南方电网技术2023，Vol.17Issue(12)：80-89,10.DOI:10.13648/j.cnki.issn1674-0629.2023.12.010
基于气象耦合特征分析及改进XGBoost算法的用户分布式光伏短期出力预测模型
Short-Term Output Forecasting Model of User Side Distributed Photovoltaic Based on Meteorological Coupling Characteristics Analysis and Improved XGBoost Algorithm
摘要
Abstract
With the popularization of user side distributed photovoltaic power generation equipment,higher requirements are put forward for distributed photovoltaic output forecasting and regulation technology.To solve the problems of low generalization ability and high sample dependence of traditional photovoltaic output forecasting methods,a user distributed short-term photovoltaic forecast-ing model based on meteorological coupling characteristics and improved XGBoost algorithm is proposed.Mutual information and principal component analysis are used to select characteristics and reduce dimensions to obtain highly correlated and decoupled meteorological characteristic variables.The parallel integration of XGBoost forecasting model based on Bagging algorithm improves the model generalization ability.An evaluation index of combined forecasting accuracy based on mean absolute error(MAE)and mean arctangent absolute percentage error(MAAPE)is proposed.In the example analysis,the mean MAE of the model in this paper is 6.934kW,and the mean MAAPE is 16.73%.The relative error is less than 10%in more than half of cases.Compared with the traditional BP neural network or random forest forecasting model,forecasting accuracy is greatly improved and has good practical ap-plication ability.关键词
分布式光伏/XGBoost/光伏出力预测/气象特征Key words
distributed photovoltaic/XGBoost/photovoltaic output forecasting/meteorological characteristics分类
信息技术与安全科学引用本文复制引用
邓序之,陈小毅,刘淇,叶傲霜,许佳时,杨王旺,王玺,应文韬,邵佳佳,李芝娟..基于气象耦合特征分析及改进XGBoost算法的用户分布式光伏短期出力预测模型[J].南方电网技术,2023,17(12):80-89,10.基金项目
国家自然科学基金资助项目(51707074). Supported by the National Natural Science Foundation of China((51707074). （51707074）
