气象与环境学报2025，Vol.41Issue(3)：36-43,8.DOI:10.3969/j.issn.1673-503X.2025.03.005
XGBoost方法在风电功率预报中的应用
Application of the XGBoost method in wind power forecasting
摘要
Abstract
Using the wind power observation and training data from the 2014 Global Energy Forecasting Competi-tion(GEFCom2014),a study on the application of the XGBoost machine learning method in wind power forecas-ting was conducted.To assess the potential influence of variable distribution on machine learning models,wind power forecasting models were developed using zonal wind and meridional wind,as well as wind speed and wind direction as feature variables.Forecast experiments show that although the distributions of actual observed wind power and numerically forecasted wind speed generally follow the wind power curve,the high degree of dispersion is a major factor contributing to the uncertainty in wind power forecasts.While the correlation between zonal/me-ridional winds and wind power is not high,models trained with these features using XGBoost still achieve good forecasting performance comparable to those built directly with wind speed and wind direction.The forecast results of the models tend to underestimate wind power peaks and overestimate low power values,which may be attributed to errors in numerically forecasted wind speed.关键词
风电功率/机器学习/XGBoost/风能预报Key words
Wind power/Machine learning/XGBoost/Wind energy forecasting分类
能源科技引用本文复制引用
段云霞,李忠娴,李得勤..XGBoost方法在风电功率预报中的应用[J].气象与环境学报,2025,41(3):36-43,8.基金项目
国家自然科学基金(42275171)资助. （42275171）
