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Short-Term Load Forecasting Based on XGBoost Model

This repository contains the source materials and calculation results used in the study Short-Term Forecasting of Regional Electrical Load Based on XGBoost Model

validation result (01/01/2025 - 01/06/2025)

Month /
MAPE
XGBoost
br3_pred
XGBoost
br3_act
XGBoost
br3_act_LC
(y:6, md:6)
XGBoost
br2_act_LC
(y:10, md:5)
CatBoost
br3_pred
CatBoost
br2_act_LC
ARIMA
default
January 1.605% 1.576% 1.640% 1.636% 1.616% 1.827% 11.248%
February 1.233% 1.246% 1.196% 1.242% 1.154% 1.194% 11.446%
March 1.169% 1.181% 1.104% 1.122% 1.190% 1.221% 10.091%
April 1.689% 1.619% 1.570% 1.490% 1.710% 1.596% 11.567%
May 1.610% 1.609% 1.618% 1.548% 1.744% 1.770% 11.023%
total 1.464% 1.449% 1.429% 1.411% 1.488% 1.527% 11.062%
MAE, MW 39.912 39.346 38.992 38.487 40.341 41.396 277.585
Loss, RUR 15.961.596 17.237.366 16.409.258 16.726.061 16.557.290 17.005.792 138.535.705
Mean Loss, RUR/h 4404.41 4756.45 4527.94 4615.36 4568.79 4692.55 38.227
Median Loss, RUR/h 262.78 223.77 282.37 319.47 259.47 315.83 7271.8
τ, s/it 28.99 31.07 1.97 2.01 45.8 1.28 4.29

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Short-Term Forecasting of Regional Electrical Load Based on XGBoost Model

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