SHORT-TERM PHOTOVOLTAIC POWER PREDICTION OF EXTREME WEATHER DRIVEN BY DATA-MODEL COOPERATION

Chen Pengyu; Mei Jianwei; Tuo Mingjian

doi:10.19912/j.0254-0096.tynxb.2025-1108

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (12) : 153-162. DOI: 10.19912/j.0254-0096.tynxb.2025-1108

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION OF EXTREME WEATHER DRIVEN BY DATA-MODEL COOPERATION

Chen Pengyu, Mei Jianwei, Tuo Mingjian

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Abstract

To address the problem that photovoltaic power prediction under extreme meteorological conditions is limited by sample scarcity and insufficient generalization ability, this paper designs a heterogeneous hybrid framework fusing the physically constrained time-series generative adversarial network （TimeGAN-PC） and the snow ablation-optimized long short-term memory neural network （SAO-LSTM）. Firstly, key influencing factors such as radiation index and air temperature are extracted based on the Pearson correlation coefficient method. Then, TimeGAN-PC is used to generate extreme meteorological data that satisfy the nighttime zero-power constraint, the photoelectric conversion efficiency boundary constraint, and the inclined-plane radiation conservation constraint. Finally, the snow ablation algorithm is adopted to optimize core hyperparameters of the LSTM such as the learning rate, so as to improve the prediction accuracy of the model. The experimental results under extreme meteorological conditions show that the proposed prediction model achieves better performance than traditional models.

Key words

photovoltaic power generation / prediction model / long short-term memory network / generative adversarial network

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Chen Pengyu, Mei Jianwei, Tuo Mingjian. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION OF EXTREME WEATHER DRIVEN BY DATA-MODEL COOPERATION[J]. Acta Energiae Solaris Sinica. 2026, 47(12): 153-162 https://doi.org/10.19912/j.0254-0096.tynxb.2025-1108

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