RESEARCH ON PHOTOVOLTAIC POWER PREDICTION MODEL CONSIDERING SEASONALITY AND TREND CHARACTERISTICS

Wang Dongfeng; Li Qingbo; Zhang Boyang; Huang Yu

doi:10.19912/j.0254-0096.tynxb.2023-1805

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 348-356. DOI: 10.19912/j.0254-0096.tynxb.2023-1805

RESEARCH ON PHOTOVOLTAIC POWER PREDICTION MODEL CONSIDERING SEASONALITY AND TREND CHARACTERISTICS

Wang Dongfeng, Li Qingbo, Zhang Boyang, Huang Yu

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Abstract

A photovoltaic power prediction method based on Neural-Prophet （NP） and deep neural networks is proposed to address the issue of insufficient consideration of seasonal and trend characteristics of photovoltaic power in photovoltaic power prediction. Firstly, the main factors affecting photovoltaic power are selected through the mutual information method. The PV power is then modeled using the NP model to obtain the seasonal and trend characteristics of PV power. These seasonal and trend characteristics, along with the main influencing factors, are used as inputs to the model. Secondly, an NP-ResNet-BiGRU photovoltaic power prediction model was established using an improved residual network （ResNet） and a bidirectional gated loop unit （BiGRU）, and the photovoltaic power prediction was completed. Experiments are conducted using data from the four seasons—spring, summer, autumn, and winter. Results show that, compared to other methods, the proposed method improves the MAE metric by at least 7.44% and the RMSE metric by at least 4.62%.

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photovoltaic power / forecasting / neural network / ResNet / Neural-Prophet

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Wang Dongfeng, Li Qingbo, Zhang Boyang, Huang Yu. RESEARCH ON PHOTOVOLTAIC POWER PREDICTION MODEL CONSIDERING SEASONALITY AND TREND CHARACTERISTICS[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 348-356 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1805

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