DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICTION MODEL BASED ON MULTIVARIATE METEOROLOGICAL INFORMATION AND IMPROVED COMBINED NEURAL NETWORK

Wu Weili; Mi Chan; Li Lei

doi:10.19912/j.0254-0096.tynxb.2024-1159

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 181-192. DOI: 10.19912/j.0254-0096.tynxb.2024-1159

DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICTION MODEL BASED ON MULTIVARIATE METEOROLOGICAL INFORMATION AND IMPROVED COMBINED NEURAL NETWORK

Wu Weili^1,2, Mi Chan^1,2, Li Lei^1,2

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Abstract

In order to improve the accuracy of PV power forecast, a short-term PV power forecast model is proposed, which takes into account the multivariate meteorological information from nearby power stations and improves the combined neural network. Firstly, considering the correlation between geographical factors and climatic conditions among adjacent distributed photovoltaic power stations, the grey correlation method is used to determine the main influencing factors of the power stations to be predicted, and the key features of multivariate meteorological information are constituted as the input sequence of the prediction model. Secondly, combining the advantages of the temporal convolutional network （TCN） for efficient extraction of input sequence information and bidirectional gated recurrent unit （BiGRU） for bidirectional data learning, a combined prediction model of TCN-BiGRU is built, and the improved Grey Wolf optimization algorithm （IGWO） is used to optimize the hyperparameters of BiGRU, so as to achieve high-precision prediction of photovoltaic power generation. Finally, the proposed model is verified by the measured data and compared with the similar methods. The results show that combined with multivariate meteorological information, the prediction model can effectively improve the prediction accuracy of power generation in different types of weather throughout the year, and compared with other prediction models, the proposed method can also show good prediction accuracy even when the climate conditions change drastically or randomly.

Key words

photovoltaic power prediction / neural network / variational mode decomposition / bidirectional gated recurrent unit / temporal convolutional network / improved grey wolf optimization algorithm

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Wu Weili, Mi Chan, Li Lei. DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICTION MODEL BASED ON MULTIVARIATE METEOROLOGICAL INFORMATION AND IMPROVED COMBINED NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 181-192 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1159

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