Goal:
Enhance day-ahead and intraday forecasts for wind, solar, and battery dispatch to reduce imbalance costs and curtailment.
Impact Keywords:
- Forecast Optimization
- Grid Stability
- Energy Efficiency
- Renewables Integration
Approach:
LunarTech Lab designed a forecasting and optimization suite that merges weather, asset, and market data.
- Data Fusion & Cleansing:
- Integrated NWP weather, SCADA, and market price data into spatiotemporal feature sets.
- Hybrid Forecast Models:
- Combined LSTM and gradient-boosting ensembles to predict generation and demand 1–24 hours ahead.
- Battery Dispatch Optimization:
- Applied reinforcement learning to optimize charge/discharge schedules based on forecast uncertainty.
- Visualization & Control:
- Built a grid-operations dashboard with scenario analysis and forecast-confidence visualization.
- Continuous Model Retraining:
- Automated retraining with live data, maintaining accuracy during seasonal changes.
Summary:
The system improved forecast accuracy by 18 %, reduced imbalance penalties by 25 %, and enabled smoother renewable integration into the grid.
