Solar Energy Forecasting
Zusammenfassung
- Ziel
- Forecast solar energy production with uncertainty estimates for grid management
- Daten
- Historical solar production data (hourly resolution) with weather covariates (temperature, cloud cover, irradiance). Time-based train/test split (70/30) respecting temporal order. Data spans 2 years with seasonal coverage.
- Modell(e)
- ARIMA, LSTM, Quantile Regression
- Kernmetriken
- MAPE: 8.5%, Calibrated prediction intervals
- Werkzeuge
- Python, PyTorch, statsmodels, scikit-learn, pandas
Problem
Forecasting solar energy production accurately to support grid management and energy trading decisions, accounting for weather variability and seasonal patterns.
Kontext
Solar energy production is highly dependent on weather conditions, creating significant uncertainty in energy supply. Accurate forecasts are essential for grid operators to balance supply and demand, and for energy traders to make informed decisions. The challenge lies in capturing both short-term weather effects and longer-term seasonal trends while providing uncertainty estimates.
Modellierungsansatz
I explored multiple time series approaches including ARIMA, LSTM networks, and ensemble methods combining weather forecasts with historical production data. The key was recognizing that solar production has both deterministic components (time of day, season) and stochastic components (weather). I used feature engineering to extract temporal patterns and incorporated external weather data as covariates. For uncertainty quantification, I implemented prediction intervals using quantile regression rather than just point forecasts.
Technisches Highlight
The most important technical decision was choosing how to handle uncertainty. Instead of only providing point forecasts, I built a quantile regression model that outputs prediction intervals at multiple confidence levels. This allows decision-makers to understand not just what will likely happen, but the range of possible outcomes. The model architecture separates deterministic trends (learned through seasonal decomposition) from weather-driven variability (modeled through the quantile regression framework).
Ergebnis
The final model achieved a mean absolute percentage error (MAPE) of 8.5% for 24-hour ahead forecasts, with well-calibrated prediction intervals. The uncertainty estimates proved valuable for risk-aware decision making, allowing operators to plan for worst-case scenarios. The separation of deterministic and stochastic components also made the model more interpretable and easier to debug when forecasts deviated from actual production.
Zuverlässigkeit
Daten
Historical solar production data (hourly resolution) with weather covariates (temperature, cloud cover, irradiance). Time-based train/test split (70/30) respecting temporal order. Data spans 2 years with seasonal coverage.
Baselines
Naive persistence (yesterday's value) achieves MAPE ~25%. Seasonal naive (same hour last week) achieves MAPE ~18%. Simple linear regression achieves MAPE ~15%. ARIMA baseline achieves MAPE ~12%.
Fehlermodi
Model underestimates uncertainty during extreme weather events (storms, sudden cloud cover). Performance degrades during transition periods (sunrise/sunset) due to rapid changes. Assumes weather forecasts are available and accurate. Struggles with rare events (e.g., solar eclipse).
Reproduzierbarkeit
Python 3.9+, PyTorch, statsmodels. Weather data from public APIs. Full preprocessing and model training pipeline on GitHub. Hyperparameters documented in config files.