Forecasting Wind Energy Production Using Machine Learning Techniques

Wind energy is an essential source of renewable that has gained popularity in recent years. Accurately forecasting wind production crucial for efficient management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) Random Forest (RFR) to forecast production. The proposed methodology involves data collection, preprocessing, feature selection, model training, optimization, evaluation. performance the models assessed mean squared error (MSE), root (RMSE), coefficient determination (R-squared) metrics. results indicate SVR-RFR outperforms individual models, achieving higher accuracy