SHORT TERM PHOTOVOLTAIC POWER PREDICTION CONSIDER SIMILAR DAYS SELECTION AND PVFORMER MODEL

Li Lianbing; Dai Liangliang; Li Xinda; Yang Pengwei; Yang Shaobo; Gao Guoqiang

doi:10.19912/j.0254-0096.tynxb.2024-0552

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 341-351. DOI: 10.19912/j.0254-0096.tynxb.2024-0552

SHORT TERM PHOTOVOLTAIC POWER PREDICTION CONSIDER SIMILAR DAYS SELECTION AND PVFORMER MODEL

Li Lianbing¹, Dai Liangliang¹, Li Xinda¹, Yang Pengwei², Yang Shaobo³, Gao Guoqiang¹

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Abstract

In order to improve the accuracy of PV power prediction, this paper proposes a short-term PV power prediction model PVformer model based on SFLA similar day selection and improved Transformer model. This article calculates similarity factors based on grey correlation degree, optimizes comprehensive similarity factors based on SFLA, and realizes the selection of photovoltaic similarity days. The PVformer model is based on CNN networks to obtain features from photovoltaic data and reduce data dimensions. The BiGRU network is used to extract temporal features from photovoltaic data and embed data positions. The multi-head autocorrelation attention mechanism is used to find inter sequence relationships and break the bottleneck of information utilization. This article selects the historical day with the highest comprehensive similarity factor as the similarity day for the predicted day, selects features with high correlation as the model input, constructs historical feature vectors and future meteorological vectors to input into the PVformer model. The results of the control experiments show that the PVformer model improves the accuracy of day-ahead PV power prediction,E_MAPE,E_MAE, and E_MSE reach 1.526%, 0.274 MW, and 0.134 MW², respectively. Finally, the effectiveness and necessity of model improvement are demonstrated through ablation experiments, which have certain practical value.

Key words

photovoltaic power generation / similar day selection / power prediction / PVformer model / multi-head autocorrelated attention mechanism

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Li Lianbing, Dai Liangliang, Li Xinda, Yang Pengwei, Yang Shaobo, Gao Guoqiang. SHORT TERM PHOTOVOLTAIC POWER PREDICTION CONSIDER SIMILAR DAYS SELECTION AND PVFORMER MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 341-351 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0552

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