Transformer-based short-term forecasting of renewable power and grid load using high-resolution weather data with explainable AI

Abstract

Accurate forecasting of electricity load, solar power, and wind power is critical to the reliability of modern power systems, particularly in regions such as California with high penetration of variable power sources. California’s power system is managed by the California Independent System Operator (CAISO), which operates a grid that receives a significant portion of its power from various renewable energy sources. A novel forecasting method based on the Transformer architecture is introduced to accurately forecast electricity load, solar power, and wind power. This study will utilize an interval data set that is collected every 10 min. A new forecasting methodology has been developed by combining CAISO’s real-time operational data with high-frequency meteorological datasets from the National Renewable Energy Laboratory (NREL) National Solar Radiation Database (NSRDB). Since the forecasting model was tested using historical data, we used a chronological data split. We also engineered all feature combinations using historical data. Three different transformers were developed specifically for each task: Load, Solar Power, and Wind Power. Each transformer includes positional encoding, multi-head self-attention and temporal characteristics. Testing demonstrated strong performance of the forecasting models, as evidenced by average R2 values of approximately 0.95 for Load, 0.80 for Solar, and 0.77 for Wind. The results indicate the inherent volatility of renewable energy generation and confirm that an attention-based architecture can be utilised to represent complex time-series relationships. The proposed solution offers a viable approach to developing a scalable and reliable short-term energy-forecasting platform for renewable-integrated power systems.