Photovoltaic power prediction based on similar day clustering and GA-BP AdaBoost model

Qiang Gao; Feng Yuan; Mingyuan Xu; Nan Ma; Jun Fang; Jicheng Dai; Zailin Li; Fenghou Pan; Jiayu Pan

doi:10.1117/12.2692016

24 October 2023 Photovoltaic power prediction based on similar day clustering and GA-BP AdaBoost model

Qiang Gao, Feng Yuan, Mingyuan Xu, Nan Ma, Jun Fang, Jicheng Dai, Zailin Li, Fenghou Pan, Jiayu Pan

Author Affiliations +

Proceedings Volume 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023); 128040X (2023) https://doi.org/10.1117/12.2692016
Event: 2nd International Conference on Sustainable Technology and Management (ICSTM2023), 2023, Dongguan, China

Abstract

Due to the rising global energy demand, countries are actively seeking new alternative energy sources. Photovoltaic power generation has gained significant momentum in recent years due to its renewable nature, lack of pollution, versatile applications, unlimited scalability, and capacity. It has emerged as a crucial means of harnessing solar energy. However, the intermittent and unpredictable nature of photovoltaic power generation poses challenges to the reliable and stable operation of the power grid. Therefore, accurate prediction of photovoltaic power generation holds immense importance. Although the BP neural network is widely employed for power output prediction, it faces issues such as poor convergence and susceptibility to local minima. To address these concerns, this paper focuses on optimizing the BP neural network to achieve improved prediction outcomes.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Gao, Feng Yuan, Mingyuan Xu, Nan Ma, Jun Fang, Jicheng Dai, Zailin Li, Fenghou Pan, and Jiayu Pan "Photovoltaic power prediction based on similar day clustering and GA-BP AdaBoost model", Proc. SPIE 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023), 128040X (24 October 2023); https://doi.org/10.1117/12.2692016

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