Adaptive Short-Term Renewable Forecasting: Random Forest Framework

Accurate load and generation forecasts by Balancing Groups (BGs) and Transmission System Operators (TSOs) are crucial for maintaining grid stability and ensuring efficient energy market operations. This study introduces a machine-learning framework that combines Random Forest regression with bootstrap resampling to enable short-term energy forecasting. The model uses historical forecast data, meteorological variables (e.g., temperature, wind speed, humidity), and economic indicators (e.g., day-ahead energy prices) to achieve better performance.To capture complex temporal, environmental, and market-driven dynamics, the framework employs advanced feature engineering techniques, including lag features, rolling statistics, and cyclic transformations. Bootstrapping further enhances reliability by generating prediction intervals, enabling uncertainty quantification. This allows TSOs to compare forecasted BRP schedules against actual submissions, facilitating proactive energy market monitoring and improving grid management strategies.The framework's ability to perform well even with limited data makes it practical and adaptable for various energy systems, offering a dependable solution for improving forecast accuracy and supporting decision-making in grid operations.