MEDIUM-AND LONG-TERM ROLLING PREDICTION MODEL OF PV POWER FUSED WITH DT-BO-GU

Li Chao; Tu Teng; Peng Xunhui; Li Zhen; Chao Zibo; Liu Shuyu

doi:10.19912/j.0254-0096.tynxb.2024-0068

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 275-284. DOI: 10.19912/j.0254-0096.tynxb.2024-0068

MEDIUM-AND LONG-TERM ROLLING PREDICTION MODEL OF PV POWER FUSED WITH DT-BO-GU

Li Chao, Tu Teng, Peng Xunhui, Li Zhen, Chao Zibo, Liu Shuyu

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Abstract

In view of the low prediction accuracy and slow convergence speed of traditional photovoltaic power prediction models, the deep neural network model has problems such as gradient explosion or vanishing gradient. In this paper, a Bayesian-optimized GRU medium- and long-term photovoltaic power rolling prediction model based on decision tree extraction is proposed. Firstly, the parameters of the photovoltaic module model are extracted with the help of the decision tree model to reconstitute the feature data set, subsequently the Bayesian optimization algorithm is introduced to construct a new GRU neural network model, Finally, the photovoltaic power prediction is carried out on the photovoltaic parameters extracted from the tree model. Experimental results show that the hybrid model proposed in this paper has a high-precision prediction effect in special scenarios such as extreme areas, The fitting curve of the experimental simulation results is closer to the real value, and the error of the overall evaluation index of the model is low. Therefore, the fused DT-BO-GRU model proposed in this paper has higher prediction accuracy and provides the feasible method for predicting photovoltaic power generation in northern China.

Key words

photovoltaic modules / neural networks / Bayesian algorithms / decision tree models / parameter extraction / power prediction

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Li Chao, Tu Teng, Peng Xunhui, Li Zhen, Chao Zibo, Liu Shuyu. MEDIUM-AND LONG-TERM ROLLING PREDICTION MODEL OF PV POWER FUSED WITH DT-BO-GU[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 275-284 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0068

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