基于XGBoost联合模型的光伏发电功率预测

王献志; 曾四鸣; 周雪青; 陈天英; 郭少飞; 张卫明

doi:10.19912/j.0254-0096.tynxb.2020-0890

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (4) : 236-242. DOI: 10.19912/j.0254-0096.tynxb.2020-0890

电化学储能安全性与退役动力电池梯次利用关键技术专题

基于XGBoost联合模型的光伏发电功率预测

王献志, 曾四鸣, 周雪青, 陈天英, 郭少飞, 张卫明

作者信息 +

POWER FORECAST OF PHOTOVOLTAIC GENERATION BASED ON XGBOOST COMBINED MODEL

Wang Xianzhi, Zeng Sining, Zhou Xueqing, Chen Tianying, Guo Shaofei, Zhang Weipeng

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文章历史 +

摘要

提出一种考虑时间序列和多特征的光伏发电功率XGBoost联合预测模型。首先,基于偏最小二乘（PLS）提取影响光伏发电功率的多特征;然后,基于XGBoost算法分别建立发电功率的时间序列预测单模型和多特征预测单模型;最后,通过训练线性模型构建了光伏发电功率联合预测模型。使用某地区光伏电厂运行数据验证,结果证明,所提XGBoost联合模型预测精度更高,泛化能力更强,并且对噪声数据具有较强的抵抗能力。

Abstract

This paper proposes a XGBoost combined model considering time series and multi-features for forecasting photovoltaic power. First, the multiple features is extracted that affect photovoltaic power based on partial least squares （PLS）, then, the single PV power prediction models is established considering time series and multi-feature, respectively, based on XGBoost algorithm. Finally, the combined forecast model based on XGBoost is established through the training linear model parameters. The proposed XGBoost combined model is verified by the operation data of photovoltaic power plants in a certain area. As the results, the model has higher prediction accuracy, stronger generalization ability and stronger resistance to noise data.

导出引用

王献志, 曾四鸣, 周雪青, 陈天英, 郭少飞, 张卫明. 基于XGBoost联合模型的光伏发电功率预测[J]. 太阳能学报. 2022, 43(4): 236-242 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0890

Wang Xianzhi, Zeng Sining, Zhou Xueqing, Chen Tianying, Guo Shaofei, Zhang Weipeng. POWER FORECAST OF PHOTOVOLTAIC GENERATION BASED ON XGBOOST COMBINED MODEL[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 236-242 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0890

中图分类号： TK513.5

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