Solar and Wind Power Generation Forecasting
Module: Sustainable Tech | Difficulty: Premium
Solar Power Forecasting
Wind Power Curve
Comparison
| Horizon | Persistence | ML Baseline | Physics-ML | Ensemble |
|---|---|---|---|---|
| 1 hour | 85% | 92% | 95% | 96% |
| 6 hours | 75% | 85% | 89% | 91% |
| 24 hours | 65% | 78% | 83% | 86% |
| 48 hours | 55% | 70% | 76% | 80% |
Python Implementation
import numpy as np
class RenewableForecaster:
def solar_power(self, irradiance, temperature, rated_power, gamma=-0.004):
return irradiance * (rated_power / 1000) * (1 + gamma * (temperature - 25))
def wind_power(self, wind_speed, cut_in=3, rated_speed=12, cut_out=25, rated_power=2000):
power = np.zeros_like(wind_speed)
mask1 = (wind_speed >= cut_in) & (wind_speed < rated_speed)
mask2 = (wind_speed >= rated_speed) & (wind_speed < cut_out)
power[mask1] = rated_power * (wind_speed[mask1]**3 - cut_in**3) / (rated_speed**3 - cut_in**3)
power[mask2] = rated_power
return power
Research Insight: Probabilistic renewable forecasting using conformal prediction provides guaranteed coverage intervals.