Predicting Short-Term Wind Power Generation at Musalpetti Wind Farm: Model Development and Analysis

In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R2 values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more influential at longer forecasting horizons. These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts. Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition, and future research will focus on real-time deployment and uncertainty quantification.