SHORT TERM PHOTOVOLTAIC POWER PREDICTION BASED ON GRO-SSA-LSTM

Wang Lingzhi; Li Chenyang; Liu Jing; Li Cheng

doi:10.19912/j.0254-0096.tynxb.2023-1597

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 401-409. DOI: 10.19912/j.0254-0096.tynxb.2023-1597

SHORT TERM PHOTOVOLTAIC POWER PREDICTION BASED ON GRO-SSA-LSTM

Wang Lingzhi¹, Li Chenyang¹, Liu Jing², Li Cheng¹

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Abstract

In order to improve the accuracy of photovoltaic power generation prediction and ensure the sustainable and stable operation of the power grid, this article combines long short term memory （LSTM） network with sparrow search algorithm （SSA） improved by gold rush optimizer （GRO） algorithm to achieve short-term photovoltaic power generation prediction. Firstly, the Pearson correlation coefficient is used to extract key factors that affect photovoltaic power; Secondly, the sparrow search algorithm is used to optimize the long and short term memory network, obtaining the optimal hyperparameters such as the number of hidden layer nodes, training times, and learning rate in the network. Then, introducing Tent chaotic mapping to optimize the initial distribution of sparrow population, making the initial position distribution of the population more uniform. Finally, to avoid the algorithm falling into local optima, GRO is introduced to optimize SSA, making the search range of sparrow population more extensive and the results more accurate. The experimental results indicate that compared with LSTM and SSA-LSTM, GRO-SSA-LSTM has higher prediction accuracy in short-term photovoltaic power generation prediction and has crucial practical significance.

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photovoltaic power / prediction model / long short-term memory network / sparrow search algorithm / gold rushing optimization algorithm

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Wang Lingzhi, Li Chenyang, Liu Jing, Li Cheng. SHORT TERM PHOTOVOLTAIC POWER PREDICTION BASED ON GRO-SSA-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 401-409 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1597

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