EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application

Wind power prediction is critical for the efficient utilization of wind energy and the stable operation of power grids. However, existing prediction methods struggle to handle the complex characteristics of meteorological data. To address this challenge, this paper proposes an entropy-driven high-dimensional feature gain XGBoost model. First, traditional meteorological data are transformed into information entropy from an informational perspective, enabling the capture of randomness and nonlinear relationships within the data. Next, an entropy-driven high-dimensional (EDHD) classification feature selection method is designed to identify the relationships between covariates and response variables in the information entropy. Subsequently, a feature-gain XGBoost model is constructed to dynamically determine the optimal splitting features and thresholds, achieving the entropy-driven high-dimensional feature gain XGBoost model (EDHD-FG-XGBoost). Finally, experiments using real wind farm data from the northeastern region of China demonstrate the feasibility and effectiveness of the proposed method.