Emin Demir

Short-term wind power forecasting with lag-based feature engineering: a turbine-level machine learning comparison

A turbine-level forecasting framework with a 10-minute temporal resolution, integrating unified multi-turbine data, lag- and rolling-based feature engineering, trigonometric time encoding, and machine learning models including Ridge, Lasso, K-Nearest Neighbors, Random Forest, Extra Trees, and XGBoost is developed, offering a reproducible solution for real-world short-term wind power forecasting.