Accurate wind speed forecasting through ensemble models: A real-world case study in Sfax,Tunisia

Abstract

The inherent variability of wind energy necessitates precise forecasting for its effective grid integration. This study investigates machine learning techniques for wind speed forecasting, applying Random Forest, AdaBoost, and Support Vector Regression (SVR), alongside a novel two-layer stacking ensemble model developed to leverage their combined strengths. The models were trained and validated using meteorological data from the National School of Electronics and Telecommunications of Sfax (ENET'Com) for October 2024 and May 2025. The ensemble model consistently outperformed the base models, achieving a Mean Absolute Error (MAE) of 0.255, a Root Mean Square Error (RMSE) of 0.334, and an R² of 0.801 for October. High accuracy was maintained in May, with an MAE of 0.314, an RMSE of 0.429, and an R² of 0.745. These findings validate the efficacy of advanced machine learning, particularly ensemble methods, for enhancing wind energy’s predictability and reliability.