oaNXE7NYuCeaXvMO0Pjr7Xy69jQ5ag1zXg3ijxFNddaWezn1Msm5yfqtB5Nl2DIRVC4lf4E+Ja4qV4bUHsLWmK1306vPC7IsM/nAZLSylqnNpz5ewa1CAS0+EOkbpY1oz1/WBW6dKrTMRmnD4MDB7Lq+i9MJp/X0DsoJd5yvHxoK3S9rFxi6RTQ+2Ljp55yffiq2x8HcWtRC5WWK32deln5segamHppKl7+7kJj9BN3qSdGweii41ILucx8/m2FhBw6+4NmAmSnHSNfkMbn9jyhCxz6+TpuRUsPD2oNlHZehkTWsvrD63gs+zcHcDs5vLLReoZAI9LArMx2SxnRkcdwVbE0oXEegRyRJ4rve9RjbPoD/Tt9k46mbzNp+gZnbLhDgakObmi5YmJmg1mjJ08ioNVryC/4dcS2FczdF10eghy3vt61Ox9puVHOx1rnKe3h8QT2YPjsjz/0jZoB1nS0uFHpicOBg1lxYw7dHv2XlSyufG6HR1RdXc/DmQT5t8ulDmjsl4tNURAWOLYMdX4qi/eCB0PrTux19r9V8jV/O/sLciLnMbztf92+grKNRCzHKPdMh+bKoZ+y3QhQNlzWRVJca0GMe/DlYFLC/uvKx9JtKg/iseNZdWodaVjNx30R+bPtjyX+jGjWsKZgr2+/XkicMFEHYrTD+uvgXQ2sPNTaYlHHcVG7Ud65PWNx9kismZkI498ImMfT+vpvMQE9bVh65ilqjxdTAxflGJ6w47o4u0n+H5IO42VkwpLkvQ5r7EpeWw3+nbrLx1C3m7Y66m/1RmigwM5EwLXj0dVLxyUs16RDodk+4Tk8cizuGg4UDPrY++jmBOg+2TgbnmlD/df2cowBLU0tGBY/i430fsz56Pd2qdtPr+coCNzJu8N3R72js3pje1Xs/3UFMTMUA9dq9YNc3cHQhnP4bWo6DkCGolCqG1B7CzPCZRMRHVAjBysdCkw8n/7jP+aoL/VaKGpXScr46dRKP/z1cmPxIAntA7v+Emv6aYfDKsmfW49MFq86vQiNrGFp7KEtOL2Hp6aUMqzOs+J0O/QA3wsWMTYeHx9yURJ4mjymHpuBp7clb9d56SsuNlCYhbiHMOT6H5Jxk7C3sxZMBneD0aiFXUbnx3bV1PO1Ymq8lOiGz2DF6pYHh/8LKMgZ0wu7H1daCwc19Gdzclzy1FoUkxvsYcobhsbhjNHBtoD8bjiwQF7DX1pTKheAlv5dYcW4Fs8Nn07Zy28IdghUMraxl8v7JAExpNuXZf4eW9tBpGoQMhc0fiRTy7m+gziv0q9eXny0c+CHiBxa2X6gD68sw+TkQ8Svsmw2pVw3jfN0hO/vp9gseKBoHNn8E/4yE7j8aVLoiR53Dnxf+pJV3K0YHj+Z6+nXmHJ9DA9cGj3bqb18Q0dkaXQoJqD4JS08v5XLqZX5s8yOWppbP8A6MlBYhriGAuDa18Smoh6zWVjR1RW54yAkDOHU91eBO2PORd3larMqGE3Y/SlMFpgYeIn0r8xaxmbH6S0WGLRG6Pf7tdSLM+jgoJAXjGo0jPjuepWeWlso5DcWfkX9y+NZhxjYci4e1DpsdnKvD62uEnEVAZ4hYgdXiDgxLz+bQzUOEXd2tu3OVJXIzhOr67Hqw4QOwcRUNDCP2iOLgspZ6LImm/wcvToQTK+G/D59OTV5HbIjeQEpuCgNqDbgrBuymcmPcnnGk5hZR06PVCDkKpRW89P1T/exj0mJYcHIBHap0oIVXCx28CyOlQW2n2pibmBdOSVpWgiovPCRV4edsjaWZSZmoCzNGwopDaQVKa72OLiqP6E0fTJaFwN6e6WKsSO+lpXoBC3IJokOVDiw7vYxe/r1wU+mpaNqAXE+/zozwGTR1b8or/q/o5yQ+TcXWaRqcWk2f8GUsk9KZu2kESy1rIdm4iyizylkUqaucRPNFpcpg8Rgt43mZYjhvylVwqQlO1Q3j6GQnw5FFcOhHyE4ScxlfXiAey5vj9SChH0JumnAuzW2g7WelboIsy/x67lcC7APuRjlslDZMD53OwP8GMvnAZGa2mln4hvTQj3D9KLy8SDjDT3HOqYemojRRMr7heF29FSOlgNJEST3neoWdMBBO2I4vCklVmBQo55+JNTphZZ9SGF1U3jgWdwyVmYoA+wDdHVSTD/+OgojfRErkpZkGqUcZ02AMO6/uZPax2Xzd4utSP78+0cpaJh2YhEJS8Hmzz/UfTbW0h0ZvYtHwDd44/C1fR/7KkZybNL59HjLiQZv/8D5WjmDvKxphHHzFv0EIbd7ZUh6YGWflBD7NxJetT3PRDafPFNrNk6L+7eSfQsKmekdoMVZoqVUUJAnaTRUXrn0zhSPW4oNSNeHwrcNcSrnE1OZTC31W6zjXYXSD0XwX9h2rIlfxao1XxQsJl8TFNuAlqFP0DUZaXhprL65lffR6cjW5mJuYY2FqgbmJOeYm5qi1ag7fPMwnjT/B2cq5NN6mER0S4hrCvBPzSM1Nxc68QAnAoWAaSlJ0IbHvOp52/BF2DY1WxkRhuJsmoxNWEqU0uqg8cSz+GPWd6+tuhlhuBvw5SGj6tPoIWo43WCTB09qTAbUGsPj0YvrX6E8dZ90p9BuaVedXcfTWUT5v9jnu1u6ld2JJolfD0Sy5tpXp9pVYPng7VqaWYgxVZoL4+8q4Bckxog4w+YqIZpz5C+SCofYmSqFl5dUQggaAcwDYeUPcabiyH2L2i25aEPP8fJqJrXIzcK8rOqWeBXUunP1HOF/XDoOpJdTtDY1GgFvtZzu2PujS5dmPIUnQeYb4+9w+RTz3wvul9rf569lfcbBwoJNvp4deG1BrAIdvHmb60ekEuQRRo5I/rHsHTC2gy8NpyKiUKFacW8G/0f+Src6mrnNdvGy8yNXkkqvJJUedQ2puKrmaXLpV7UbvgKdsVjFiUELcQpBPCA3LVt6txJOOBU5YYlQhJyzQw5asPA2XEzKo5mK4ujCjE1YSVk6Qet3QVpQZUnJSuJRyqcgvxqciIx5+6y10prrNEVEwA/NGnTdYe2kt08Om83PHnw1af6crLiZfZNaxWTT3bG6QgcLmJuZMajqJd3e8y7g945j14ixMLSuJmg2nakXvpMkXUS9ZFpGxoiKjnsH3PjMpVwscsn0QcxAiC/SBzFQiSuXTHCo3Ec6bylmkJor63coyZMQJpzAlRnw2T6wUzqKDH3T4Cur3F5G+ssrYsbo5jkIBPX4UzvD2KaI0o/0Xei/Wj0mLYff13bxV7y3MTR6WylBICr584Ute+ecVxu4eyxjrmlgmRGDRYiwWeUlYpGZhaWLJuaRzrDi3gsO3DqNUKOns15n+NfobpzhUUOo41cFMYUbYrbB7Ttid7tikqMJrvUSk7PSNNKMTVqZROUHscUNbUWY4Hi9+Fs9UlC/LEH8WonaILsjMBKFLVL2Djqx8NqyV1rwb9C6fHfyMZWeWMaT2EEOb9EzEZsTy1ta3sDGz4fOmpZCGfAShXqF81Ogjvjz8JdOOTGNi44nF22Jidu8u9nGoVBnqV4b6Bemp9FtivFLMAeGU7fwKuK/I3NRSOGN36tNkrXC6Uq4Kpe07SApRo9joDfBrXbEHXReFiRm8vFD8nA79IMSru//w7NHFYlhxbgWmClP6BvR95Bp7C3umhU5j+JbhjE6LATcXuLRcbPfhpnJjVPAoevn3uiddYKRCYmFqQR2nOoXrwsxtwNoVEqMLra3mbI1nJUsy89SlbGVhjE5YSaicxZdOKcyPLA8ciz+GmcLsydN06XEQvROidorHjDjxvHNNGLQMvPSovP8U9PTvyYHYA3wf/j0e1h50qFI2HMQnJSUnhRFbR5CtzmZZp2W4qp68WFmX9KvRj9jMWJaeXoqHtQdDaw/V38ls3CCwp9hAFNLfOCacs8zbBVtBOjS9YFyPc4DoyrWvIpy6Sj7iUVnOJEtatRKPu3bp5ngKBXScJr4Pd0yFrCTo8/NTiaCWRFpeGn9f+ptOVTrhZOlU7NqGDoFsVTuRkHSRnF6LyDZXkaPOIUeTQ446B3sLe17wfAFThfFS97wQ4hbColOLyMjLwFpZIPLtUPWhSJipiYL9Eww/gsr4yXyAM4lnWHFuBWMajBFfACpn0KohJ6Vspx9KAVmW2Xdj391W4BLRauD8ejj4g6ijAVF47fciVH1RPNp56tfop0QhKfiqxVfEZ8Xz8d6PcbFyIcglyNBmPRFZ+Vm8s/0dYjNi+andT2VG9Xt08GhuZdxiZvhM3Kzc6OzXuXRObGlfapInFRJJgtCxIiK2fgws7w79/xAzRXXI3xf/JludzWu1Xit+4dXDsPZtnJKicOq5AHzb6tQOI+WTENcQFpxcwPH44/ckRhz94MIWwxr2CJ6zuHrJpOak8k/UP8SkxYgn7h9d9Jxz9NZRLqVconvV7sUvzMuEIwthTrCYRZcRL4Y+j9gDYy/BK4sh6PUy64DdwdzEnP+1/h/u1u68t+O9e5+JckC+Np+xu8dyOvE037b8lhC3EEObdBeFpOCLF76ggWsDPtn/CUdvHTW0SRWO29kJ3MiIRXunsUGXNBgMvX8WXaJLO0HqDZ0dWqPVsPL8SoJdggl0DCx6UX42bJ4ISzqIDttB/0K9R6ctjTxf1HOuh6lkWjgl6VAVMuMhJ81whj0CoxP2AHfEK2MzYsUTKkfxaOyQ5Jezv2Bvbs9Lfi8VvSA9DrZPhZmBsHGscGD7/ALvhov2dvd65a6ext7Cnnlt5iEh8fa2t0nKSTK0SSUiyzKfHfiMvTf28kmTT2hTuexFf5QmSma/OBsvGy9G7RxFVEpUyTsZKRFZlllwcgFXUi8Tm3GDrw5/hawPsdVa3YQwb+oNmNcUtnz6sHTIU7Dr2i5uZNzg9VqPGFV27QjMbwEH54oJDW8fFLpsRowUYGVmRaBTYGEnzPE+mYoyRvm6IpYCd1r3b2QU3N3djYQ9307YnW6lPgF9sDC1KPxiXqbonJpVB/bOEF1oQ7fAG9vEl7WupCwMhLetN3PazCE+K553d7xLzv1F22WQWcdm8U/UP/xf/f97+rmQpYCduR3z2s5DqVDy9ra3iUyKNLRJ5RpZlpl5bCZzjs/B0dIJN5U7v0f+zrdHv9WPI+bbAt7YClVbi5KD2fVE5Dvm4FOr7P9y7hc8VB686P3ivSe1Wsi4LRy9JR1E08TAdUKKwtxaR2/GSEUixDWEswlnycrPEk/c1Qorezd7xpqwBzA3McfZ0rkIJ+z5Tkf+evZXTBQm9KvR796Tsgxn14rUQNoNqNNHaHw9SnKgHFPPuR7TWkzj/V3v89Hej/iu5Xe600nTEbIss+jUIpacXkKf6n14q27ZHzzsae3JD21/YMTWEfRZ34ee1XoyMmhkiQXZRgqjlbV8eehL/rjwB30D+lLlDXsk4LWa6fx67lfMTcwZFTxK952xLjWh9zJIuSY01MJ/hrPrwL0+NH5LOGq2nsU3NckypF7nwPk/CY8LZ6yFH6Yr+4vmnYw4Uc4ga8TaBoOFiOzjTFYw8twS4hbC4tOLibgdQTOPZvdkKhLLXiTM6IQVgae15710pNWddOTz64Sl5qayLmodnX0737s4xp+H/8bB5d3gWgd6LRajaiowbX3aMjZkLNPDpjP10FQmNp6ImR7b9J+ErPwsJh+YzKYrm+hUpRMfN/643OibBToGsqHnBn46+RMrz69k4+WNDKs9jIGBA43Dkx8DtVbNp/s/ZX30eobWHsro4NFITcTvfrwsk6fJY/HpxZibmvN2vbf1Y0Qlb2g3RdyEnVgFh+fD2oKbADOVuDFzql6w+YuhyrERcDMCYiPIyE5kspc7vlqZfgmRYpyVtasQwrV2FZtHEHg30o/9RioUQS5BmEgmhN0KE06Y0gpsPIyRsPKCh7UHJ26fEP8xMRNdVc9xOvKvi3+Rrc5mQK0BorBx9zfiS1apgs7fQYMhBhkxZAgG1BpAcm4yi04t4lzSOaaHTqeybWWD2nQ59TJjdo7hctplRgePZmjtoeXGAbuDnbkd4xqOo29AX2aGz2RuxFz+vPAno4JH8ZLfSygkY+VEUeRp8hi3Zxzbr27nvaD3eLPum+KFLJGGkays+KTJJ+Rp8vgx4kfMTcz1KwuiVEHDYeI74fpRMdEg4SIkXBDdjKf+vLdWMhGRtOodmSElEZ9yhuXtF2HubnS0jDwbKjMVNR1q3p1zDIi6sESjE1Yu8LT2ZMuVLai1aqEvY/X8zo9Ua9WsOL+Chm4NqWFmDwtbQ+IlCB4AbSaLdvXnCEmSGBU8itqOtZl0YBJ91vdhUpNJpSez8ADbY7Yzcf9ElAol89vOp6lH+Y5G+tj6MOvFWYTdCmN62HQ+3vcxGy5vYG7ruUatpwfIVmczeudoDsQeYEKjCbxW8z5Jh84Fn8ddu+7OCs3T5DEzfCbmJuaF1+oDhQIqNxbb/eRliguhJh9ca4GZJQdjD7J663AGBw6mntEBM6IjQtxC+O3cb+Soc0Qds4OfkEwqYxhvL4vAw9oDtazmdlaB46Vyfm7TkdtitnEr8xYDfLvB8m6QFguD/hEjhp4zB+x+2vi0YXXX1VS3r874veOZtH/SvSLQUkCtVTMrfBajd43G19aX37v8Xu4dsPsJcQth5Usr+TDkQ/bf2M/8E/MNbVKZIleTy6gdozgYe5ApzaaU6FSZKEz4ssWXtKnchmlHprHmwppSsvQBlCoxy9OrAZhZkpmfyeQDk6liW4V36r9jGJuMVEhCXEPI1+ZzKuGUeMKxKmQlQnaKYQ17AKMTVgR3ZCruFec/v5GwX87+QmVrT1pu+0bM0uv/u7ElvAB3a3eWdFjCm3XeZO2ltfTb0K9UOvyiU6N5a9tbLD69mN7Ve/Nzp59LdyB3KaGQFAwMHEj3qt1ZcHIBB2MPGtqkMkGeJo9RO0dx6OYhpjSfQk//x5sFaqYwY3rodJp7NmfKoSlsuWJ48crvw77nVuYtpjaf+nDXtREjz0CQaxASEmG3CqQqymiHpNEJKwIvay/gAZmKrOcvEhYRH8HJhJO8lpSEIjFKzHf0bWFos8oUpgpT3gt+jwXtF5Cel07/Df35aO9HHLp5SOdCmdEp0YzbM44ea3tw8vZJpjSbwqSmk1CaKHV6nrLGx40/xs/Ojwl7J9yLTj+n5GvyeX/X++y/sZ/JTSfTo1qPJ9rfzMSMma1mUs+5HuP3jufAjQN6srRkDt88zB8X/mBArQHUd6lvMDuMVExslbbUcKhxTy/sjlZYGeuQNDphReCmckNCuk+w1VnMStMYdtBnafPL6SXYyBI94q5AvxVi1JCRImni3oTVXVfT078nu6/t5s0tb9JxTUfmHJ/D1bRnE7GMSoli3O5x9FjXg13XdjGk9hA29dr02BGQ8o6VmRXftfyOrPwsJuydgEarMbRJBiFfm8+Hez5k9/XdfNrkU3pV7/VUx7E0tWRum7lUtavK6F2jiYiP0LGlJXOnm9fH1oeRQSNL/fxGng8auDbgxO0T5GnywN4XkIyRsPKA0kSJs5Vz4XQkMmSXfbV0XRGbdIFtV3fySlo6Vr2Xg79xLltJOFo68kmTT9jZdyfTQ6fjV8mPRacW8dLfLzHwv4EsPrWYbTHbiEyKLLZ+LCMvg6iUKPbd2Me43ePoua4nu6/vZmjtoWzutZkxDcbgYKHbeX1lnWr21fi48cccuXWEBScXGNqcUketVTN+z3i2X93OR40+ok9An+J3GDxYbI/AVmnL/HbzcbZ05v+2/1+pC+XODJ9JbEYsU5pNMcqQGNEbIW4h5GpyOZ1wGswswM6rzHVI6rXdSJKkjsBswARYJMvytAdeDwVmAXWBfrIsr9anPU+Cp7UnsZl3ImEFBeiZt4V+TUUnL4sV6wYhSTL9Q6dAQEdDW1SuMDcxp6NvRzr6diQ+K5710etZd2kds47NKrTOxdIFb1tv3FRupOSkEJcVx63MW2TkZ9xdY2VqxbA6wxhYayD2Fs/3APke1XoQFhfGvBPzCHYNprF745J3qgCotWo+3vsxW2O28mHIh/Sv2b/knYpxwO7gZOnEgvYLGPjfQN7a9hbLOy7H29b72Q0uhnxtPluubGFV5Cper/k6wa7Bej2fkeebBi4NAAiLCxOfNQe/MhcJ05sTJkmSCfAD0A64DhyVJOkfWZbP3rfsKjAYGKsvO54WD2uPe2H652x0Uebur/lLTqOdYx3c6g8wtDnlGhcrF4bWHsrQ2kNJy0vjWvo1rqVd42r6Va6mXeVa+jWOxx3H3sIeH1sfGrk1wk3ldnerVqkaNkobQ7+NMoEkSUxsPJFTCacYv2c8q7utrvDK+jnqHCbum8iWmC2MaTCGgYEDH2/HhIIaVqfifz6e1p4sbLeQQZsG8ebWN1neaTkuVrq90dRoNYTHhbPpyia2xmwlJTcFPzs/3gt+T6fnMWLkQSpZVMLf3p+wW2EMrztc1IWd/svQZhVCn5GwRsAlWZajASRJWgV0B+46YbIsXyl4TbcVzDrAQ+XBpsxNQivsORpdlH87knFRq8iwtGBQs08MbU6FwlZpS6BjIIGOgYY2pdxiZWbFjJYz6L+hPxP2TuCntj+VufFRuiIhO4H3drzH6YTTjA0Zy6DAQY+/8yuviMddu0pc6lfJj/lt5zN081D6re9H34C+9Kre65kcXK2s5cTtE/x3+T+2xmwlITsBS1NLWnm1ooNvB17wfAFzE/OnPr4RI49LiGsIay+tFXphDlUhJ0XUeFuVjZIOfTphnsC1+/5/HXiq/IEkScOB4QCVK5eOOrmXjRcaWUN8Vjwez4kTptFqmPjfUPZYWjApaDS1nWob2iQjRh7C396fjxp/xOQDk3ljyxtMbT4VLxsvQ5ulUyKTIhm5YySpuanMenEWrSu31uv5Ap0CWdR+EXOOz2FuxFzmn5xPe5/2vFrjVeo513vsCQxRKVFsiN7AhugNxGbGYm5iTqhXKB2qdCDUK9RY/2Wk1Gnl3YqV51eyP3Y/be52SEY9F05YUX+18tMcSJblBcACgJCQkKc6xpNyv1aYh0sDMWKjAqcjZVnmyy1v85+cxhjHhvSuO8zQJhkx8khe9n8ZhaTgmyPf8PI/LzM2ZCy9q/cud+OaimLXtV2M2zMOW6UtP3f8mZqONUvlvHWc67Cg/QIup17m98jfWXdpHRsvb6SGQw36BPTBv5I/KjMVNkobrM2ssTKzQiEpiM+K57/L/7EhegPnks6hkBQ0dW/KyKCRtK7cGpWZqlTsN2KkKBq6NcTO3I5tMdtoU2OIeDIpCrwbGtawAvTphF0H7q/y9AJi9Xg+neKp8gSEE9bQraEY5F2BnbDZYd/zZ9xBhuUqGNrRqE5upOzTo1oPGrs1ZtKBSUw9NJXtV7fzebPPcVO5Gdq0p0KWZZafXc6MsBkEOgbyv9b/w9nKudTt8LXzZUKjCbwX9B7ro9ez8vxKphyc8tA6CQmVmYosdRZaWUttx9qMbziejr4dK3ytnpbFRSEAABzoSURBVJHyg5nCjBe9X2RbzDbyGk1EKSnKVIekPp2wo4C/JEm+wA2gH/AYbT1lgyK1wipoOnLRqUUsPruMPmnpjOqwEEwrtvinkYqDu7U7C9ot4I/IP5gRPoOX173MhMYT6OrXtVxFxVJzU5kRNoO/L/1Ne5/2fPHCFwZP3VmZWdEnoA+9q/fmQvIFErITyMjPICMvg4z8DNLz0snMz8RWaUtH34742vka1F4jRh5FO592rL20lkO3jxFq512mOiT15oTJsqyWJGkksBkhUbFEluUzkiRNAcJkWf5HkqSGwN+APdBVkqTPZVkuE1XLZiZmuFi5FNYKq4Cq+X9E/sHsY7PplJXLRMcmSNXbGdokI0aeCEmS6FujL808mvHJ/k+YuG8im69sZlzDcfjY+hjavGJJyE5g+dnl/H7+d7LUWQyvO5x36r+DQnpGCce339aNgYifb4BDAAEE6OyYRoyUJk3cm2BtZs22mG2EOlZ9biJhyLK8Edj4wHOT7vv3UUSaskziae1ZOBIWe8ywBumIXE0uF5MvcjD2IHOOzyHUpBJfJkSi6Pu1oU0zYuSp8bb1ZkmHJfx27jd+PPEjPdb1oH+N/oyoNwJbpe1THVOj1XAy4SR+dn7YmdvpzNbYjFiWnF7C3xf/Ri2r6VClA8NqDyPAQUeOTt++ujmOESMVAKWJkpbeLdlxbQef2jfC7HoYyDKUgWi5Xp2w8o6ntSfhceHiPyqncpmOlGWZUwmnOJVwinOJ5ziXdI6olCg0shj90rhSDWZEbMXshQ/AwZhOMFK+MVGYMDBwIJ39OjP3+Fx+OfsL/0b9yzv136FX9V6YKh7/K+/wzcNMPzqdyORIzBRmtPJuRRe/LrTwbIGZidkT25anyeNM4hlWX1jNxuiNIEH3qt0ZUnuI7iN21woa0731K75qxEh5oV3ldmyI3kCYswVNc9PE9dy69GsuH8TohBWDh7UHGy5vIF+bj5nKCXLTID9HjD8oB9yZz7bpyiYAHCwcqOlYk5ZeLanlWIua9tXxWPEqko0ntHjfwNYaMaI7nCyd+KzZZ/Sr0Y9vj37LF4e/YFXkKj4M+ZCmHk2LrReLSYthRtgMdl7biYfKg0lNJxGdEs3GyxvZGrOVSuaV6FClA12rdqWuU91HHiszP5OI+AjC48I5Fn+M0wmnydXkYmFiQb8a/RgUOEh/TQQDCkSWH0MnzIiR54Fmns2wNLVkW34CTUHUhRmdsLKNp7UnWllLXGYcXne0wrISxPypMs6V1CuM2TWG6NRoRtYfSY9qPXCxcil8wTi6GG6dhleWgtLYRm6k4lHDoQaL2y9mx7UdzAibwYhtI6hkXon6zvWp51KPIJcgAh0DsTC1IDU3lZ9O/sTK8ytRKpSMCh7FgFoD7oqKvh/yPgdjD/Jv1L+svbSW3yN/x9LUEqWJEqVCiZnCDKWJElOFKbIsczntMlpZi0JSUMOhBr2r9ybENYQQtxCdpjaNGDFSMpamlrTwbMH2W0f4GDBJjILKTQxtltEJK447WmGxGbH3nLDMsu+E7bi6g4n7JmKqMGV+2/k09Wj68CKtBvbOgMrNILBn6RtpxEgpIUkSbSq3oYVnC/67/B9hcWFExEew6/ouAEwVptRyqMXV9Kuk5qbysv/LjAwa+ZDMgpnCjFCvUEK9QknPS2dbzDYuplwkX5NPvrZg0+STp81DI2to49OGBi4NqOdSz6iVZcRIGaCdTzu2xGzhuIUlIWWkQ9LohBXD/YKtFOiGleW6MI1Ww9yIuSw6tYhAx0BmtpqJu7V70YujdkDaDejwVZkoTjRiRN8oTZR0r9ad7tW6A5Cck8yJ2yc4Hn+ciPgI6jrX5d2gd6nhUKPEY9kobejpb7x5MWKkPNHCqwVKhZJtDi6ElJEOSaMTVgxuKjcUkoLYzFhwqCueLKOCrck5yYzfM56DNw/Sy78XHzX+qPjZbMeWCwHagM6lZ6QRI2UIewt7Wnm3opV3K0ObYsSIkVJAZaaiuWdztl3by7ikSzyjEIxOMDphxWCmMMPVylXIVNxNR5ZNJ+y9He9xNvEsnzX9jF7VexW/OOM2RG6Exm8ZhVmNGKmIfPCBoS0wYqRM0s6nHTuv7eRU+jXqlQGZCqMTVgIe1h4iHWluAybmZdIJO3X7FBG3I5jQaELJDhjAyVWgVUPQAP0bZ8SIkdKna1dDW2DESJmkpXdLTFGwTSlRLyMObAw75qwsROPKNJ7WnsIJk6QyO7poVeQqrEyt6F61e8mLZRmO/QJeDcGl5NoXI0b+v707D6+qOvc4/n1JCFMCghAgARGQoIgMigMIDuCAtahtnYfqVa9Wa69j1VKsYmvF3ge1teLFOqA+WlAc6ojiWAiCgsyTooIkQQZFRpmS9/6xd8ohZDiBnLNDzu/zPPs5e1h7n3cv1xNe91pnbdkHLV4cLCKyi6YZTTmmeVcmNmmMr1kSdThKwqqSk5nDqs2r2F68HZrlwprPow5pF2u3rGXC1xMY0nkImRmZVZ9Q8CmsWaynYCJ12dVXB4uI7ObkAwZRWD+dRUUfRx2KkrCq5DTJocRL+Hbzt9B5IBTOCMZU1RKvLHmFbSXbOK9rnK8p+expqN8Euv88sYGJiIjUQid2+Tlp7kz8dlrUoSgJq0puZjA1RdHGIsgbDDgsmRhtUKHikmLGLR7HEa2PoEvzLlWfsHUDzHsJuv8sGOMmIiKSYpo3aUWf4npM3LQUd480FiVhVcjNiknC2vaErLaw+K2IowrkF+VTuLGQ8w8+P74T5r8M2zdB718mNjAREZFa7OSM1iz1rXz5Q7TzhSkJq0Lrxq1Js7Sdg/PzTg0mOt2xLerQGLtoLC0btWRQ+0HxnfDZM9AyD9ofldjAREREarFBLQ7D3Jm49J1I41ASVoX0eum0btw6SMIg6JLcthGWTY40ruUbljO5cDJn551N/bT6VZ+wejEUfBIMyNcM+SJ127BhwSIi5WqZ3Y2/r1zNhe0GRhqHkrA45GTmBN2RAB2Ph/SG8Pnbkcb0wuIXqGf1OLvL2fGd8NnTUC8del6Q2MBEJHonnRQsIlK+Fp057sctNNu4KtIwlITF4T8TtgJkNA4SscVvBXNuRWDLji28tOQlBh4wkNZNWld9wo5tMHssdD0NMlslPkARidasWcEiIuXbv3PwGfE7JJWExSE3M3fnXGEAXQfDD8uCLr4IvL30bdZtXcf5XeMckP/5BNi8RgPyRVLFDTcEi4iUr2k7aH80xDO/ZgIpCYtDbmYujvPtpm+DHV1ODT4/j+ZXkmMXjaVTs04c2ebI+E6Y+Qxk5cBBcQ7gFxERqcvq1YMr3oEe50QbRqTfvo/IycwBoHBT2CXZLBfa9IhkXNi8NfOY9908zut6HhbPAPt1hbDkXeh1IdRLS3yAIiIiEhclYXEonbC1cEPhzp15g2H5NNj8fVJjGbtoLI3SGzGkc5wv6P1kdPB5uF5TJCIiUpsoCYtDduPsnXOFleo6GLwEvkje7Pk/bPmBCUsnMKTTELIy4pjxfss6mP4kdDsLmh+Y8PhEREQkfulRB7AvSK+XTpsmbSjaVLRzZ9vekNk6GBfWM873Nu6l95e/z9birZydF+e0FNOfgK3rob8G6IqklD//OeoIRCQOSsLilJuZu3OuMAgG9XU5BRb8C4q3QzwTpu6lyYWTyW6czcEtDq668PYtMPUR6HRi8LolEUkd/fpFHYGIxEHdkXHaZa6wUnmDgydNy6Yk/Pt3lOxgatFUBuQOiG9A/pyxsHGlnoKJpKIpU4JFRGo1PQmLU05mDqs3r2Zb8TYy0jKCnZ1OgLQGwa8kOx2f0O+fu2YuG7Zv4NjcY6suXFIM+X+Dtr2CiWVFJLUMHRp8fvhhpGGISOX0JCxOu80VBtAgEzoOCMaFJXj2/EkFk0izNI5ue3TVhRe9Dt9/Ccder/dEioiI1FJKwuKU0ySYK6xgY8GuB/IGw/dfwXdLEvr9+UX59GzVk6YZTSsv6A6TH4TmHaHbmQmNSURERPackrA4lc4VtsvgfIC80tnzJyTsu7/78TsWfLcgvq7IpZOg6DPo9xtNzioiIlKLKQmLU3bjbNItffckbL8DoHV3WJy4JGxKUTDANq4kbPKD0KRVMEO+iIiI1FoamB+ntHpptGnShjlr5lDiJdSzmPw179Qg+Zn6f9Chb5CU1eBTqPyifFo0bMEhLQ6pvOCKOfDlezDwDqjfqMa+X0T2MQ8+GHUEIhIHPQmrhl/k/YJpK6Yx/OPhlHjJzgO9L4bmHWDCbTD6OBjRAZ75OXz0v7B0cjBn1x4q8RKmFE6hX06/XRO/8uT/NXgj/JFX7PH3iUgd0KtXsIhIraYnYdVwRfcr2LJjC6PnjKbES7ir712k1UuDFp3gf2bCD8vhm6nwzZTg84M/BSc2aAaHngk9zoMD+gUTvcZpwXcLWLt1Lf1z+1decO1SmP8SHHMtNGq+5zcpIvu+d98NPk86Kdo4RKRSSsKqwcy4rvd1pFkao2aPosRLuLvf3UEiBrBf+2DpcU6wvfn74CXfC/4Fc1+Ez56GZgcEx3ucB626VvmdkwsnYxh9c/pWUfABsDTo++u9vEsR2ef9KfwfQCVhIrWakrA9cE2va8Bg1KxRuDt/PPaPOxOxWI1bQNfTguX0kbDozWAm+8kPwKSRkHsEnPVIpclYfmE+h+5/KC0atqg4oE8fgxlj4OhfQdOcvb9BERERSTiNCdtD1/S8ht/0/g2vffUaQycPZUfJjspPyGgSPAG7+EW4aRGcem/Qffn4KbDs43JPWbd1HXPWzKn8V5Fzx8Mbt0DeaXDKn/bijkRERCSZlITthat6XMX1h1/Pm1+/ydBJQykuKY7vxKzW0PdauHJiMJ3E02cGXZZlfLziY0q8pOLxYF9MhJevhg794Jwnk/IScREREakZSsL20pWHXcmNR9zIW0vf4qGZD1Xv5OYHwhXvQNue8PylMG30LofzC/PJysiie8vuu5/7zVQYdwlkd4ML/qkpKURERPYxGhNWAy7vfjkFGwp4fN7jHNryUE7ucHL8JzduAZe+Ci9eCW/dCusK4KThuBn5hfn0bduX9Hpl/jN9Ow+eOxea5cLFL0HDZjV7QyKybxs9uuoyIhI5JWE15Pajbmfx2sUMmzyMTs060Xm/zvGfXL8RnPt0kIRN+RusL+Lz429k9Y+rd++K/P4reOZnUL8JXPIyZLaq2RsRkX1f16p/eS0i0TN3jzqGaunTp49Pnz496jDKtXLTSs59/VyaZjTludOfIysjq3oXcA9+OfnecJ7YrzkPNM/ivQ0ZZDdoFjztatAUCmfAto3wXxMg++DE3IiI7Nteey34HDIk2jhEBDOb4e59yjumMWE1qHWT1ow8fiQFGwr4/eTf7zqrfjzMYMBNcOHzTG7dkby0JmRndw8SsC3rYeV8aJAFF72oBExEKjZyZLCISK2m7sga1qdNH2458hZGfDKCf8z5B1f3vLra19jUsT8zp/7AJYdeAkfclIAoRUREJGp6EpYAFx58Iad3Op2HZz3MpIJJ1T5/2opp7PAd9M+p4lVFIiIiss9SEpYAZsadfe8kr3ket026jeXrl1fr/PzCfBqlN6J3du8ERSgiIiJRS2gSZmaDzWyxmS0xs9vLOd7AzMaFx6eZ2YGJjCeZGqU34oETH8Awbv7oZraXbI/rvKKNRbzx9Rv0z+1PfU2+KiIiUmclLAkzszTgYeA0oBtwgZl1K1PsCmCtux8EPADcl6h4otA+qz1397ubhd8vZPTsquftKfES/jDlD7g7N2ksmIjsqWeeCRYRqdUS+STsKGCJu3/l7tuAscCZZcqcCTwVro8HBpmZJTCmpBvUYRBndD6Dx+Y+xpzVcyot+8LiF5i2Yho397mZdlntkhShiNQ57dsHi4jUaolMwnKB2MFQBeG+csu4+w5gHbB/2QuZ2VVmNt3Mpq9evTpB4SbO7UfdTnbjbIZOHsrm7ZvLLbN8w3JGzhhJv5x+nJN3TpIjFJE6Zdy4YBGRWi2RSVh5T7TKzgwbTxnc/VF37+PufVq12vdmiM/KyOKe/vewbP0y7p9x/27HS7yEO/LvIM3SGN5vOHXsYaCIJNsjjwSLiNRqiUzCCoDY5+HtgKKKyphZOtAM+D6BMUXmyDZHckm3Sxi3eBz5hfm7HHt24bPMWDmDW4+8lTZN2kQUoYiIiCRTIpOwT4EuZtbRzDKA84FXy5R5Fbg0XD8beN/3tfcoVcP1h19P52aduSP/DtZtXQfA0nVL+etnf+X4dsdz1kFnRRyhiIiIJEvCkrBwjNd1wNvAQuB5d59vZneb2RlhsceB/c1sCXATsNs0FnVJg7QG3DvgXtZuWcs9U++huKSYYfnDaJDWgDv73qluSBERkRSS0NcWufubwJtl9v0hZn0LkFKj0A/Z/xCu6XUND818iPXb1jN79WxGDBhBq8b73lg3ERER2XN6d2QELu9+OR8VfER+UT4nHXASP+n4k6hDEpG6ZPz4qCMQkTgoCYtAer10RgwYwZh5Y7i217XqhhSRmtWyZdQRiEgclIRFpH1We+7oe0fUYYhIXTRmTPB52WVRRiEiVdALvEVE6poxY3YmYiJSaykJExEREYmAkjARERGRCCgJExEREYmAkjARERGRCOjXkSIidc2bb1ZdRkQipyRMRKSuadw46ghEJA7qjhQRqWtGjQoWEanVlISJiNQ1zz8fLCJSqykJExEREYmAkjARERGRCCgJExEREYmAkjARERGRCJi7Rx1DtZjZamBZDV2uJbCmhq61r1IdqA5AdQCqA1AdgOoAVAdQs3XQwd1blXdgn0vCapKZTXf3PlHHESXVgeoAVAegOgDVAagOQHUAyasDdUeKiIiIREBJmIiIiEgEUj0JezTqAGoB1YHqAFQHoDoA1QGoDkB1AEmqg5QeEyYiIiISlVR/EiYiIiISiZRMwsxssJktNrMlZnZ71PFExcyWmtlcM5tlZtOjjicZzOwJM1tlZvNi9rUws4lm9kX42TzKGBOtgjq4y8wKw7Ywy8x+EmWMiWZm7c3sAzNbaGbzzez6cH9KtIVK7j/V2kFDM/vEzGaH9TA83N/RzKaF7WCcmWVEHWuiVFIHY8zs65i20CvqWBPJzNLMbKaZvR5uJ6UNpFwSZmZpwMPAaUA34AIz6xZtVJE60d17pdDPkccAg8vsux14z927AO+F23XZGHavA4AHwrbQy93fTHJMybYDuNndDwGOAX4d/h1IlbZQ0f1DarWDrcBAd+8J9AIGm9kxwH0E9dAFWAtcEWGMiVZRHQD8NqYtzIouxKS4HlgYs52UNpBySRhwFLDE3b9y923AWODMiGOSJHH3fwPfl9l9JvBUuP4UcFZSg0qyCuogpbj7Cnf/LFzfQPDHN5cUaQuV3H9K8cDGcLN+uDgwEBgf7q+z7QAqrYOUYWbtgNOBx8JtI0ltIBWTsFxgecx2ASn4xyfkwDtmNsPMroo6mAi1dvcVEPzjBGRHHE9UrjOzOWF3ZZ3shiuPmR0I9AamkYJtocz9Q4q1g7AbahawCpgIfAn84O47wiJ1/t+IsnXg7qVt4Z6wLTxgZg0iDDHRHgRuBUrC7f1JUhtIxSTMytmXUll/jGPd/XCCrtlfm9lxUQckkXkE6EzQHbECGBltOMlhZpnAi8AN7r4+6niSrZz7T7l24O7F7t4LaEfQU3JIecWSG1Vyla0DM+sO/A44GDgSaAHcFmGICWNmPwVWufuM2N3lFE1IG0jFJKwAaB+z3Q4oiiiWSLl7Ufi5CniZ4A9QKlppZm0Bws9VEceTdO6+MvxDXAL8gxRoC2ZWnyABedbdXwp3p0xbKO/+U7EdlHL3H4APCcbI7Wdm6eGhlPk3IqYOBodd1u7uW4Enqbtt4VjgDDNbSjA8aSDBk7GktIFUTMI+BbqEv3zIAM4HXo04pqQzsyZmllW6DpwCzKv8rDrrVeDScP1S4F8RxhKJ0sQj9DPqeFsIx3w8Dix09/tjDqVEW6jo/lOwHbQys/3C9UbASQTj4z4Azg6L1dl2ABXWwaKY/xkxgvFQdbItuPvv3L2dux9IkA+87+4XkaQ2kJKTtYY/u34QSAOecPd7Ig4p6cysE8HTL4B04LlUqAcz+ydwAtASWAncCbwCPA8cAHwDnOPudXbgegV1cAJBF5QDS4GrS8dG1UVm1h+YBMxl5ziQoQTjoup8W6jk/i8gtdpBD4JB12kEDyWed/e7w7+PYwm64WYCF4dPhOqcSurgfaAVQdfcLOBXMQP46yQzOwG4xd1/mqw2kJJJmIiIiEjUUrE7UkRERCRySsJEREREIqAkTERERCQCSsJEREREIqAkTERERCQCSsJEREREIqAkTESqxcyKzWxWzHJg1DFB8A5EM7uwkmNVTjZpZkNrPrJyv+dDM+tTwbHx4RxFmNkez8sUTsI5YU/PF5HEUxImItX1o7v3ilmWxnNSzCtAEuVAoNwkrBqqnYSZWdpefmfstQ4F0tz9q2qcU269uvtqYIWZHVtT8YlIzVISJiJ7zcwamtmTZjbXzGaa2Ynh/svM7AUzew14J9z3WzP71MzmmNnwmGv8Mtw328yeCfcNMbNp4TXfNbPW4f7jY57EzQxfwTUCGBDuu7GSWC8zs5fMbIKZfWFmfwn3jwAahec/G+672Mw+CfeNLk24zGyjmd1tZtOAoWb2fMz1TwjvFzN7xMymm9n82HutxEWUeT2Kmd0T1snUmPsfY2b3m9kHwH0V1AcEb4O4KI7vFZEouLsWLVq0xL0AxQSvMZkFvBzuuxl4Mlw/mOCVPw2By4ACoEV47BTgUYJXodQDXgeOAw4FFgMtw3Kl5Zuz880eVwIjw/XXgGPD9UyCV2+dALxeQcwHAvPC9cuAr4BmYYzLgPbhsY0x5xwSfk/9cHsU8Mtw3YFzw/X08H6bhNuPELziJPY+0ghejNwj3P4Q6FNOnB8Bh8VsOzAkXP8LMCxcHxPWXVpF9RGu5wJzo24zWrRoKX9JdPeAiNQ9P7p7rzL7+gMPAbj7IjNbBuSFxyb6zvcvnhIuM8PtTKAL0BMY7+5rwmuUlm8HjAtfJpwBfB3uzwfuD59YveTuBcF7huP2nruvAzCzBUAHYHmZMoOAI4BPw2s3AlaFx4qBF8NYd4Rjr4aY2XjgdODWsNy5ZnYVQaLWFugGzKkkrrbA6pjtbQTJFsAM4OSYYy+4e3G4vlt9hPtXATmVfJ+IREjdkSJSEyrLgDaVKXev7xxPdpC7Px7uL+9Ftg8Bf3f3w4CrCZ5c4e4jCJ6MNQKmmtnB1Yw39kW8xVDu/5Aa8FRMrF3d/a7w2JaYBAhgHHAuMBD41N03mFlH4BZgkLv3AN4ojb8SP5Yps93dS+ulbJz/qddK6qNheE0RqYWUhIlITfg34dgjM8sDDiDoXizrbeByM8sMy+aaWTbwHsFTo/3D/S3C8s2AwnD90tKLmFlnd5/r7vcB0wm6QDcApWOh9tR2M6sfrr8HnB3Gh5m1MLMOFZz3IXA48N8ECRlAU4JEaV04luu0OL5/IXBQdYOuoD4geBpZ5a9CRSQaSsJEpCaMAtLMbC5BEnKZu28tW8jd3wGeAz4Oy44Hstx9PnAP8JGZzQbuD0+5C3jBzCYBa2IudYOZzQvL/gi8RdDNtyMcxF7hwPwqPArMMbNn3X0BMAx4x8zmABMJugt3Ez4Ve50g0Xo93DeboNt1PvAEQZdhVd4gGNtWXeXVB8CJ4TVFpBaynU+6RUQkSmbWCPiAYJB9cVXl47jev4Ez3X3tXgcnIjVOSZiISC1iZqcCC939m728TiuCZO6VmolMRGqakjARERGRCGhMmIiIiEgElISJiIiIREBJmIiIiEgElISJiIiIREBJmIiIiEgE/h8gtS4YLWmbrwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10, 6))\n", + "plt.plot(periods, corr_timestep_1, label = 'Lag = i - 1')\n", + "plt.plot(periods, corr_timestep_2, label = 'Lag = i - 2')\n", + "plt.plot(periods, corr_timestep_3, label = 'Lag = i - 3')\n", + "plt.axvline(24, color = \"r\", ls = '--', label = '24h')\n", + "plt.legend()\n", + "plt.xlabel('Forecast Interval (hrs)')\n", + "plt.ylabel('Correlation')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def check_stationarity(series):\n", + " # Copied from https://machinelearningmastery.com/time-series-data-stationary-python/\n", + "\n", + " result = adfuller(series.values)\n", + "\n", + " print('ADF Statistic: %f' % result[0])\n", + " print('p-value: %f' % result[1])\n", + " print('Critical Values:')\n", + " for key, value in result[4].items():\n", + " print('\\t%s: %.3f' % (key, value))\n", + "\n", + " if (result[1] <= 0.05) & (result[4]['5%'] > result[0]):\n", + " print(\"\\u001b[32mStationary\\u001b[0m\")\n", + " else:\n", + " print(\"\\x1b[31mNon-stationary\\x1b[0m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forecast Interval = 6\n", + "ADF Statistic: -33.863838\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.430\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chamath\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n", + " FutureWarning,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forecast Interval = 12\n", + "ADF Statistic: -33.407029\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.430\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chamath\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n", + " FutureWarning,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forecast Interval = 18\n", + "ADF Statistic: -31.926828\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.430\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chamath\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n", + " FutureWarning,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forecast Interval = 24\n", + "ADF Statistic: -29.030458\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.430\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chamath\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n", + " FutureWarning,\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig1, ax1 = plt.subplots(2,2, figsize = (18, 18))\n", + "fig2, ax2 = plt.subplots(2,2, figsize = (18, 18))\n", + "\n", + "i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]}\n", + "\n", + "for i, period_id in enumerate([12, 24, 36, 48]):\n", + " print(f\"Forecast Interval = {round(period_id/2)}\")\n", + "\n", + " df_all_delta = df_all.loc[df_all.period_id == period_id].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + " check_stationarity(df_all_delta.forecast_error_relative)\n", + " \n", + " plot_acf(df_all_delta.forecast_error_relative, lags = 100, ax = ax1[i_subplot[i][0]][i_subplot[i][1]])\n", + " ax1[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag')\n", + " ax1[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Forecast Interval = {round(period_id/2)}h') \n", + " ax1[i_subplot[i][0]][i_subplot[i][1]].set_ylim(0,1)\n", + " \n", + " plot_pacf(df_all_delta.forecast_error_relative, lags = 100, ax = ax2[i_subplot[i][0]][i_subplot[i][1]])\n", + " ax2[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag')\n", + " ax2[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Forecast Interval = {round(period_id/2)}h')\n", + " ax2[i_subplot[i][0]][i_subplot[i][1]].set_ylim(-0.5,1)\n", + " \n", + "#fig1.suptitle('Autocorrelation')\n", + "#fig2.suptitle('Partial Autocorrelation')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check these:\n", + "- https://www.kaggle.com/code/iamleonie/time-series-interpreting-acf-and-pacf\n", + "- https://medium.com/@kis.andras.nandor/understanding-autocorrelation-and-partial-autocorrelation-functions-acf-and-pacf-2998e7e1bcb5\n", + " - https://stackoverflow.com/questions/68398481/how-to-skip-the-the-first-lag-n-days-in-arima-model-python\n", + " - https://www.quantstart.com/articles/Autoregressive-Moving-Average-ARMA-p-q-Models-for-Time-Series-Analysis-Part-3/\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explanatory variables\n", + "Current variables to explain error:\n", + "- Error of previous forecast (with same forecast interval) 24 hours ago (this notebook). 48h ago may also help.\n", + "- Forecasted temperature at future time\n", + "- Day of the week (future date)\n", + "- Month (future date)\n", + "\n", + "Example inputs for predicting total demand for \"2010-02-02 12:00:00\" (future) at \"2010-02-02 00:00:00\" (current):\n", + "- Error of the forecast for \"2010-02-01 12:00:00\" at \"2010-02-01 00:00:00\"\n", + "- Forecast temperature at \"2010-02-01 12:00:00\"\n", + "- Day of the week = \"Monday\"\n", + "- Month = \"February\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Regression\n", + "Forecast interval = 12h\n", + "\n", + "Check: https://realpython.com/linear-regression-in-python/" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Existing model MSE = 55098\n", + "Existing model MAPE = 2.05%\n" + ] + } + ], + "source": [ + "delta = 24\n", + "\n", + "df_lag = df_all.loc[df_all.period_id == delta].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "df_lag_temp = df_lag.copy()[[\"forecast_error\", \"forecast_error_relative\", \"date_time_future\"]].rename({\"forecast_error\" : \"forecast_error_24h_ago\", \n", + " \"forecast_error_relative\": \"forecast_error_relative_24h_ago\",\n", + " \"date_time_future\": \"date_time_future_24h_ago\"}, axis = 1)\n", + "\n", + "df_lag[\"date_time_current_24h_ago\"] = df_all_delta.date_time_current - pd.DateOffset(hours = 24)\n", + "df_lag[\"date_time_future_24h_ago\"] = df_all_delta.date_time_future - pd.DateOffset(hours = 24)\n", + "\n", + "df_lag = df_lag.loc[df_lag.date_time_future_24h_ago >= min(df_lag.date_time_future)]\n", + "df_lag = pd.merge(df_lag, df_lag_temp, on = \"date_time_future_24h_ago\", how = 'left')\n", + "df_lag = df_lag.loc[df_lag.forecast_error_relative_24h_ago.notna()]\n", + "\n", + "mse = (1/len(df_lag))*sum(df_lag.forecast_error**2)\n", + "mae = (1/len(df_lag))*sum(abs(df_lag.forecast_error/df_lag.total_demand))\n", + "print(f\"Existing model MSE = {round(mse)}\")\n", + "print(f\"Existing model MAPE = {round(100*mae,2)}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_lm = df_lag.copy()\n", + "df_lm[\"week_day_name\"] = df_lm.date_time_future.dt.day_name()\n", + "\n", + "df_lm[\"isSaturday\"] = df_lm.week_day_name.apply(lambda x: 1 if x == 'Saturday' else 0)\n", + "df_lm[\"isSunday\"] = df_lm.week_day_name.apply(lambda x: 1 if x == 'Sunday' else 0)\n", + "\n", + "df_lm[\"isDecember\"] = df_lm.date_time_future_month.apply(lambda x: 1 if x == 12 else 0)\n", + "df_lm[\"isJanuary\"] = df_lm.date_time_future_month.apply(lambda x: 1 if x == 1 else 0)\n", + "df_lm[\"isFebruary\"] = df_lm.date_time_future_month.apply(lambda x: 1 if x == 2 else 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.095\n", + "Model: OLS Adj. R-squared: 0.095\n", + "Method: Least Squares F-statistic: 2.049e+04\n", + "Date: Fri, 18 Apr 2025 Prob (F-statistic): 0.00\n", + "Time: 18:46:47 Log-Likelihood: -1.3291e+06\n", + "No. Observations: 194690 AIC: 2.658e+06\n", + "Df Residuals: 194688 BIC: 2.658e+06\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==========================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "const 5.7669 0.506 11.396 0.000 4.775 6.759\n", + "forecast_error_24h_ago 0.3085 0.002 143.160 0.000 0.304 0.313\n", + "==============================================================================\n", + "Omnibus: 25207.646 Durbin-Watson: 0.083\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 279682.601\n", + "Skew: -0.196 Prob(JB): 0.00\n", + "Kurtosis: 8.859 Cond. No. 235.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\n", + "New model MSE = 49785\n", + "New model MAPE = 1.91%\n" + ] + } + ], + "source": [ + "x_columns = [\"forecast_error_24h_ago\"]\n", + "x = sm.add_constant(df_lm[x_columns])\n", + "x = sm.add_constant(x)\n", + "y = np.array(df_lm.forecast_error)\n", + "\n", + "model = sm.OLS(y, x)\n", + "results = model.fit()\n", + "print(results.summary())\n", + "\n", + "df_lm[\"forecast_error_pred\"] = results.predict(x)\n", + "df_lm[\"forecast_demand_new\"] = df_lm.forecast_demand + df_lm.forecast_error_pred\n", + "\n", + "df_lm[\"forecast_demand_new_error\"] = df_lm.forecast_demand_new - df_lm.total_demand\n", + "mse_new = (1/len(df_lm))*sum((df_lm.forecast_demand_new_error)**2)\n", + "mape_new = (1/len(df_lag))*sum(abs(df_lm.forecast_demand_new_error/df_lm.total_demand))\n", + "print(f\"\\nNew model MSE = {round(mse_new)}\")\n", + "print(f\"New model MAPE = {round(100*mape_new,2)}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.101\n", + "Model: OLS Adj. R-squared: 0.101\n", + "Method: Least Squares F-statistic: 3645.\n", + "Date: Fri, 18 Apr 2025 Prob (F-statistic): 0.00\n", + "Time: 18:46:47 Log-Likelihood: -1.3285e+06\n", + "No. Observations: 194690 AIC: 2.657e+06\n", + "Df Residuals: 194683 BIC: 2.657e+06\n", + "Df Model: 6 \n", + "Covariance Type: nonrobust \n", + "==========================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "const -6.8162 0.666 -10.233 0.000 -8.122 -5.511\n", + "forecast_error_24h_ago 0.3032 0.002 140.658 0.000 0.299 0.307\n", + "isSaturday 19.7731 1.462 13.525 0.000 16.908 22.639\n", + "isSunday 32.7209 1.460 22.417 0.000 29.860 35.582\n", + "isDecember 31.9698 1.841 17.361 0.000 28.361 35.579\n", + "isJanuary 33.2391 1.773 18.744 0.000 29.763 36.715\n", + "isFebruary -6.9496 1.845 -3.768 0.000 -10.565 -3.334\n", + "==============================================================================\n", + "Omnibus: 26419.076 Durbin-Watson: 0.084\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 291222.176\n", + "Skew: -0.259 Prob(JB): 0.00\n", + "Kurtosis: 8.969 Cond. No. 945.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\n", + "New model MSE = 49470\n", + "New model MAPE = 1.9%\n" + ] + } + ], + "source": [ + "x_columns = [\"forecast_error_24h_ago\", \"isSaturday\", \"isSunday\", \"isDecember\", \"isJanuary\", \"isFebruary\"]\n", + "x = sm.add_constant(df_lm[x_columns])\n", + "y = np.array(df_lm.forecast_error)\n", + "\n", + "model = sm.OLS(y, x)\n", + "results = model.fit()\n", + "print(results.summary())\n", + "\n", + "df_lm[\"forecast_error_pred\"] = results.predict(x)\n", + "df_lm[\"forecast_demand_new\"] = df_lm.forecast_demand + df_lm.forecast_error_pred\n", + "\n", + "df_lm[\"forecast_demand_new_error\"] = df_lm.forecast_demand_new - df_lm.total_demand\n", + "mse_new = (1/len(df_lm))*sum((df_lm.forecast_demand_new_error)**2)\n", + "mape_new = (1/len(df_lag))*sum(abs(df_lm.forecast_demand_new_error/df_lm.total_demand))\n", + "print(f\"\\nNew model MSE = {round(mse_new)}\")\n", + "print(f\"New model MAPE = {round(100*mape_new,2)}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation = -0.005841855731759133\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_lm[\"forecast_demand_new_error_relative\"] = df_lm.forecast_demand_new_error/df_lm.total_demand\n", + "\n", + "x = df_lm.forecast_demand_new_error_relative[48:len(df_lm)]\n", + "y = df_lm.forecast_demand_new_error_relative[0:len(df_lm)-48]\n", + "\n", + "plt.figure(figsize = (12, 9))\n", + "plt.plot(np.array(x), np.array(y), '.', alpha = 0.3)\n", + "plt.plot(0,0, 'r.')\n", + "plt.xlim(-0.1, 0.1)\n", + "plt.ylim(-0.1, 0.1)\n", + "plt.grid(alpha = 0.5)\n", + "plt.xlabel('Relative Forecast Error at Time = t')\n", + "plt.ylabel('Relative Forecast Error at Time = t - 24h')\n", + "plt.title('Error of Current vs Lag 24 ago | Forecast Interval = {}h '.format(delta/2))\n", + "print(\"Correlation = {}\".format(np.corrcoef(x, y)[0,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/W5 Arima Final.ipynb b/src/W5 Arima Final.ipynb new file mode 100644 index 000000000..57791da35 --- /dev/null +++ b/src/W5 Arima Final.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import warnings\n", + "\n", + "import statsmodels.api as sm\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n", + "\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read and format data\n", + "path = 'data/combined_data_new.csv'\n", + "\n", + "df_all = pd.read_csv(path)\n", + "df_all = df_all.loc[df_all.Temperature.notna()]\n", + "\n", + "df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded\n", + "df_all[\"forecast_error\"] = df_all.total_demand - df_all.forecast_demand\n", + "df_all[\"forecast_error_relative\"] = df_all.forecast_error/df_all.total_demand\n", + "\n", + "df_all[\"date_time_future_hour\"] = df_all.date_time_future.dt.hour" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "period_id = 24\n", + "arima_order = (6,0,2)\n", + "arima_season_order = (1, 0, 1, 7)\n", + "\n", + "train_test_split = 0.7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SARIMA - Without Exogenous Variables" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_predict = pd.DataFrame(columns = [\"period_id\", \"date_time_future\", \"new_forecast\", \"forecast_demand\", \"total_demand\"])\n", + "\n", + "for hour_of_day in set(df_all.date_time_future_hour):\n", + " df_delta = df_all.loc[(df_all.period_id == period_id) & (df_all.date_time_future_hour == hour_of_day) & (df_all.date_time_future.dt.minute == 0)].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + " # Test/Train split\n", + " split_int = int(train_test_split * len(df_delta))\n", + " df_delta_train, df_delta_test = df_delta[:split_int], df_delta[split_int:]\n", + " x_all, x_train, x_test = df_delta.forecast_error, df_delta_train.forecast_error, df_delta_test.forecast_error\n", + "\n", + " # Model - Train Data\n", + " arima_model_train = ARIMA(x_train, order = arima_order, seasonal_order = arima_season_order)\n", + " arima_mode_train_fit = arima_model_train.fit()\n", + "\n", + " # Model - Test Data\n", + " arima_model_test = ARIMA(x_all, order = arima_order, seasonal_order = arima_season_order)\n", + " arima_model_test_fit = arima_model_test.filter(arima_mode_train_fit.params) # Assigns parameter values from train\n", + "\n", + " # Predicted Values\n", + " arima_model_test_predict = arima_model_test_fit.predict().loc[split_int:]\n", + "\n", + " # Calculate new forecast\n", + " df_delta_test[\"predicted_forecast_error\"] = arima_model_test_predict\n", + " df_delta_test[\"new_forecast\"] = df_delta_test.forecast_demand + df_delta_test.predicted_forecast_error\n", + "\n", + " # Model evaluation\n", + " mse_pre = mean_squared_error(df_delta_test.total_demand,df_delta_test.forecast_demand)\n", + " mse_post = mean_squared_error(df_delta_test.total_demand,df_delta_test.new_forecast)\n", + " mape_pre = mean_absolute_percentage_error(df_delta_test.total_demand,df_delta_test.forecast_demand)\n", + " mape_post = mean_absolute_percentage_error(df_delta_test.total_demand,df_delta_test.new_forecast)\n", + "\n", + " df_predict = pd.concat([df_predict, df_delta_test[[\"period_id\", \"date_time_future\", \"new_forecast\", \"forecast_demand\", \"total_demand\"]]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SARIMA - With Exogenous Variables\n", + "- Temperature (forecasted)\n", + "- Humidity (forecasted)\n", + "- Wind_speed (forecasted)\n", + "- Rain (forecasted)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df_predict_with_exog = pd.DataFrame(columns = [\"period_id\", \"date_time_future\", \"new_forecast\", \"forecast_demand\", \"total_demand\"])\n", + "exog_vars = [\"Temperature\", \"Humidity\", \"Wind_speed\", \"Rain\"]\n", + "\n", + "for hour_of_day in set(df_all.date_time_future_hour):\n", + " df_delta = df_all.loc[(df_all.period_id == period_id) & (df_all.date_time_future_hour == hour_of_day) & (df_all.date_time_future.dt.minute == 0)].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + " # Test/Train split\n", + " split_int = int(train_test_split * len(df_delta))\n", + " df_delta_train, df_delta_test = df_delta[:split_int], df_delta[split_int:]\n", + " x_all, x_train, x_test = df_delta.forecast_error, df_delta_train.forecast_error, df_delta_test.forecast_error\n", + " exog_all, exog_train, exog_test = df_delta[exog_vars], df_delta_train[exog_vars], df_delta_test[exog_vars]\n", + "\n", + " # Model - Train Data\n", + " arima_model_train = ARIMA(x_train, exog = exog_train, order = arima_order, seasonal_order = arima_season_order)\n", + " arima_mode_train_fit = arima_model_train.fit()\n", + "\n", + " # Model - Test Data\n", + " arima_model_test = ARIMA(x_all, exog = exog_all, order = arima_order, seasonal_order = arima_season_order)\n", + " arima_model_test_fit = arima_model_test.filter(arima_mode_train_fit.params) # Assigns parameter values from train\n", + "\n", + " # Predicted Values\n", + " arima_model_test_predict = arima_model_test_fit.predict().loc[split_int:]\n", + "\n", + " # Calculate new forecast\n", + " df_delta_test[\"predicted_forecast_error\"] = arima_model_test_predict\n", + " df_delta_test[\"new_forecast\"] = df_delta_test.forecast_demand + df_delta_test.predicted_forecast_error\n", + "\n", + " # Model evaluation\n", + " mse_pre = mean_squared_error(df_delta_test.total_demand,df_delta_test.forecast_demand)\n", + " mse_post = mean_squared_error(df_delta_test.total_demand,df_delta_test.new_forecast)\n", + " mape_pre = mean_absolute_percentage_error(df_delta_test.total_demand,df_delta_test.forecast_demand)\n", + " mape_post = mean_absolute_percentage_error(df_delta_test.total_demand,df_delta_test.new_forecast)\n", + "\n", + " df_predict_with_exog = pd.concat([df_predict_with_exog, df_delta_test[[\"period_id\", \"date_time_future\", \"new_forecast\", \"forecast_demand\", \"total_demand\"]]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE Old = 55078.33198205807, MSE SARIMA (no Exog) = 49519.31974525575, MSE SARIMA (with Exog) = 49165.3293292465\n", + "MAPE Old = 2.178%, MAPE SARIMA (no Exog) = 2.049%, MAPE SARIMA (with Exog) = 2.082%\n" + ] + } + ], + "source": [ + "df_predict[\"forecast_error_old\"] = df_predict.total_demand - df_predict.forecast_demand\n", + "df_predict[\"forecast_error_new\"] = df_predict.total_demand - df_predict.new_forecast\n", + "df_predict_with_exog[\"forecast_error_new\"] = df_predict_with_exog.total_demand - df_predict_with_exog.new_forecast\n", + "\n", + "mse_pre = mean_squared_error(df_predict.total_demand, df_predict.forecast_demand)\n", + "mse_sarima = mean_squared_error(df_predict.total_demand, df_predict.new_forecast)\n", + "mse_sarima_with_exog = mean_squared_error(df_predict_with_exog.total_demand, df_predict_with_exog.new_forecast)\n", + "\n", + "mape_pre = mean_absolute_percentage_error(df_predict.total_demand, df_predict.forecast_demand)\n", + "mape_sarima = mean_absolute_percentage_error(df_predict.total_demand, df_predict.new_forecast)\n", + "mape_sarima_with_exog = mean_absolute_percentage_error(df_predict_with_exog.total_demand, df_predict_with_exog.new_forecast)\n", + "\n", + "print(f'MSE Old = {mse_pre}, MSE SARIMA (no Exog) = {mse_sarima}, MSE SARIMA (with Exog) = {mse_sarima_with_exog}')\n", + "print(f'MAPE Old = {round(100*mape_pre,3)}%, MAPE SARIMA (no Exog) = {round(100*mape_sarima,3)}%, MAPE SARIMA (with Exog) = {round(100*mape_sarima_with_exog,3)}%')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize = (10,7))\n", + "sns.kdeplot(abs(df_predict.forecast_error_old), label = 'old')\n", + "sns.kdeplot(abs(df_predict.forecast_error_new), label = 'new - SARIMA (no Exog)')\n", + "sns.kdeplot(abs(df_predict_with_exog.forecast_error_new), label = 'new - SARIMA (with Exog)')\n", + "plt.xlabel('abs(Forecast Error)')\n", + "plt.legend()\n", + "plt.xlim(0, 1000);" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Sample Forecast (no Exog)')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize = (15, 6))\n", + "df_plot = df_predict.iloc[100:130]\n", + "plt.plot(df_plot.date_time_future, df_plot.forecast_demand, label = 'Old Forecast')\n", + "plt.plot(df_plot.date_time_future, df_plot.new_forecast, label = 'New Forecast')\n", + "plt.plot(df_plot.date_time_future, df_plot.total_demand, label = 'Actual')\n", + "plt.legend()\n", + "plt.title('Sample Forecast (no Exog)')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df_predict[[\"date_time_future\", \"total_demand\", \"forecast_demand\", \"new_forecast\"]].rename({\"new_forecast\": \"sarima_prediction\"}, axis = 1).to_csv(\"data/results_SARIMA.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/W5 Arima Tuning.ipynb b/src/W5 Arima Tuning.ipynb new file mode 100644 index 000000000..4891f8fb3 --- /dev/null +++ b/src/W5 Arima Tuning.ipynb @@ -0,0 +1,459 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import statsmodels.api as sm\n", + "import warnings\n", + "\n", + "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", + "from statsmodels.tsa.stattools import adfuller\n", + "from matplotlib.pyplot import figure\n", + "from sklearn.linear_model import LinearRegression\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n", + "\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read and format data\n", + "path = 'data/combined_data_new.csv'\n", + "df_all = pd.read_csv(path)\n", + "\n", + "df_all.date_time_current = pd.to_datetime(df_all.date_time_current, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_future = pd.to_datetime(df_all.date_time_future, format = \"%Y-%m-%d %H:%M:%S\")\n", + "df_all.date_time_current_rounded = pd.to_datetime(df_all.date_time_current_rounded, format = \"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "df_all[\"forecast_interval\"] = df_all.date_time_future - df_all.date_time_current_rounded\n", + "df_all[\"forecast_error\"] = df_all.total_demand - df_all.forecast_demand\n", + "\n", + "df_all[\"forecast_error_relative\"] = df_all.forecast_error/df_all.total_demand\n", + "\n", + "df_all[\"date_time_future_month\"] = df_all.date_time_future.dt.month\n", + "df_all[\"date_time_future_year\"] = df_all.date_time_future.dt.year\n", + "df_all[\"date_time_future_weekday\"] = df_all.date_time_future.dt.dayofweek\n", + "df_all[\"date_time_future_yearTime\"] = df_all.date_time_future_year.apply(lambda x: pd.DateOffset(years=x-2000))\n", + "df_all[\"date_time_future_hour\"] = df_all.date_time_future.dt.hour" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def check_stationarity(series):\n", + " # Copied from https://machinelearningmastery.com/time-series-data-stationary-python/\n", + "\n", + " result = adfuller(series.values)\n", + "\n", + " print('ADF Statistic: %f' % result[0])\n", + " print('p-value: %f' % result[1])\n", + " print('Critical Values:')\n", + " for key, value in result[4].items():\n", + " print('\\t%s: %.3f' % (key, value))\n", + "\n", + " if (result[1] <= 0.05) & (result[4]['5%'] > result[0]):\n", + " print(\"\\u001b[32mStationary\\u001b[0m\")\n", + " else:\n", + " print(\"\\x1b[31mNon-stationary\\x1b[0m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADF Statistic: -33.406571\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.430\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAHiCAYAAAAuz5CZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de5xcdZ3n/9enu+lcgBiuMYFwU0TASxh7ZbzMbFZAwRnFcVyFmVVU/DFeZx1nd9XRhzo4F5xdx9FZH7OygiLj4G3WMY4gIhp9OIpDkKjcLwFMSEgiIQZITJPO5/dHnYbqSlV1fbuqU53k9Xw8+tFV53zPOd869a1T7zrne86JzESSJElS5wb6XQFJkiRpT2OIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiWpCxHxZxHx6Q7LfjYi/mK66zTTRcTrI+IHXUx/VUSc18s6SVIpQ7SkvVpE3BsR2yLikYhYHxGfiYgDpjivpRGxpn5YZv5VZr6pN7V9fBkZEf+jcLoPRcQ/9qoeM0Wz15WZZ2XmZf2qkySBIVrSvuFlmXkA8BvAfwDeXzqDiBjqea2aOw/YVP2f0aJmYLJhkrQ3ckMnaZ+RmfcDVwHPAIiIN0TErRHxcESsiog/Gi87vtc5It4dEQ8AV1TTLqr2aj8SEYsa95RGxJcj4oGI+FVEfD8iTu60fhExF3gV8Dbg+IgYaaxPQ/l7I+L0iDgT+DPgNVW9flqNXxQRyyJiU0TcFRH/X920g1VXlLur139DRCyuxj0/Iq6vXsP1EfH8uumWR8RfRsS/AVuB41oMe1JEXBIR6yLi/oj4i4gYbPG6Px4RqyNiS1WP36qGt3pdyyPiTdXjgYh4f0TcFxEbIuJzEfGkatwx1V798yLiFxHxy4h4X6fvhyS1Y4iWtM+oQuJLgRurQRuA3wXmAW8APhYRv1E3yZOBg4GjgdcBZwFrM/OA6m9tk8VcBRwPHA78BPh8QRV/H3gE+DJwdbXMSWXmN4G/Ar5Y1evZ1agrgDXAImrh/K8i4rRq3LuAc6mtj3nAG4GtEXEw8A3gE8AhwN8C34iIQ+oW+VrgAuBA4L4Wwy4DdgBPBU4BXgy06vZyPbCE2rr+J+DLETG7zeuq9/rq7z8BxwEHAP+7ocwLgROA04APRMSJLeohSR0zREvaF/xLRGwGfgB8j1owIzO/kZl3Z833gG8Bv1U33U7gg5m5PTO3dbKgzLw0Mx/OzO3Ah4Bnj+8Z7cB51ALjGLUweW5E7NfhtBNUPxheCLw7M3+dmSuBT1MLu1ALtO/PzNur1//TzHwQ+B3gzsy8PDN3ZOYVwG3Ay+pm/9nMvLka/1jjMGph+CzgnZn5aGZuAD4GnNOsrpn5j5n5YDW/jwKzqIXeTvwh8LeZuSozHwHeC5zT0P3mzzNzW2b+FPgp0CyMS1IRQ7SkfcErMnN+Zh6dmW8dD8QRcVZEXFd1d9hMba/soXXTbczMX3e6kKqLxEVVF4ktwL3VqEPbTDY+7WJqe1PH91x/DZhNLdROxSJgU2Y+XDfsPuCI6vFi4O4W093XMKx+OoDVTaarH3Y0sB+wLiI2V+v2U9T2zu8iIv606lbzq6rsk+hgnbWo733AELCgbtgDdY+3UttbLUldMURL2idFxCzgn4H/BSzIzPnAlUDUFcuGyRqfN/oD4GzgdGpB8JjxxXVQpddS2yZ/veqDvYpaiB7v0vEoMLeu/oPAYW3qthY4OCIOrBt2FHB/9Xg18JQm9VhLLQTXq5+u2bIah60GtgOHVj9e5mfmvMzcpX941f/53cCrgYOq9+FXPLHOJlvnjfU9ilo3kvWTTCdJXTFES9pXDVPrNrAR2BERZ1Hrt9vOeuCQNt0zDqQWHh+kFnj/qqA+rwP+nFrf4PG/3wd+p+qPfAcwOyJ+p+ri8f6q/vV1O2b8yhiZuRr4IfDXETE7Ip4FnM8Te7o/DXw4Io6vrqjxrGo5VwJPi4g/iIihiHgNcBLwr52+kMxcR61rzEcjYl518t9TIuI/Nil+ILXQuxEYiogPUOuj3fR1NXEF8CcRcWzULl043od6R6f1laSpMERL2idV3Rz+GPgS8BC1vcjLJpnmNmqhbVXVTWFRQ5HPUetOcD9wC3BdJ3WJiN+kttf6k5n5QN3fMuAu4NzM/BXwVmrh935qe6brr9bx5er/gxHxk+rxudV81wJfpda/+5pq3N9Wr/1bwBbgEmBO1S/6d4E/pfZj4H8Av5uZv+zktdR5HbUfKrdQW79fARY2KXc1tZMx76C27n7NxK4hzV5XvUuBy4HvA/dU07+jsK6SVCwyJztSJkmSJKmee6IlSZKkQj0J0RFxaXWR+5tajI+I+ER1sf+f1V+HtboI/p3V34y/Q5ckSZLUqz3RnwXObDP+LGo3Hzie2sX4/wGguqj/B4FTgecCH4yIg3pUJ0mSJGla9CREZ+b3gU1tipwNfK66oP91wPyIWAi8BLgmMzdl5kPANbQP45IkSVLf7a4+0Ucw8WzrNdWwVsMlSZKkGWto8iI90exGA9lm+K4ziLiAWlcQ9t9//+c8/elP713t2tjw8HbWb9n1hmUL5s3m8ANnNZlCkiRJe4Mbbrjhl5l5WLNxuytEr6F2i9lxR1K7bukaYGnD8OXNZpCZFwMXA4yMjOSKFSumo567uPbW9bzjihvZOjr2+LC5w4P8/bmncNqJC9pMKUmSpD1ZRNzXatzu6s6xDHhddZWO3wR+Vd3R6mrgxRFxUHVC4YurYTPG0hMOZ8ni+cTYKORO5g4PsmTxfJaecHi/qyZJkqQ+6cme6Ii4gtoe5UMjYg21K27sB5CZ/4fabWRfSu3OW1uBN1TjNkXEh4Hrq1ldmJntTlDc7QYHgsvPP5XnvfJ8Rvc/nI++/09YesLhDA4064kiSZKkfUFPQnRmnjvJ+ATe1mLcpdRu2zpjDQ4EczevYu7mVXbhkCRJkncslCRJkkrtrhML9xpjO5Plt2/g5rVbOHnRPLt2SJIk7YMM0QWS4LWX/JiVqzezbXSMOdVJhpeff6pBWpIkaR9id44C2+Yfy8rVm9k6OkYCW0fHWLl6M8tv39DvqkmSJGk3MkQXGN1/AdvqrhcNsG10jFvWbulTjSRJktQPhugCw4+uZ87w4IRhc4YHOWnRvD7VSJIkSf1giC4wZ/M93nhFkiRJhugSQXL5+ady2J1fZ/6af+Pvzz3FkwolSZL2QV6do5A3XpEkSZJ7oiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRCPQnREXFmRNweEXdFxHuajP9YRKys/u6IiM1148bqxi3rRX0kSZKk6TTU7QwiYhD4JHAGsAa4PiKWZeYt42Uy80/qyr8DOKVuFtsyc0m39ZAkSZJ2l17siX4ucFdmrsrMUeALwNltyp8LXNGD5UqSJEl90YsQfQSwuu75mmrYLiLiaOBY4Dt1g2dHxIqIuC4iXtFqIRFxQVVuxcaNG3tQbUmSJGlqehGio8mwbFH2HOArmTlWN+yozBwB/gD4u4h4SrMJM/PizBzJzJHDDjusuxpLkiRJXehFiF4DLK57fiSwtkXZc2joypGZa6v/q4DlTOwvLUmSJM04vQjR1wPHR8SxETFMLSjvcpWNiDgBOAj4Ud2wgyJiVvX4UOAFwC2N00qSJEkzSddX58jMHRHxduBqYBC4NDNvjogLgRWZOR6ozwW+kJn1XT1OBD4VETupBfqL6q/qIUmSJM1EXYdogMy8EriyYdgHGp5/qMl0PwSe2Ys6SJIkSbuLdyyUJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSCvUkREfEmRFxe0TcFRHvaTL+9RGxMSJWVn9vqht3XkTcWf2d14v6SJIkSdNpqNsZRMQg8EngDGANcH1ELMvMWxqKfjEz394w7cHAB4ERIIEbqmkf6rZekiRJ0nTpxZ7o5wJ3ZeaqzBwFvgCc3eG0LwGuycxNVXC+BjizB3WSJEmSpk0vQvQRwOq652uqYY1+PyJ+FhFfiYjFhdNKkiRJM0YvQnQ0GZYNz78OHJOZzwK+DVxWMG2tYMQFEbEiIlZs3LhxypWVJEmSutWLEL0GWFz3/EhgbX2BzHwwM7dXT/8v8JxOp62bx8WZOZKZI4cddlgPqi1JkiRNTS9C9PXA8RFxbEQMA+cAy+oLRMTCuqcvB26tHl8NvDgiDoqIg4AXV8MkSZKkGavrq3Nk5o6IeDu18DsIXJqZN0fEhcCKzFwG/HFEvBzYAWwCXl9NuykiPkwtiANcmJmbuq2TJEmSNJ26DtEAmXklcGXDsA/UPX4v8N4W014KXNqLekiSJEm7g3cslCRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgr1JERHxJkRcXtE3BUR72ky/l0RcUtE/Cwiro2Io+vGjUXEyupvWS/qI0mSJE2noW5nEBGDwCeBM4A1wPURsSwzb6krdiMwkplbI+ItwN8Ar6nGbcvMJd3WQ5IkSdpderEn+rnAXZm5KjNHgS8AZ9cXyMzvZubW6ul1wJE9WK4kSZLUF70I0UcAq+uer6mGtXI+cFXd89kRsSIirouIV/SgPpIkSdK06ro7BxBNhmXTghH/BRgB/mPd4KMyc21EHAd8JyJ+npl3N5n2AuACgKOOOqr7WkuSJElT1Is90WuAxXXPjwTWNhaKiNOB9wEvz8zt48Mzc231fxWwHDil2UIy8+LMHMnMkcMOO6wH1ZYkSZKmphch+nrg+Ig4NiKGgXOACVfZiIhTgE9RC9Ab6oYfFBGzqseHAi8A6k9IlCRJkmacrrtzZOaOiHg7cDUwCFyamTdHxIXAisxcBvxP4ADgyxEB8IvMfDlwIvCpiNhJLdBf1HBVD0mSJGnG6UWfaDLzSuDKhmEfqHt8eovpfgg8sxd1kCRJknYX71goSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUaKjfFdDebWxnsvz2Ddy8dgsnL5rH0hMOZ3Ag+l0tSZKkrhiiNW3GdiavveTHrFy9mW2jY8wZHmTJ4vlcfv6pBmlJkrRHszuHps3y2zewcvVmto6OkcDW0TFWrt7M8ts39LtqkiRJXTFEq+fGdibX3rqeS35wD9tGxyaM2zY6xi1rt/SpZt0bf22fuPZOrr11PWM7s99VkiRJfWB3DvVUfReOrQ0BGmDO8CAnLZrXh5o116rPdrPhwITuKbP3G+DoQ/bnzGc8mWce8ST7e0uStA8xRKun6rtwPC5re2vnzhpiyeL5jwfSyXRyUmI3Jy429tkeD8UvPnkB19y8nvs2bZ3Ql/sNLzhmwmvb9thObnvgYW5/4GH7e0uStI8xRKsnxsNssy4ckMze8gv+/o9f1XHI7eSkxG5PXGwM/OOh+LYHHp5QbuvoGDfc9xCbHh1t8trYpb/3aScumHTZUq95JRx1ql1bsR1JnTNE63GlG8/x8j+//1dcfdMD3Ldpa9MuHLFzB/PW3cBpJ76t47o0BtzxIPunX1rJy569iKUnHN60zI2/eIiPf/sOhgYHOPHJB0LAresebvr4M/92b9NQ3Mz2HTt3CdeNto6OcckP7gEo3iPul9beZ7re1066GnlkZM+zu7YD7XY+gO1IKmGI7qPdGZ4m6/tbH4Q72XhO1ve5vgvHzo3rmLP5no7q1i7gbt+xk39ZuZarb36Aow/Zn4Pm7rfriYuP7eQfvnc3j40l49XembR8PCXVayN2XS8/uvtBbvzFQx33lW7VpaTbftYlfb339i/HbroFTXVamJ4w0ioANXY16tWRkX2xvfTDZMG2l+9Bs50P9VdNmo52pN1nd277/Z4xRPfNVLoi9GJPcau+v41BeLKNZ9O+zxM80YXjwrd8hKB5Ym0M450E3PFuF608Npa7zKPV41pVW4TilmE5mbXlFxx3wjO57f4HyYEhiIFqTFlf6VZdSjrtZ10S4D77hufy+s/8+6RtbqYf6m32o6vxaENJmG31WexkfZWE2vqjJFPpv7/89g18/adrueG+h9i+Y+fj8125ejPf+Nm6llfCKQ0/U/1h3Wo+09lWuj0nYjrPuSiZT6sjb+/64o3csf6Rpu/B+HSl3wftrpqU1eNm4zppR52Eqsk+s+3KT3f469d8prK8Zuvx5rVbJv2u79VnGbr7nilZ1kwO4D0J0RFxJvBxYBD4dGZe1DB+FvA54DnAg8BrMvPeatx7gfOBMeCPM/PqXtRppmjVINrtDRjvqtDNnq12e4pb9f1t1Gzj2b7v8xPqu3B8uEWAhl2/PDoOuE3K7Dc08HiALrNrKJ4zPMSOzQ+w39aNjC185uOhZfy1PWndDfzrJ9/K8155PlsWPoftTzpml1fZSV/pm9du6aifdX2bmGyD+bQFB+wStG647yFed8mPuXH15l2G/+mXVvI7z1zYdJ5zhgd59pFP4o0vPLbpuGZf6JN1o2n3uOQKKY0/usYfZzLpuqh/zd/42bopr69W0zbrPz9+lGTHWBYdcZjsqM/4cuYMD04YPzw0wN0bH+HaW9dP6VyEZj+sG19/q2AzlT2rnQSnxjKf+eG9kx7FKb3SzskL57X9fNWX6aRddzKfVkfevvbTdU3fg3bheipHDuuvmjTVdtTJidqdfGZblc+k+EdEszbV6fvazdWaWs2nVZ062SbWL6/Vemz8/pzsPJ/Gz3In7Xeyo1//+zt3TvloxnQdmZ1OkTnVY9rVDCIGgTuAM4A1wPXAuZl5S12ZtwLPysw3R8Q5wO9l5msi4iTgCuC5wCLg28DTMrNtR9WRkZFcsWJFV/UutXTpUgCWL18+4XE77RrEvb98lK+tXDsheAXwztOP58f3bGq6Z+u/fmHlhI3b3OFB/v7cU5qG7uW3b+AdV9zYZk/x5BrnP1nf54ldOFax4NYv873l3226vurD+I/ufrB1zM6d7Ld1IwNPWjghyDaWmb3lF1zwe6fz6R/cU/yaY2yUw+78Oj/6f5fwvFeez+j+h/PR9/8JF77lHACO+MO/5kd3rCMHhpg7a79dXtvW+cfxyLNe3XK5AbzrjKfxjtOO32Xctbeun/R9et5xB7N562NNv4R6pd08BwL2Gxxouv5nDQ1w5skLHv9Cb7dxn+xxqy/SyT4H0/Gap3PaegGThrNv/GwdV930QMv2P3d4kI+/Zgmf+eG9j7fTgYHqyEjSUbjsdFmNr79x/uP1bzafxrbSSdBq1yaGhwZ4bGznLu9B/TptnKb+x9XVN69v+jo7eW9L23gn8+nWrKEBznrGk5uGoqbva8NVk+r7RDdrR5Mdufr6T9d21HZ68TpbtaNWP4I63W520u5K2tD4emu2I6KTbWXjDoFWy+vGVNrvrKEBjj10f25/4OFdMszznnJI0+/05z/lEN7w/GPa/khp1YaC1u1vd4iIGzJzpOm4HoTo5wEfysyXVM/fC5CZf11X5uqqzI8iYgh4ADgMeE992fpy7ZZ58NEn5hl/dmlX9S618qcrAVjy7CXc8JMbATj+pGe0nebhX+/g/s3b6HQVR8DBc4fZtHV0wjQRcOCsIbb8escu08zZb4CdCaM7dj7eaGcNDTA4EFMLGtWCYyCYs98giw+azeqHfs22x8aav476PcU7thOPbeOIRQtZt+o2gto6uvOWmwB46okn88j2MbaN7uCR7WMT6ty6PjuJR3/JnIMXsnX7Y0A06Xaxk4FHf8nTnnocv9i0bWK5+vo1eRwDQY5uY/CRDTytrq6N9b7jrlUwOMyiJq8tgVkLjmtZvwg4Yv4cDpy964GfzNy1zmqq3edgxurkSEoX8x3/nB518BwA7rhrFbnfXJh1QMvP1qyhAQ6YNdj5Z1C7z3S1lxbLih2/5ojDD+aAWYNEtczMbNuO5s0e4sDZQwSw7bEx21GHAvb6ddQqw7RSui0aCHjq4Qdw0NzhntS3U1968/OnNUS/CjgzM99UPX8tcGpmvr2uzE1VmTXV87uBU4EPAddl5j9Wwy8BrsrMrzRZzgXABQAHLHzKc176wcu7qnc3tvz6sccfNwtd4493zn4SO+fMbzuvCR+sHdshd8J+cyYWyoSxUWJo1sRGVtyXd3KzhgY4cPYQs4YGWLfmF5N+IUNtT9jB+w9P2BA3mkpYjGBCQHhk+xi/Ht3Bw3UfuPoyEUFm8sj2MbY/VttTFdQOi7Z6PGu/wbb17tT4chvrB3T846LptHpCJjH6KMw+oOMfpjPB3OFB5uw3yKZHR3v6vs6bPcS8OftNaFMAGx/ezi8fGW094e4Maio2a2hgt2wD6n/c1393jWvajlrtlNA+LwJydBsDj2xg9oLjWu94qzeFNnTk/DkccdCcyQv2ULsQ3Ys+0c1efeOqa1Wmk2lrAzMvBi6GWneOL/7R80rqOG2WLv0AAFd96q27PJ7sMD/A2UsWceyh+/PNmx7gtvtHycFZzQsODhNRW2GPH15p1fAa94TyxCGhl5z8ZL518679wc56xkKeccTEvldrH5xfO2mujfEuH5P1dRrvtjB+Al4zL3jKIby+Otxz27qHOWmSfm63rN3Sskw/jdfvpvt/9cT7Onsemx4dZb8BWHDrlye0l29+avku007azaUDs4YGOGXx/An9etsNbxTQ8pB5X+zcwZwt93H00b856eHQRp285m7WV6sy9V2i2l7NpsDchsOaS5deCNS2O9BBF6GCL6w51fbhhAUH8s2bp/9Qfal2XY32RPVdcxq7AfbyPdi1DT3x3TWuaTuqbztTDM/97PKyN5ms+0er7/rp+Cy/4CmH8MYXHsuFbzmHILn2H77b2fdYYRuaOzzIn5998m6/UsyX3tx6XC9C9Bpgcd3zI4G1LcqsqbpzPAnY1OG0e5yxncnW+cexff8FHH3w3JZ9iOcOD/KyZy8C4FPfX0UOtjhEUTW0ndm6L1I7z68a+HjY/OPTjm8bQq+9dT0rV29uXR8m9lHq5A6ErU6gGzd3eJA3vvDYxz8cZ5z05JZlBweC005cMGMvuTReP5j4vm4dHYMDFvHLp5zFtbeuJ4ldrlpSP22rwNXJBnOyM6Xrh7fqg3rWMxZy0sLmJ2+12xBPtU/0ZF+kOwcG2Xroidz74KMT6jf+o+vpVf/PW9Zu4ZtNToRp95q7WV+TTTv++br8/FMn/LhqeW5BC+N9Xsevk97qh+PSEw5nyeL5XQX2xmUBbCy8is74fDr5wdZosvmPb3/G+5rWv+el67RVn/5Wn6/Sfv+dzKe+Hb3oxAW86MQFu2yj4Yn3YKqhqNM2BFNvR+36LHf6mW1WvtP++q2Uvq9Tfb2N72u7HRFT7Y9c3/e9fj027nxq9l0PrT/Lk7XfZm2t/rt7/CICnXyPlazTxu3pTNKL7hxD1E4sPA24n9qJhX+QmTfXlXkb8My6EwtfmZmvjoiTgX/iiRMLrwWOn4knFrbSeMJhEhNORJszPNQ25Fx+/ql88rt38bFr7tglFB910BxWP7Rtl477Zy9ZxLduWd9Rw+x0T3G9T1x7Z9P6wBN7pur3XHeyF7jV3rF+nzAwnZquxxYnXzZqdVJq45fQSU1Ozmj8cdTJ8FYb4fG6NNsQd1K/+vlO9rjVF2mrDXe7dt3Na56OaVvVrzFQt/oSa/cZaXfibqeBvZNlNXv9jeG1kx8XnRwVG29D7cJV4/an2Wtut7xmV3ro9L0tadedzqeTo2qTfRZ72Ybql9fJSYON2/L6dTrV19buii+d/DAp3W5Oti2aSht6eouryHSyrWy1Q6AX15svacuttvuN9WlsR6WXsO22DU2XaT2xsFrAS4G/o3aJu0sz8y8j4kJgRWYui4jZwOXAKdT2QLLxJtsAABOQSURBVJ+Tmauqad8HvBHYAbwzM6+abHkzOUQ368LReJWLxgbRLGDOHR7kTS88dperTbQ73NfuA17S8JrVp2QPRjPtQmFJGN+TTHZYffyqIP/+L81Pkt1Tuq30sn7N5tnsR2bQ+oone5pOvtDard/JrhY0WbhsFzC6qX83P/Bazb/TunWzvD3R7mhDzcJQY1Cd7m15tzsEer2sbttjN/Xol3brpXZ1qwV89P3vnNKPlJmaB6Y9RO9uMzlEbz7iefxq8QuLvvCncrMHaP4LrRcfuKncCKbT+c6kjcF0m/yujjuZv+bfWPn5i3Ydp8e1+pFZeoRlb9XpJTdh3/sMqjOdtKHpCKraO4x/19VfCrbTG4XtCduidiHaOxb22PCj63e5WH39heybqe8v2digWg0HmvYL7kV/4cmW2818Z3Jf5l6rX4/NDofGzh0MP7qhjzXcM9T3zZzJfeP2BPvaZ1CTGz+HZ3T/BW1vqNKs7bQ7d0X7jvGbptWf/zPZDVb2lm2RIbrH5my+h6dO4Qu/VYPqV0PbWxp4v42vx6UnHM6Ghyfu3d+5cR1zNt/T7yrOeNP1o07a143vQdx4/MvIgSHeccWNe+X5KZpezS4cUHK7+D2ZIbrHgvQLX7toFgQvfMtHdrk6h5rzR53Ue1PZgyg1OnnRvOIj8HsLQ/Q08AtfzTS2iw8boNWlTg/FS83sy3sQ1Tv7cpc7Q7Qk7YE8FK9u7ct7ENU7+3KXO0O0JO2BPBSvbu3LexDVW/vqEXhDtCTtgTwUr27ty3sQpV4wREvSHshD8eqFfXUPotQLA/2ugCSp3Pih+LnDgwS1G9B4KF6Sdh/3REvSHshD8ZLUX4ZoSdpDeShekvrH7hySJElSIUO0JEmSVMgQLUmSJBUyREuSJEmFDNGSJElSIUN0j4ztTLbOP47NRzyPa29dz9jO7HeVJEmSNE28xF0PJMFrL/kxG49/GTkwxDuuuJEli+dz+fmnes1WSZKkvZB7ontg2/xjWbl6Mzk4DDHA1tExVq7ezPLbN/S7apIkSZoGhugeGN1/AdtGxyYM2zY6xi1rt/SpRpIkSZpOhugeGH50PXOGBycMmzM8yEmL5vWpRpIkSZpOhugemLP5HpYsns/c4UECmDs8yJLF81l6wuH9rpokSZKmgScW9kCQXH7+qSy/fQO3rN3CSYvmsfSEwz2pUJIkaS9liO6RwYHgtBMXcNqJC/pdFe0Bxi+JOLr/Aq69db0/uiRJ2sPYnaMLXhtaUzG2Mx+/JOLmI5/PO664kdde8mPbjyRJexBD9BQZhDRVy2/f4CURJUnawxmip8ggpKm6ee0WL4koSdIezhA9RQYhTdXJi+Z5SURJkvZwhugpMghpqpaecLiXRJQkaQ/n1TmmaDwIrVy9mW2jY8wxCKlDgwPhJRElSdrDReaedyLcyMhIrlixot/VYGxnGoQkSZL2UhFxQ2aONBvnnugueG1oSZKkfZN9oiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRCXYXoiDg4Iq6JiDur/wc1KbMkIn4UETdHxM8i4jV14z4bEfdExMrqb0k39ZEkSZJ2h273RL8HuDYzjweurZ432gq8LjNPBs4E/i4i5teN/++ZuaT6W9llfSRJkqRp122IPhu4rHp8GfCKxgKZeUdm3lk9XgtsAA7rcrmSJElS33Qbohdk5jqA6v/h7QpHxHOBYeDuusF/WXXz+FhEzOqyPpIkSdK0G5qsQER8G3hyk1HvK1lQRCwELgfOy8yd1eD3Ag9QC9YXA+8GLmwx/QXABQBHHXVUyaIlSZKknpo0RGfm6a3GRcT6iFiYmeuqkLyhRbl5wDeA92fmdXXzXlc93B4RnwH+W5t6XEwtaDMyMpKT1VuSJEmaLt1251gGnFc9Pg/4WmOBiBgGvgp8LjO/3DBuYfU/qPWnvqnL+kiSJEnTrtsQfRFwRkTcCZxRPSciRiLi01WZVwO/Dby+yaXsPh8RPwd+DhwK/EWX9ZEkSZKmXWTueT0jRkZGcsWKFf2uhiRJkvZiEXFDZo40G+cdCyVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRChmhJkiSpkCFakiRJKmSIliRJkgoZoiVJkqRCXYXoiDg4Iq6JiDur/we1KDcWESurv2V1w4+NiB9X038xIoa7qY8kSZK0O3S7J/o9wLWZeTxwbfW8mW2ZuaT6e3nd8I8AH6umfwg4v8v6SJIkSdOu2xB9NnBZ9fgy4BWdThgRAbwI+MpUppckSZL6pdsQvSAz1wFU/w9vUW52RKyIiOsiYjwoHwJszswd1fM1wBFd1keSJEmadkOTFYiIbwNPbjLqfQXLOSoz10bEccB3IuLnwJYm5bJNPS4ALgA46qijChYtSZIk9dakITozT281LiLWR8TCzFwXEQuBDS3msbb6vyoilgOnAP8MzI+IoWpv9JHA2jb1uBi4GGBkZKRl2JYkSZKmW7fdOZYB51WPzwO+1lggIg6KiFnV40OBFwC3ZGYC3wVe1W56SZIkaabpNkRfBJwREXcCZ1TPiYiRiPh0VeZEYEVE/JRaaL4oM2+pxr0beFdE3EWtj/QlXdZHkiRJmnZR2yG8ZxkZGckVK1b0uxqSJEnai0XEDZk50mycdyyUJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEKGaEmSJKmQIVqSJEkqZIiWJEmSChmiJUmSpEJdheiIODgiromIO6v/BzUp858iYmXd368j4hXVuM9GxD1145Z0Ux9JkiRpd+h2T/R7gGsz83jg2ur5BJn53cxckplLgBcBW4Fv1RX57+PjM3Nll/WRJEmSpl23Ifps4LLq8WXAKyYp/yrgqszc2uVyJUmSpL7pNkQvyMx1ANX/wycpfw5wRcOwv4yIn0XExyJiVqsJI+KCiFgRESs2btzYXa0lSZKkLkwaoiPi2xFxU5O/s0sWFBELgWcCV9cNfi/wdOA/AAcD7241fWZenJkjmTly2GGHlSxakiRJ6qmhyQpk5umtxkXE+ohYmJnrqpC8oc2sXg18NTMfq5v3uurh9oj4DPDfOqy3JEmS1DfddudYBpxXPT4P+FqbsufS0JWjCt5ERFDrT31Tl/WRJEmSpl23Ifoi4IyIuBM4o3pORIxExKfHC0XEMcBi4HsN038+In4O/Bw4FPiLLusjSZIkTbtJu3O0k5kPAqc1Gb4CeFPd83uBI5qUe1E3y5ckSZL6wTsWSpIkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYW6CtER8Z8j4uaI2BkRI23KnRkRt0fEXRHxnrrhx0bEjyPizoj4YkQMd1MfSZIkaXfodk/0TcArge+3KhARg8AngbOAk4BzI+KkavRHgI9l5vHAQ8D5XdZHkiRJmnZdhejMvDUzb5+k2HOBuzJzVWaOAl8Azo6IAF4EfKUqdxnwim7qI0mSJO0Ou6NP9BHA6rrna6phhwCbM3NHw3BJkiRpRhuarEBEfBt4cpNR78vMr3WwjGgyLNsMb1WPC4ALqqePRMRke8Cnw6HAL/uw3D2V66uM66uM66uM66uM66uM66uM66tz/V5XR7caMWmIzszTu1z4GmBx3fMjgbXUVsj8iBiq9kaPD29Vj4uBi7usS1ciYkVmtjyBUhO5vsq4vsq4vsq4vsq4vsq4vsq4vjo3k9fV7ujOcT1wfHUljmHgHGBZZibwXeBVVbnzgE72bEuSJEl91e0l7n4vItYAzwO+ERFXV8MXRcSVANVe5rcDVwO3Al/KzJurWbwbeFdE3EWtj/Ql3dRHkiRJ2h0m7c7RTmZ+Ffhqk+FrgZfWPb8SuLJJuVXUrt6xp+hrd5I9kOurjOurjOurjOurjOurjOurjOurczN2XUWtV4UkSZKkTnnbb0mSJKmQIbpDrW5drpqIWBwR342IW6tbwf/XaviHIuL+iFhZ/b10snntKyLi3oj4ebVeVlTDDo6IayLizur/Qf2uZ79FxAl17WdlRGyJiHfatiaKiEsjYkNE3FQ3rGl7ippPVNuzn0XEb/Sv5rtfi3X1PyPitmp9fDUi5lfDj4mIbXXt7P/0r+b90WJ9tfz8RcR7q7Z1e0S8pD+17p8W6+uLdevq3ohYWQ23fbXODzN++2V3jg5E7dbldwBnULtk3/XAuZl5S18rNoNExEJgYWb+JCIOBG6gdgfKVwOPZOb/6msFZ6CIuBcYycxf1g37G2BTZl5U/Vg7KDPf3a86zjTVZ/F+4FTgDdi2HhcRvw08AnwuM59RDWvanqrA8w5q566cCnw8M0/tV913txbr6sXAdzJzR0R8BKBaV8cA/zpebl/UYn19iCafv4g4CbiC2vlOi4BvA0/LzLHdWuk+ara+GsZ/FPhVZl5o+2qbH17PDN9+uSe6M01vXd7nOs0ombkuM39SPX6Y2pVYvANlubOBy6rHl1HbkOgJpwF3Z+Z9/a7ITJOZ3wc2NQxu1Z7OpvYFn5l5HbVr9i/cPTXtv2brKjO/VXcH3euo3btAtGxbrZwNfCEzt2fmPcBd7FkXEOhau/UVEUFt59IVu7VSM1ib/DDjt1+G6M60unW5mqh+WZ8C/Lga9PbqkMuldk+YIIFvRcQNUbsjJ8CCzFwHtQ0LcHjfajczncPELx/bVnut2pPbtPbeCFxV9/zYiLgxIr4XEb/Vr0rNQM0+f7at9n4LWJ+Zd9YNs31VGvLDjN9+GaI7U3SL8n1ZRBwA/DPwzszcAvwD8BRgCbAO+GgfqzfTvCAzfwM4C3hbdQhQLUTtZk0vB75cDbJtTZ3btBYi4n3ADuDz1aB1wFGZeQrwLuCfImJev+o3g7T6/Nm22juXiTsCbF+VJvmhZdEmw/rSxgzRnWl163LViYj9qH0APp+Z/w8gM9dn5lhm7gT+L/vYYb12quupk5kbqF1v/bnA+vHDUtX/Df2r4YxzFvCTzFwPtq0OtWpPbtOaiIjzgN8F/rC6qy5Vt4QHq8c3AHcDT+tfLWeGNp8/21YLETEEvBL44vgw21dNs/zAHrD9MkR3pumty/tcpxml6ud1CXBrZv5t3fD6fkq/B9zUOO2+KCL2r06gICL2B15Mbd0sA86rip0HfK0/NZyRJuzBsW11pFV7Wga8rjrL/TepneS0rh8VnCki4kxqd9F9eWZurRt+WHVCKxFxHHA8sKo/tZw52nz+lgHnRMSsiDiW2vr6991dvxnqdOC2zFwzPsD21To/sAdsv7q6Y+G+ojpbe/zW5YPApXW3LlfNC4DXAj8fv3QP8GfAuRGxhNqhlnuBP+pP9WacBcBXa9sOhoB/ysxvRsT1wJci4nzgF8B/7mMdZ4yImEvt6jj17edvbFtPiIgrgKXAoRGxBvggcBHN29OV1M5svwvYSu1KJ/uMFuvqvcAs4Jrqc3ldZr4Z+G3gwojYAYwBb87MTk+y2yu0WF9Lm33+MvPmiPgScAu1bjFv25euzAHN11dmXsKu53SA7Qta54cZv/3yEneSJElSIbtzSJIkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFTJES5IkSYUM0ZIkSVIhQ7QkSZJUyBAtSZIkFfr/ARalF8/MQU8ZAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "period_id = 24\n", + "year_portion = 0.1\n", + "\n", + "fig, ax = plt.subplots(figsize = (15, 8))\n", + "df_all_delta = df_all.loc[df_all.period_id == period_id].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + "check_stationarity(df_all_delta.forecast_error_relative)\n", + "\n", + "plot_acf(df_all_delta.forecast_error_relative, lags = 200, ax = ax)\n", + "plt.show()\n", + "plt.rcParams[\"figure.figsize\"] = (12, 8)\n", + "\n", + "plot_pacf(df_all_delta.forecast_error_relative, lags = 200)\n", + "plt.show()\n", + "plt.rcParams[\"figure.figsize\"] = (12, 8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Timeseries - 1 hour of day" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hour of Day = 10\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "period_id = 24\n", + "\n", + "fig1, ax1 = plt.subplots(figsize = (18, 10))\n", + "fig2, ax2 = plt.subplots(figsize = (18, 10))\n", + "\n", + "i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]}\n", + "\n", + "for i, hour_of_day in enumerate([10]):\n", + " print(f\"Hour of Day = {hour_of_day}\")\n", + " df_all_delta = df_all.loc[(df_all.period_id == period_id) & (df_all.date_time_future_hour == hour_of_day) & (df_all.date_time_future.dt.minute == 0)].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + "\n", + " plot_acf(df_all_delta.forecast_error_relative, lags = 28, ax = ax1)\n", + " ax1.set_xlabel('lag', fontsize = 18)\n", + " ax1.set_title(f'ACF of Forecast Error, lag = 24h, Hour of Day = {hour_of_day}', fontsize = 18) \n", + " ax1.set_ylim(0,1)\n", + " ax1.tick_params(axis='both', which='major', labelsize=18)\n", + "\n", + " plot_pacf(df_all_delta.forecast_error_relative, lags = 28, ax = ax2)\n", + " ax2.set_xlabel('lag', fontsize = 18)\n", + " ax2.set_title(f'PACF of Forecast Error, lag = 24h, Hour of Day = {hour_of_day}', fontsize = 18) \n", + " ax2.set_ylim(-0.1,1)\n", + " ax2.tick_params(axis='both', which='major', labelsize=18)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hour of Day = 4\n", + "ADF Statistic: -5.875132\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.432\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n", + "Hour of Day = 10\n", + "ADF Statistic: -7.680207\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.432\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n", + "Hour of Day = 16\n", + "ADF Statistic: -9.601104\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.432\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n", + "Hour of Day = 22\n", + "ADF Statistic: -7.968233\n", + "p-value: 0.000000\n", + "Critical Values:\n", + "\t1%: -3.432\n", + "\t5%: -2.862\n", + "\t10%: -2.567\n", + "\u001b[32mStationary\u001b[0m\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "period_id = 24\n", + "\n", + "fig1, ax1 = plt.subplots(2,2, figsize = (18, 10))\n", + "fig2, ax2 = plt.subplots(2,2, figsize = (18, 10))\n", + "\n", + "i_subplot = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]}\n", + "\n", + "for i, hour_of_day in enumerate([4, 10, 16, 22]):\n", + " print(f\"Hour of Day = {hour_of_day}\")\n", + " df_all_delta = df_all.loc[(df_all.period_id == period_id) & (df_all.date_time_future_hour == hour_of_day) & (df_all.date_time_future.dt.minute == 0)].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + " check_stationarity(df_all_delta.forecast_error_relative)\n", + "\n", + " plot_acf(df_all_delta.forecast_error_relative, lags = 28, ax = ax1[i_subplot[i][0]][i_subplot[i][1]])\n", + " ax1[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag')\n", + " ax1[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Hour of Day = {hour_of_day}') \n", + " ax1[i_subplot[i][0]][i_subplot[i][1]].set_ylim(0,1)\n", + "\n", + " plot_pacf(df_all_delta.forecast_error_relative, lags = 28, ax = ax2[i_subplot[i][0]][i_subplot[i][1]])\n", + " ax2[i_subplot[i][0]][i_subplot[i][1]].set_xlabel('lag')\n", + " ax2[i_subplot[i][0]][i_subplot[i][1]].set_title(f'Hour of Day = {hour_of_day}') \n", + " ax2[i_subplot[i][0]][i_subplot[i][1]].set_ylim(-0.1,1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SARIMA models considered:\n", + "- order=(1,0,0), seasonal_order=(0, 0, 0, 0) \n", + "- order=(1,0,1), seasonal_order=(0, 0, 0, 0) \n", + "- order=(7,0,1), seasonal_order=(0, 0, 0, 0)\n", + "- order=(7,0,7), seasonal_order=(0, 0, 0, 0)\n", + "- order=(1,0,0), seasonal_order=(1, 0, 1, 7)\n", + "- order=(6,0,2), seasonal_order=(1, 0, 1, 7) ## My preference\n", + "- order=(6,0,2), seasonal_order=(1, 0, 2, 7)\n", + "- order=(6,0,1), seasonal_order=(2, 0, 1, 7)\n", + "- order=(2,0,1), seasonal_order=(2, 0, 1, 7) " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sarimas = pd.DataFrame({\"order\":[(1,0,0), (1,0,1), (7,0,1), (7,0,7), (1,0,0), (6,0,2), (6,0,2), (6,0,1), (2,0,1)],\n", + " \"seasonal_order\": [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (1, 0, 1, 7), (1, 0, 1, 7), (1, 0, 2, 7), (2, 0, 1, 7), (2, 0, 1, 7)]})\n", + "sarimas = sarimas.reset_index().rename({\"index\": \"id\"}, axis = 1)\n", + "\n", + "columns = [\"sarima_id\", \"hour_of_day\", \"order\", \"seasonal_order\", \"ljung_val\", \"ljung_p\", \"jb_val\", \"jb_p\", \"hetro_val\", \"hetro_p\", \"skew\", \"kurtosis\", \"aic\", \"bic\", \"n_observations\", \"mse_pre\", \"mse_post\", \"mape\"]\n", + "sarima_tune = pd.DataFrame(columns = columns)\n", + "\n", + "period_id = 24\n", + "\n", + "hours_all = [0, 4, 8, 12, 16, 20]\n", + "\n", + "for hour_of_day in hours_all:\n", + " for index, row in sarimas.iterrows():\n", + " order = row[\"order\"]\n", + " seasonal_order = row[\"seasonal_order\"]\n", + " sarima_id = row[\"id\"]\n", + "\n", + " df_all_delta = df_all.loc[(df_all.period_id == period_id) & (df_all.date_time_future_hour == hour_of_day) & (df_all.date_time_future.dt.minute == 0)].sort_values(\"date_time_future\").reset_index(drop = True)\n", + "\n", + " # Model fit\n", + " model = ARIMA(df_all_delta.forecast_error, order = order, seasonal_order = seasonal_order)\n", + " model_fit = model.fit()\n", + " df_all_delta[\"predicted_forecast_error\"] = model_fit.fittedvalues\n", + " df_all_delta[\"new_forecast\"] = df_all_delta.forecast_demand + df_all_delta.predicted_forecast_error\n", + "\n", + " # Model Evaluation (MSE)\n", + " mse_pre = mean_squared_error(df_all_delta.total_demand,df_all_delta.forecast_demand)\n", + " mse_post = mean_squared_error(df_all_delta.total_demand,df_all_delta.new_forecast)\n", + " mape = mean_absolute_percentage_error(df_all_delta.total_demand,df_all_delta.new_forecast)\n", + "\n", + " # Model Evaluation (Crit values)\n", + " stat_tests = pd.read_html(model_fit.summary().tables[2].as_html(),header=None,index_col=0)[0]\n", + " ljung_val, ljung_p = stat_tests[1].iloc[0], stat_tests[1].iloc[1], \n", + " jb_val, jb_p = stat_tests[3].iloc[0], stat_tests[3].iloc[1], \n", + " hetro_val, hetro_p = stat_tests[1].iloc[2], stat_tests[1].iloc[3], \n", + " skew, kurtosis = stat_tests[3].iloc[2], stat_tests[3].iloc[3]\n", + "\n", + " # Model Evaluation (AIC, BIC)\n", + " stat_tests = pd.read_html(model_fit.summary().tables[0].as_html(),header=None,index_col=0)[0]\n", + " aic, bic = stat_tests[3].iloc[2], stat_tests[3].iloc[3]\n", + " n_observations = stat_tests[3].iloc[0]\n", + " sarima_tune = sarima_tune.append(pd.DataFrame([[sarima_id, hour_of_day, order, seasonal_order, ljung_val, ljung_p, jb_val, jb_p, hetro_val, hetro_p, skew, kurtosis, aic, bic, n_observations, mse_pre, mse_post, mape]], columns=columns), ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#sarima_tune = pd.read_csv(\"data/sarima_tune.csv\")\n", + "sarima_tune[\"mse_improvement\"] = round(100*(sarima_tune.mse_pre - sarima_tune.mse_post)/sarima_tune.mse_pre)\n", + "sarima_tune = pd.merge(sarima_tune, sarimas, on = [\"order\", \"seasonal_order\"], how = \"left\").sort_values(\"id\")\n", + "\n", + "plot = sarima_tune.groupby([\"order\", \"seasonal_order\", \"hour_of_day\"], as_index = False).mean()\n", + "sns.lineplot(data = plot, x = 'hour_of_day', y = 'mape', hue = 'id', palette = 'pastel', alpha = 1, linestyle = '--')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sarima_tune[\"mse_improvement\"] = round(100*(sarima_tune.mse_pre - sarima_tune.mse_post)/sarima_tune.mse_pre)\n", + "\n", + "plot = sarima_tune.groupby([\"order\", \"seasonal_order\", \"hour_of_day\"], as_index = False).mean()\n", + "sns.lineplot(data = plot, x = 'hour_of_day', y = 'mse_improvement', hue = 'id', palette = 'pastel', alpha = 1, linestyle = '--')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/W6 Model Comparison.ipynb b/src/W6 Model Comparison.ipynb new file mode 100644 index 000000000..3f53f49d4 --- /dev/null +++ b/src/W6 Model Comparison.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import warnings\n", + "\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "lm_results = pd.read_csv(\"data/results_LM.csv\")\n", + "lm_results[\"forecast_error\"] = lm_results.total_demand - lm_results.lm_prediction\n", + "\n", + "sarima_results = pd.read_csv(\"data/results_SARIMA.csv\")\n", + "sarima_results[\"forecast_error\"] = sarima_results.total_demand - sarima_results.sarima_prediction\n", + "\n", + "xgboost_results = pd.read_csv(\"data/results_XGBoost.csv\")\n", + "xgboost_results[\"forecast_error\"] = xgboost_results.total_demand - xgboost_results.xgb_prediction\n", + "\n", + "decisionT_results = pd.read_csv('data/results_DecisionTree.csv')\n", + "decisionT_results[\"forecast_error\"] = decisionT_results.total_demand - decisionT_results.model_prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "results_all = {\"Linear\": lm_results, \n", + " \"SARIMA\": sarima_results, \n", + " \"XGBoost\": xgboost_results,\n", + " \"Decision Tree\": decisionT_results}\n", + "\n", + "colors = ['#1f77b4', '#ff7f0e', 'g', '#7f7f7f']" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(1, 2, figsize = (16,7))\n", + "\n", + "plt.subplot(1,2,1)\n", + "for i, model in enumerate(results_all):\n", + " model_result = results_all[model]\n", + " sns.kdeplot(model_result.forecast_error, label = model, color = colors[i], alpha = 0.8)\n", + " \n", + "sns.kdeplot(lm_results.total_demand - lm_results.forecast_demand, label = \"AEMO\", color = 'r', ls = '--')\n", + "plt.xlim(-1200, 1200);\n", + "plt.ylim(0, 0.0025)\n", + "plt.xlabel('Forecast Error')\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.title(\"Distribution of Error\");\n", + "\n", + "plt.subplot(1,2,2)\n", + "for i, model in enumerate(results_all):\n", + " model_result = results_all[model]\n", + " sns.kdeplot(abs(model_result.forecast_error), label = model, color = colors[i], alpha = 0.8)\n", + " \n", + "sns.kdeplot(abs(lm_results.total_demand - lm_results.forecast_demand), label = \"AEMO\", color = 'r', ls = '--')\n", + "plt.xlim(0, 1000);\n", + "plt.ylim(0, 0.0046)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.xlabel('abs(Forecast Error)')\n", + "plt.title(\"Distribution of Absolute Error\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/XGBoost_CombinedVariables.py b/src/XGBoost_CombinedVariables.py new file mode 100644 index 000000000..28ac07f56 --- /dev/null +++ b/src/XGBoost_CombinedVariables.py @@ -0,0 +1,166 @@ +import pandas as pd +import numpy as np +from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit +from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error +import xgboost as xgb +import shap +import matplotlib.pyplot as plt + + +# Load and preprocess +df = pd.read_csv("C:/Users/nmutt/OneDrive/Documents/Masters/combined_data_new.csv") +df = df.loc[df.Temperature.notna()] +df = df[(df['period_id'] == 24)] +df['date_time_future'] = pd.to_datetime(df['date_time_future'], errors='coerce') +df = df.drop_duplicates(subset='date_time_future') +period_id = 24 + +df.date_time_current = pd.to_datetime(df.date_time_current, format = "%Y-%m-%d %H:%M:%S") +df.date_time_future = pd.to_datetime(df.date_time_future, format = "%Y-%m-%d %H:%M:%S") +df.date_time_current_rounded = pd.to_datetime(df.date_time_current_rounded, format = "%Y-%m-%d %H:%M:%S") + +df["forecast_interval"] = df.date_time_future - df.date_time_current_rounded +df["forecast_error"] = df.total_demand - df.forecast_demand +df["forecast_error_relative"] = df.forecast_error/df.total_demand + +df["date_time_future_hour"] = df.date_time_future.dt.hour + +# Calculate forecast error and lag features +df['forecast_error'] = df['forecast_demand'] - df['total_demand'] +df['24hrpreverrors'] = df['forecast_error'].shift(24) +df['48hrpreverrors'] = df['forecast_error'].shift(48) +df['7daypreverrors'] = df['forecast_error'].shift(24 * 7) +df['14daypreverrors'] = df['forecast_error'].shift(24 * 14) +# Time-based features +df["Hour"] = df.date_time_future.dt.hour +df["MonthNumb"] = df.date_time_future.dt.month +df["Day of week"] = df.date_time_future.dt.dayofweek # Monday = 0, Sunday = 6 + +df = df.dropna() + + + +# Encode Hour as cyclic features +df["hour_sin"] = np.sin(2 * np.pi * df["Hour"] / 24) +df["hour_cos"] = np.cos(2 * np.pi * df["Hour"] / 24) + +# Interaction features +df["hour_x_temp"] = df["Hour"] * df["Temperature"] +df["month_x_temp"] = df["MonthNumb"] * df["Temperature"] +df["hour_x_forecast"] = df["Hour"] * df["forecast_demand"] +df["temp_x_forecast"] = df["Temperature"] * df["forecast_demand"] +df["temp_x_hour_sin"] = df["Temperature"] * df["hour_sin"] +df["temp_x_hour_cos"] = df["Temperature"] * df["hour_cos"] +df["forecast_x_hour_sin"] = df["forecast_demand"] * df["hour_sin"] +df["forecast_x_hour_cos"] = df["forecast_demand"] * df["hour_cos"] +df["forecast_24_hour_cos"] = df["24hrpreverrors"] * df["hour_cos"] +df["forecast_24_hour_sin"] = df["24hrpreverrors"] * df["hour_sin"] + +features = [ + 'Temperature', 'Humidity', + 'Wind_speed', 'Rain', + 'hour_sin', 'hour_cos', + 'MonthNumb', 'Day of week', + 'forecast_demand', + '24hrpreverrors', + '48hrpreverrors', '7daypreverrors', '14daypreverrors', + 'hour_x_temp', 'month_x_temp', 'hour_x_forecast', 'temp_x_forecast', + 'temp_x_hour_sin', 'temp_x_hour_cos', + 'forecast_x_hour_sin', 'forecast_x_hour_cos', + 'forecast_24_hour_cos', 'forecast_24_hour_sin' + +] + +train_df = df[(df['date_time_future'] >= "2017-10-07 23:00:00") & (df['date_time_future'] <= "2020-03-05 23:00:00")] +test_df = df[(df['date_time_future'] > "2020-03-06 23:00:00") & (df['date_time_future'] <= "2021-03-17 23:00:00")] + +# Prepare train/test split sets +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, test_df['forecast_demand']) * 100 + +# Model creation, taken from fine tuning +xgb_model = xgb.XGBRegressor(objective='reg:squarederror', tree_method='hist', random_state=42) + +model = xgb.XGBRegressor( + objective='reg:squarederror', + learning_rate=0.1, + n_estimators=150, + max_depth=3, + subsample=0.8, + random_state=42 +) + +# TimeSeriesSplit to respect time order +tscv = TimeSeriesSplit(n_splits=3) + +# Parameter grid for randomized search +param_dist = { + 'n_estimators': [100, 150, 200, 250], + 'max_depth': [3, 4, 5], + 'learning_rate': [0.01, 0.03, 0.05, 0.1], + 'subsample': [0.7, 0.8, 1.0], + 'colsample_bytree': [0.7, 0.8, 1.0] +} + +# Create base model +xgb_model = xgb.XGBRegressor( + objective='reg:squarederror', + tree_method='hist', + random_state=42 +) + +# Randomized search +random_search = RandomizedSearchCV( + estimator=xgb_model, + param_distributions=param_dist, + n_iter=2000, + scoring='neg_mean_absolute_percentage_error', + cv=tscv, + verbose=1, + n_jobs=-1, + random_state=42 +) + +# Run the search +random_search.fit(X_train, y_train) + +# Use the best model +model = random_search.best_estimator_ + +# Optional: Print best parameters +print("Best Parameters:", random_search.best_params_) + +#model.fit(X_train, y_train) + + +# Predict and evaluate +y_pred = model.predict(X_test) +model_mse = mean_squared_error(y_test, y_pred) +model_mape = mean_absolute_percentage_error(y_test, y_pred) * 100 + +# Results +print(f"Original Forecast MSE: {original_mse:.2f}") +print(f"Original Forecast MAPE: {original_mape:.3f}%") +print(f"XGBoost Tuned Model MSE: {model_mse:.2f}") +print(f"XGBoost Tuned Model MAPE: {model_mape:.3f}%") + +# Explain model predictions using SHAP +explainer = shap.Explainer(model, X_test) +shap_values = explainer(X_test) +shap_df = pd.DataFrame(shap_values.values, columns=X_test.columns) + +# Forecast demand skews the plot so hide it +filtered_shap_values = shap_df.drop(columns=["forecast_demand"]) +filtered_X_test = X_test.drop(columns=["forecast_demand"]) + +shap.summary_plot( + filtered_shap_values.values, + features=filtered_X_test, + feature_names=filtered_X_test.columns +) diff --git a/src/XGBoost_FineTuning.py b/src/XGBoost_FineTuning.py new file mode 100644 index 000000000..dc4912b30 --- /dev/null +++ b/src/XGBoost_FineTuning.py @@ -0,0 +1,123 @@ +import pandas as pd +import numpy as np +from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit +from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error +import xgboost as xgb +import shap +import matplotlib.pyplot as plt + +# Load and preprocess +df = pd.read_csv("C:/Users/nmutt/OneDrive/Documents/Masters/combined_data_new.csv") +df = df.loc[df.Temperature.notna()] +df = df[(df['period_id'] == 24)] +df['date_time_future'] = pd.to_datetime(df['date_time_future'], errors='coerce') +df = df.drop_duplicates(subset='date_time_future') +period_id = 24 + +df.date_time_current = pd.to_datetime(df.date_time_current, format = "%Y-%m-%d %H:%M:%S") +df.date_time_future = pd.to_datetime(df.date_time_future, format = "%Y-%m-%d %H:%M:%S") +df.date_time_current_rounded = pd.to_datetime(df.date_time_current_rounded, format = "%Y-%m-%d %H:%M:%S") + +df["forecast_interval"] = df.date_time_future - df.date_time_current_rounded +df["forecast_error"] = df.total_demand - df.forecast_demand +df["forecast_error_relative"] = df.forecast_error/df.total_demand + +df["date_time_future_hour"] = df.date_time_future.dt.hour + +# Calculate forecast error and lag features +df['forecast_error'] = df['forecast_demand'] - df['total_demand'] +df['24hrpreverrors'] = df['forecast_error'].shift(24) +df['48hrpreverrors'] = df['forecast_error'].shift(48) +df['7daypreverrors'] = df['forecast_error'].shift(24 * 7) +df['14daypreverrors'] = df['forecast_error'].shift(24 * 14) +# Time-based features +df["Hour"] = df.date_time_future.dt.hour +df["MonthNumb"] = df.date_time_future.dt.month +df["Day of week"] = df.date_time_future.dt.dayofweek # Monday = 0, Sunday = 6 + +df = df.dropna() + + + +# Encode Hour as cyclic features +df["hour_sin"] = np.sin(2 * np.pi * df["Hour"] / 24) +df["hour_cos"] = np.cos(2 * np.pi * df["Hour"] / 24) + +# Interaction features +df["hour_x_temp"] = df["Hour"] * df["Temperature"] +df["month_x_temp"] = df["MonthNumb"] * df["Temperature"] +df["hour_x_forecast"] = df["Hour"] * df["forecast_demand"] +df["temp_x_forecast"] = df["Temperature"] * df["forecast_demand"] +df["temp_x_hour_sin"] = df["Temperature"] * df["hour_sin"] +df["temp_x_hour_cos"] = df["Temperature"] * df["hour_cos"] +df["forecast_x_hour_sin"] = df["forecast_demand"] * df["hour_sin"] +df["forecast_x_hour_cos"] = df["forecast_demand"] * df["hour_cos"] + +features = [ + 'Temperature', 'Humidity', + 'Wind_speed', 'Rain', + 'hour_sin', 'hour_cos', + 'MonthNumb', 'Day of week', + 'forecast_demand', + '24hrpreverrors', + '48hrpreverrors', '7daypreverrors', '14daypreverrors', + #'hour_x_temp', 'month_x_temp', 'hour_x_forecast', 'temp_x_forecast', + 'temp_x_hour_sin', 'temp_x_hour_cos', + #'forecast_x_hour_sin', 'forecast_x_hour_cos' +] + +train_df = df[(df['date_time_future'] >= "2017-10-07 23:00:00") & (df['date_time_future'] <= "2020-03-05 23:00:00")] +test_df = df[(df['date_time_future'] > "2020-03-06 23:00:00") & (df['date_time_future'] <= "2021-03-17 23:00:00")] + +# Prepare train/test split sets +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, test_df['forecast_demand']) * 100 + +# Model creation, taken from fine tuning +xgb_model = xgb.XGBRegressor(objective='reg:squarederror', tree_method='hist', random_state=42) + +model = xgb.XGBRegressor( + objective='reg:squarederror', + learning_rate=0.1, + n_estimators=150, + max_depth=3, + subsample=0.8, + random_state=42 +) + +model.fit(X_train, y_train) + + +# Predict and evaluate +y_pred = model.predict(X_test) +model_mse = mean_squared_error(y_test, y_pred) +model_mape = mean_absolute_percentage_error(y_test, y_pred) * 100 + +# Results +print(f"Original Forecast MSE: {original_mse:.2f}") +print(f"Original Forecast MAPE: {original_mape:.3f}%") +print(f"XGBoost Tuned Model MSE: {model_mse:.2f}") +print(f"XGBoost Tuned Model MAPE: {model_mape:.3f}%") + + +# Explain model predictions using SHAP +explainer = shap.Explainer(model, X_test) +shap_values = explainer(X_test) +shap_df = pd.DataFrame(shap_values.values, columns=X_test.columns) + +# Forecast demand skews the plot so hide it +filtered_shap_values = shap_df.drop(columns=["forecast_demand"]) +filtered_X_test = X_test.drop(columns=["forecast_demand"]) + +shap.summary_plot( + filtered_shap_values.values, + features=filtered_X_test, + feature_names=filtered_X_test.columns +) + diff --git a/src/XGBoost_Forecast.py b/src/XGBoost_Forecast.py new file mode 100644 index 000000000..18ce845e4 --- /dev/null +++ b/src/XGBoost_Forecast.py @@ -0,0 +1,153 @@ +import pandas as pd +import numpy as np +from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit +from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error +import xgboost as xgb +import shap +import matplotlib.pyplot as plt + +# Load and preprocess +df = pd.read_csv("C:/Users/nmutt/OneDrive/Documents/Masters/combined_data_new.csv") +df = df.loc[df.Temperature.notna()] +df = df[(df['period_id'] == 24)] +df['date_time_future'] = pd.to_datetime(df['date_time_future'], errors='coerce') +df = df.drop_duplicates(subset='date_time_future') +period_id = 24 + +df.date_time_current = pd.to_datetime(df.date_time_current, format = "%Y-%m-%d %H:%M:%S") +df.date_time_future = pd.to_datetime(df.date_time_future, format = "%Y-%m-%d %H:%M:%S") +df.date_time_current_rounded = pd.to_datetime(df.date_time_current_rounded, format = "%Y-%m-%d %H:%M:%S") + +df["forecast_interval"] = df.date_time_future - df.date_time_current_rounded +df["forecast_error"] = df.total_demand - df.forecast_demand +df["forecast_error_relative"] = df.forecast_error/df.total_demand + +df["date_time_future_hour"] = df.date_time_future.dt.hour + +# Calculate forecast error and lag features +df['forecast_error'] = df['forecast_demand'] - df['total_demand'] +df['24hrpreverrors'] = df['forecast_error'].shift(24) +df['48hrpreverrors'] = df['forecast_error'].shift(48) +df['7daypreverrors'] = df['forecast_error'].shift(24 * 7) +df['14daypreverrors'] = df['forecast_error'].shift(24 * 14) +# Time-based features +df["Hour"] = df.date_time_future.dt.hour +df["MonthNumb"] = df.date_time_future.dt.month +df["Day of week"] = df.date_time_future.dt.dayofweek # Monday = 0, Sunday = 6 + +df = df.dropna() + + + +# Encode Hour as cyclic features +df["hour_sin"] = np.sin(2 * np.pi * df["Hour"] / 24) +df["hour_cos"] = np.cos(2 * np.pi * df["Hour"] / 24) + +# Interaction features +df["hour_x_temp"] = df["Hour"] * df["Temperature"] +df["month_x_temp"] = df["MonthNumb"] * df["Temperature"] +df["hour_x_forecast"] = df["Hour"] * df["forecast_demand"] +df["temp_x_forecast"] = df["Temperature"] * df["forecast_demand"] +df["temp_x_hour_sin"] = df["Temperature"] * df["hour_sin"] +df["temp_x_hour_cos"] = df["Temperature"] * df["hour_cos"] +df["forecast_x_hour_sin"] = df["forecast_demand"] * df["hour_sin"] +df["forecast_x_hour_cos"] = df["forecast_demand"] * df["hour_cos"] +df["forecast_24_hour_cos"] = df["24hrpreverrors"] * df["hour_cos"] +df["forecast_24_hour_sin"] = df["24hrpreverrors"] * df["hour_sin"] + +features = [ + 'Temperature', 'Humidity', + 'Wind_speed', 'Rain', + 'hour_sin', 'hour_cos', + 'MonthNumb', 'Day of week', + 'forecast_demand', + '24hrpreverrors', + '48hrpreverrors', '7daypreverrors', '14daypreverrors', + #'hour_x_temp', 'month_x_temp', 'hour_x_forecast', 'temp_x_forecast', + 'temp_x_hour_sin', 'temp_x_hour_cos', + #'forecast_24_hour_cos', 'forecast_24_hour_sin' + #'forecast_x_hour_sin', 'forecast_x_hour_cos' +] + +train_df = df[(df['date_time_future'] >= "2017-10-07 23:00:00") & (df['date_time_future'] <= "2020-03-05 23:00:00")] +test_df = df[(df['date_time_future'] > "2020-03-06 23:00:00") & (df['date_time_future'] <= "2021-03-17 23:00:00")] + +# Prepare train/test split sets +X_train = train_df[features] +y_train = train_df['total_demand'] +X_test = test_df[features] +y_test = test_df['total_demand'] + +# Baseline metrics from forecast and total demand +original_mse = mean_squared_error(y_test, test_df['forecast_demand']) +original_mape = mean_absolute_percentage_error(y_test, test_df['forecast_demand']) * 100 + +# Model creation, taken from fine tuning +xgb_model = xgb.XGBRegressor(objective='reg:squarederror', tree_method='hist', random_state=42) + +model = xgb.XGBRegressor( + objective='reg:squarederror', + learning_rate=0.1, + n_estimators=150, + max_depth=3, + subsample=0.8, + random_state=42 +) + +model.fit(X_train, y_train) + + +# Predict and evaluate +y_pred = model.predict(X_test) +model_mse = mean_squared_error(y_test, y_pred) +model_mape = mean_absolute_percentage_error(y_test, y_pred) * 100 + +# Results +print(f"Original Forecast MSE: {original_mse:.2f}") +print(f"Original Forecast MAPE: {original_mape:.3f}%") +print(f"XGBoost Tuned Model MSE: {model_mse:.2f}") +print(f"XGBoost Tuned Model MAPE: {model_mape:.3f}%") + +#Output results to CSV +output_df = test_df.copy() +output_df["xgb_prediction"] = y_pred + +export_cols = ["date_time_future", "total_demand", "forecast_demand", "xgb_prediction"] +output_df[export_cols].to_csv("finalresultsxgboost.csv", index=False) + + +# Calculate absolute percentage error per row +output_df["abs_pct_error"] = np.abs((output_df["total_demand"] - output_df["xgb_prediction"]) / output_df["total_demand"]) * 100 + +# Extract hour from datetime +output_df["Hour"] = pd.to_datetime(output_df["date_time_future"]).dt.hour + +# Group by hour and calculate mean error +hourly_error = output_df.groupby("Hour")["abs_pct_error"].mean().reset_index() + +# Plot +plt.figure(figsize=(10, 5)) +plt.plot(hourly_error["Hour"], hourly_error["abs_pct_error"], marker='o') +plt.title("MAPE by Hour of Day") +plt.xlabel("Hour of Day") +plt.ylabel("MAPE") +plt.grid(True) +plt.xticks(range(0, 24)) +plt.tight_layout() +plt.show() + + +# # Explain model predictions using SHAP +# explainer = shap.Explainer(model, X_test) +# shap_values = explainer(X_test) +# shap_df = pd.DataFrame(shap_values.values, columns=X_test.columns) + +# # Forecast demand skews the plot so hide it +# filtered_shap_values = shap_df.drop(columns=["forecast_demand"]) +# filtered_X_test = X_test.drop(columns=["forecast_demand"]) + +# shap.summary_plot( +# filtered_shap_values.values, +# features=filtered_X_test, +# feature_names=filtered_X_test.columns +# ) diff --git a/src/forecast vs. actual demand over time.ipynb b/src/forecast vs. actual demand over time.ipynb new file mode 100644 index 000000000..d30b0c203 --- /dev/null +++ b/src/forecast vs. actual demand over time.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-03-23T18:19:27.116001700Z", + "start_time": "2025-03-23T18:16:57.023666200Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "Forecast data: 10906019 rows\n", + "Demand data: 196513 rows\n", + "\n", + "Adding lead time based on PERIODID...\n", + "\n", + "Creating standardized keys for merging...\n", + "\n", + "Merging datasets using standardized keys...\n", + "Merged data: 10906019 rows\n", + "Dataset is very large (10906019 rows). Using a 10% sample for visualization...\n", + "Sample size: 1090602 rows\n", + "\n", + "Creating time series visualization...\n", + "Sample period has 1884 points. Filtering to get clearer visualization...\n", + "Selected lead times for visualization: 0.5h (shortest), 20.0h (medium), 39.0h (longest)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12976\\3132785859.py:102: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " sample_data['HOUR'] = sample_data['DATETIME_OBJ'].dt.floor('H')\n", + "C:\\Users\\waseem\\AppData\\Local\\Temp\\ipykernel_12976\\3132785859.py:102: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " sample_data['HOUR'] = sample_data['DATETIME_OBJ'].dt.floor('H')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Creating error distribution visualization...\n", + "\n", + "Creating forecast vs actual scatter plot...\n", + "\n", + "Visualization complete. Images saved.\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
", + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import seaborn as sns\n", + "from datetime import datetime, timedelta\n", + "\n", + "# File paths\n", + "forecast_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\forecastdemand_nsw.csv\" # Chnage path\n", + "demand_path = r\"C:\\Users\\waseem\\Desktop\\UNSW\\Graduation Project\\totaldemand_nsw.csv\" #Change Path\n", + "\n", + "# Create lead time mapping from PERIODID\n", + "periodid_to_leadtime = {\n", + " 71: 35.5, 70: 35, 69: 34.5, 68: 34, 67: 33.5, 66: 33, 65: 32.5, 64: 32,\n", + " 63: 31.5, 62: 31, 61: 30.5, 60: 30, 59: 29.5, 58: 29, 57: 28.5, 56: 28,\n", + " 55: 27.5, 54: 27, 53: 26.5, 52: 26, 51: 25.5, 50: 25, 49: 24.5, 48: 24,\n", + " 47: 23.5, 46: 23, 45: 22.5, 44: 22, 43: 21.5, 42: 21, 41: 20.5, 40: 20,\n", + " 39: 19.5, 38: 19, 37: 18.5, 36: 18, 35: 17.5, 34: 17, 33: 16.5, 32: 16,\n", + " 31: 15.5, 30: 15, 29: 14.5, 28: 14, 27: 13.5, 26: 13, 25: 12.5, 24: 12,\n", + " 23: 11.5, 22: 11, 21: 10.5, 20: 10, 19: 9.5, 18: 9, 17: 8.5, 16: 8,\n", + " 15: 7.5, 14: 7, 13: 6.5, 12: 6, 11: 5.5, 10: 5, 9: 4.5, 8: 4, 7: 3.5,\n", + " 6: 3, 5: 2.5, 4: 2, 3: 1.5, 2: 1, 1: 0.5, 72: 36, 73: 36.5, 74: 37,\n", + " 75: 37.5, 76: 38, 77: 38.5, 78: 39, 79: 39.5\n", + "}\n", + "\n", + "# Load data\n", + "\n", + "forecast_df = pd.read_csv(forecast_path)\n", + "demand_df = pd.read_csv(demand_path)\n", + "\n", + "print(f\"Forecast data: {len(forecast_df)} rows\")\n", + "print(f\"Demand data: {len(demand_df)} rows\")\n", + "\n", + "# Add lead time based on PERIODID\n", + "print(\"\\nAdding lead time based on PERIODID...\")\n", + "forecast_df['LEAD_TIME_HOURS'] = forecast_df['PERIODID'].map(periodid_to_leadtime)\n", + "\n", + "# Create standardized keys for merging\n", + "print(\"\\nCreating standardized keys for merging...\")\n", + "\n", + "def create_matching_key(date_str, is_forecast=True):\n", + " \"\"\"Create a standardized key from different date formats\"\"\"\n", + " try:\n", + " if is_forecast:\n", + " dt = datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S')\n", + " else:\n", + " dt = datetime.strptime(date_str, '%d/%m/%Y %H:%M')\n", + " # Create a standardized key format\n", + " return f\"{dt.year}-{dt.month:02d}-{dt.day:02d} {dt.hour:02d}:{dt.minute:02d}\"\n", + " except Exception as e:\n", + " return None\n", + "\n", + "# Apply the function to create matching keys\n", + "forecast_df['MATCH_KEY'] = forecast_df['DATETIME'].apply(lambda x: create_matching_key(x, True))\n", + "demand_df['MATCH_KEY'] = demand_df['DATETIME'].apply(lambda x: create_matching_key(x, False))\n", + "\n", + "# Create datetime objects for visualization\n", + "forecast_df['DATETIME_OBJ'] = pd.to_datetime(forecast_df['DATETIME'])\n", + "demand_df['DATETIME_OBJ'] = pd.to_datetime(demand_df['DATETIME'], format='%d/%m/%Y %H:%M')\n", + "\n", + "# Merge the datasets using the matching keys\n", + "print(\"\\nMerging datasets using standardized keys...\")\n", + "merged_df = pd.merge(\n", + " forecast_df,\n", + " demand_df[['MATCH_KEY', 'TOTALDEMAND', 'REGIONID', 'DATETIME_OBJ']],\n", + " on=['MATCH_KEY', 'REGIONID'],\n", + " how='inner'\n", + ")\n", + "\n", + "print(f\"Merged data: {len(merged_df)} rows\")\n", + "\n", + "# If the dataset is very large, use a sample for visualization\n", + "if len(merged_df) > 1000000:\n", + " print(f\"Dataset is very large ({len(merged_df)} rows). Using a 10% sample for visualization...\")\n", + " merged_df = merged_df.sample(frac=0.1, random_state=42)\n", + " print(f\"Sample size: {len(merged_df)} rows\")\n", + "\n", + "# Calculate error metrics\n", + "merged_df['ERROR'] = merged_df['FORECASTDEMAND'] - merged_df['TOTALDEMAND']\n", + "merged_df['ABS_ERROR'] = abs(merged_df['ERROR'])\n", + "merged_df['PERC_ERROR'] = 100 * merged_df['ABS_ERROR'] / merged_df['TOTALDEMAND']\n", + "\n", + "# Use DATETIME_OBJ from the forecast dataframe for consistency\n", + "merged_df = merged_df.rename(columns={'DATETIME_OBJ_x': 'DATETIME_OBJ'})\n", + "\n", + "# Sort by datetime for time series visualization\n", + "merged_df = merged_df.sort_values('DATETIME_OBJ')\n", + "\n", + "# Visualization 1: Time series of forecast vs actual for a sample period\n", + "print(\"\\nCreating time series visualization...\")\n", + "\n", + "# Select a sample period\n", + "start_date = merged_df['DATETIME_OBJ'].min()\n", + "end_date = start_date + timedelta(days=7) # One week period\n", + "sample_data = merged_df[(merged_df['DATETIME_OBJ'] >= start_date) &\n", + " (merged_df['DATETIME_OBJ'] <= end_date)]\n", + "\n", + "# visualization\n", + "if len(sample_data) > 500:\n", + " print(f\"Sample period has {len(sample_data)} points. Filtering to get clearer visualization...\")\n", + " # Group by hour and lead time for visualization\n", + " sample_data['HOUR'] = sample_data['DATETIME_OBJ'].dt.floor('H')\n", + " grouped = sample_data.groupby(['HOUR', 'LEAD_TIME_HOURS']).agg({\n", + " 'FORECASTDEMAND': 'mean',\n", + " 'TOTALDEMAND': 'mean',\n", + " 'DATETIME_OBJ': 'first'\n", + " }).reset_index()\n", + " sample_data = grouped\n", + "\n", + "# select only a few lead times\n", + "lead_times = sorted(sample_data['LEAD_TIME_HOURS'].unique())\n", + "if len(lead_times) > 0:\n", + " # Select shortest, medium, and longest lead times\n", + " shortest_lead = lead_times[0]\n", + " medium_lead = lead_times[len(lead_times)//2] if len(lead_times) > 2 else None\n", + " longest_lead = lead_times[-1] if len(lead_times) > 1 else None\n", + "\n", + " print(f\"Selected lead times for visualization: {shortest_lead}h (shortest), \"\n", + " f\"{medium_lead}h (medium), {longest_lead}h (longest)\")\n", + "\n", + " # Filter data for these lead times\n", + " shortest_lead_data = sample_data[sample_data['LEAD_TIME_HOURS'] == shortest_lead]\n", + " medium_lead_data = sample_data[sample_data['LEAD_TIME_HOURS'] == medium_lead] if medium_lead else None\n", + " longest_lead_data = sample_data[sample_data['LEAD_TIME_HOURS'] == longest_lead] if longest_lead else None\n", + "\n", + " # Combine actual demand data for a single series\n", + " actual_demand = shortest_lead_data[['DATETIME_OBJ', 'TOTALDEMAND']].copy()\n", + " actual_demand = actual_demand.sort_values('DATETIME_OBJ').drop_duplicates('DATETIME_OBJ')\n", + "\n", + " # Create visualization\n", + " plt.figure(figsize=(14, 8))\n", + "\n", + " # Plot actual demand\n", + " plt.plot(actual_demand['DATETIME_OBJ'], actual_demand['TOTALDEMAND'], 'k-',\n", + " label='Actual Demand', linewidth=2)\n", + "\n", + " # Plot forecasts\n", + " plt.plot(shortest_lead_data['DATETIME_OBJ'], shortest_lead_data['FORECASTDEMAND'], 'b-',\n", + " label=f'Forecast ({shortest_lead}h lead time)', alpha=0.7)\n", + "\n", + " if medium_lead is not None:\n", + " plt.plot(medium_lead_data['DATETIME_OBJ'], medium_lead_data['FORECASTDEMAND'], 'g-',\n", + " label=f'Forecast ({medium_lead}h lead time)', alpha=0.7)\n", + "\n", + " if longest_lead is not None:\n", + " plt.plot(longest_lead_data['DATETIME_OBJ'], longest_lead_data['FORECASTDEMAND'], 'r-',\n", + " label=f'Forecast ({longest_lead}h lead time)', alpha=0.7)\n", + "\n", + " plt.title('Electricity Demand: Forecast vs. Actual Over Time')\n", + " plt.xlabel('Date')\n", + " plt.ylabel('Demand (MW)')\n", + " plt.legend()\n", + " plt.tight_layout()\n", + " plt.grid(True, alpha=0.3)\n", + "\n", + " # Format x-axis\n", + " plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))\n", + " plt.gca().xaxis.set_major_locator(mdates.DayLocator())\n", + " plt.xticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('forecast_vs_actual_time_series.png')\n", + "\n", + " # Visualization 2: Error distribution by lead time\n", + " print(\"\\nPLotting error distribution visualization...\")\n", + " plt.figure(figsize=(14, 6))\n", + "\n", + " # Create lead time bins for better visualization\n", + " bins = [i/2 for i in range(81)] # 0, 0.5, 1, 1.5, ..., 40\n", + " labels = [f'{i/2}-{(i+1)/2}h' for i in range(80)] # '0-0.5h', '0.5-1h', '1-1.5h', etc.\n", + "\n", + " merged_df['LEAD_TIME_BIN'] = pd.cut(merged_df['LEAD_TIME_HOURS'], bins=bins, labels=labels)\n", + "\n", + " # Sample data for visualization because the dataset is very large\n", + " boxplot_data = merged_df\n", + " if len(boxplot_data) > 100000:\n", + " boxplot_data = merged_df.sample(100000, random_state=42)\n", + "\n", + " # Box plot of percentage error by lead time bin\n", + " # Used a subset of bins for clarity\n", + " selected_bins = [labels[i] for i in range(0, len(labels), 4)] # Take every 4th bin\n", + " selected_data = boxplot_data[boxplot_data['LEAD_TIME_BIN'].isin(selected_bins)]\n", + "\n", + " sns.boxplot(x='LEAD_TIME_BIN', y='PERC_ERROR', data=selected_data)\n", + " plt.title('Forecast Error Distribution by Lead Time')\n", + " plt.xlabel('Forecast Lead Time')\n", + " plt.ylabel('Percentage Error (%)')\n", + " plt.xticks(rotation=45)\n", + " plt.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.savefig('forecast_error_distribution.png')\n", + "\n", + " # Visualization 3: Scatter plot of forecast vs actual\n", + " print(\"\\nCreating forecast vs actual scatter plot...\")\n", + " plt.figure(figsize=(10, 10))\n", + "\n", + " # Select a sample for clearer visualization\n", + " sample_size = min(5000, len(merged_df))\n", + " plot_sample = merged_df.sample(sample_size, random_state=42) if len(merged_df) > sample_size else merged_df\n", + "\n", + " # Color by lead time\n", + " scatter = plt.scatter(plot_sample['TOTALDEMAND'], plot_sample['FORECASTDEMAND'],\n", + " c=plot_sample['LEAD_TIME_HOURS'], cmap='viridis',\n", + " alpha=0.6, edgecolors='none')\n", + "\n", + " # Add color bar\n", + " cbar = plt.colorbar(scatter)\n", + " cbar.set_label('Lead Time (hours)')\n", + "\n", + " # Add perfect match line\n", + " max_val = max(plot_sample['TOTALDEMAND'].max(), plot_sample['FORECASTDEMAND'].max())\n", + " min_val = min(plot_sample['TOTALDEMAND'].min(), plot_sample['FORECASTDEMAND'].min())\n", + " plt.plot([min_val, max_val], [min_val, max_val], 'r--', label='Perfect Match')\n", + "\n", + " plt.title('Forecast vs. Actual Demand')\n", + " plt.xlabel('Actual Demand (MW)')\n", + " plt.ylabel('Forecasted Demand (MW)')\n", + " plt.grid(True, alpha=0.3)\n", + " plt.legend()\n", + " plt.tight_layout()\n", + " plt.savefig('forecast_vs_actual_scatter.png')\n", + "\n", + " print(\"\\nVisualization complete. Images saved.\")\n", + "else:\n", + " print(\"Error: No valid lead times found in the data for visualization.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}