Wind Power Generation Prediction Based on Bayesian Optimized XGBoost

Accurate prediction of wind power generation is crucial for improving power grid stability and energy utilization efficiency. Given the low accuracy of existing wind power prediction methods, this paper proposes a wind power prediction approach based on Bayesian-optimized XGBoost. This method uses the Bayesian optimization algorithm to automatically tune the hyperparameters of the XGBoost model to enhance prediction accuracy. The research results show that compared with the traditional XGBoost model, the Bayesian-optimized XGBoost model significantly improves prediction accuracy, enabling more accurate prediction of wind power generation, which can provide a reliable basis for power grid dispatching and energy management.