A Python library for wind resource assessment, wake modeling, turbine database management, MCDA-based site suitability analysis, and capacity factor computation.
- Weibull Analysis: MLE fitting, PDF, mean power density
- Wind Rose: Directional frequency and speed distributions
- Wind Shear: Power-law extrapolation to hub height
- Wake Modeling: Jensen/Park single-wake deficit, array efficiency with RSS superposition
- Capacity Factor: Hourly CF from Open-Meteo, NASA POWER, or ERA5 data
- Turbine Database: atlite YAML + OEDB REST API
- MCDA: Multi-criteria site suitability (entropy, PCA, manual weights)
- Economics: LCOE, NPV, IRR, sensitivity analysis
- Regional Analysis: Grid-based resource assessment with development zones
pip install windrexWith ERA5 support:
pip install windrex[era5]from windrex import fit_weibull, weibull_pdf, compute_wind_rose
import numpy as np
# Fit Weibull to measured speeds
speeds = np.random.weibull(2.0, 10000) * 7.0
k, A = fit_weibull(speeds)
print(f"Weibull k={k:.2f}, A={A:.2f}")
# Compute wind rose
directions = np.random.uniform(0, 360, len(speeds))
rose = compute_wind_rose(speeds, directions)MIT