APPLICABILITY EVALUATION OF PHOTOVOLTAIC POWER GENERATION PREDICTION MODEL IN YANGTZE RIVER DELTA REGION： A CASE STUDY OF JINHUA CITY, ZHEJIANG PROVINCE

Xu Chongbin; Chen Qian; Sun Xiaomin; Zhang Xiaobo; Li Guoshuai; Zuo Xin

doi:10.19912/j.0254-0096.tynxb.2024-0501

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 293-298. DOI: 10.19912/j.0254-0096.tynxb.2024-0501

APPLICABILITY EVALUATION OF PHOTOVOLTAIC POWER GENERATION PREDICTION MODEL IN YANGTZE RIVER DELTA REGION： A CASE STUDY OF JINHUA CITY, ZHEJIANG PROVINCE

Xu Chongbin^1,2, Chen Qian^1,2, Sun Xiaomin^1,2, Zhang Xiaobo³, Li Guoshuai², Zuo Xin^1,2

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Abstract

The photovoltaic power generation process is significantly affected by external environmental factors such as meteorological conditions and geographic location, leading to pronounced uncertainty. This article leverages the power generation data from six photovoltaic power stations in Jinhua City and employs various prediction models for comparative analysis, to analyze and demonstrate the supportive role neighby power stations in the predicton of photovoltaic power generation. The study indicates that： 1） Photovoltaic power stations located within the same region are affected by meteorological environmental factors and exhibit similar trends. 2） Time series forecasting based on deep learning obviates the need for manual feature extraction or an intricate understanding of the entire power generation process. Instead, employing a data-driven approach and model training can capture the changing trends of the power curves. Transformer-based models like Crossformer and PatchTST are more aptly suited for the task of predicting photovoltaic power output.

Key words

photovoltaic power generation prediction / deep learning / LSTM / Transformer

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Xu Chongbin, Chen Qian, Sun Xiaomin, Zhang Xiaobo, Li Guoshuai, Zuo Xin. APPLICABILITY EVALUATION OF PHOTOVOLTAIC POWER GENERATION PREDICTION MODEL IN YANGTZE RIVER DELTA REGION： A CASE STUDY OF JINHUA CITY, ZHEJIANG PROVINCE[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 293-298 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0501

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