Short-term Wind Power Prediction Method Based on Genetic Algorithm Optimized XGBoost Regression Model

Abstract In order to solve the problem of accuracy and rapidity short-term prediction wind power output, eXtreme Gradient Boosting (XGBoost) regression model is used in this paper predict output. For models commonly at present stage, such as Long Short Term Memory (LSTM), random forest ordinary XGBoost model, modelling time long, not enough. paper, a genetic algorithm (GA) introduced improve speed model. Firstly, learning rate optimized by using good searching ability flexibility algorithm. Then variable weight combination carried out. The objective function for mean square error that occurs between value predicted actually training set. GA responsible determining model’s final weight. historical output data plant verify based on get value, which then compared with results LSTM Example simulation analysis show can be more significantly solving