Very Short-Term Renewable Energy Power Prediction Using XGBoost Optimized by TPE Algorithm
Zhenchuan Ma, Haijun Chang, Zhongqing Sun, Fusuo Liu, Wei Li, Dongning Zhao, Chunmeng Chen
Abstract
Renewable energy power prediction is crucial to economic dispatch and reliable operation of power systems. This paper proposes a wind power forecasting approach based on the Extreme Gradient Boosting (XGBoost) algorithm. XGBoost is not only an effective feature selection method but also an accurate forecasting approach. In order to avoid excessive manual interventions for hyperparameter tuning, the Tree-Structured Parzen Estimator (TPE) model is presented to optimize the hyperparameters of XGBoost. This forecasting strategy has been tested in a real wind farm in Spain, compared with Persistence and Support Vector Regression (SVR). The results show that the XGBoost algorithm has higher accuracy and is a novel effective approach for very short-term wind power prediction.