POWER FORECAST OF PHOTOVOLTAIC GENERATION BASED ON XGBOOST COMBINED MODEL

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

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

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 236-242. DOI: 10.19912/j.0254-0096.tynxb.2020-0890

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

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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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.

Key words

XGBoost / partial least squares / combined model / photovoltaic power generation forecast

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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

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