RESEARCH ON FORECAST METHOD OF GRID CONNECTED POWER GENERATION OF SOLAR THERMAL POWER STATIONS BASED ON ISSA-LSTM MODEL

Lyu Da; Zhang Chao; Zhao Wentao

doi:10.19912/j.0254-0096.tynxb.2025-0676

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 359-368. DOI: 10.19912/j.0254-0096.tynxb.2025-0676

RESEARCH ON FORECAST METHOD OF GRID CONNECTED POWER GENERATION OF SOLAR THERMAL POWER STATIONS BASED ON ISSA-LSTM MODEL

Lyu Da^1,2, Zhang Chao³, Zhao Wentao³

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Abstract

To accurately forecast the grid connected power generation of solar thermal power stations, an improved sparrow search algorithm combined with long short-term memory networks （ISSA-LSTM model） is proposed. Firstly, based on the traditional LSTM model, the sparrow search algorithm is introduced to construct the SSA-LSTM forecast model, in order to break through the local optimal trap and enhance the global search capability. Secondly, using the elite opposition-based learning strategy to generate reverse solutions and obtain dynamic boundaries of elite individuals, the SSA-LSTM forecast model is improved to further enhance the algorithm's global search capability and search accuracy. Finally, the constructed model is trained and analyzed using real data from the solar thermal power station, and the forecast results of each model are compared with those of other models. After multiple training sessions, the results show that the ISSA-LSTM model has significantly higher forecast accuracy than other models, with a 14% improvement in forecast accuracy compared to the SSA-LSTM model. At the same time, the training results of the model are basically consistent with the actual values. Based on the ISSA-LSTM model, this article predicts the grid connected power generation of solar thermal power plants, providing a theoretical reference for comprehensively improving the accuracy of power generation pre and rational layout of solar thermal power stations.

Key words

solar thermal power stations / power generation forecast / learning algorithms / sparrow search algorithm / neural network

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Lyu Da, Zhang Chao, Zhao Wentao. RESEARCH ON FORECAST METHOD OF GRID CONNECTED POWER GENERATION OF SOLAR THERMAL POWER STATIONS BASED ON ISSA-LSTM MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 359-368 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0676

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