RESEARCH ON PHOTOVOITAIC POWER PREDICTION METHOD BASED ON IMPROVED LSTM

Peng Shurong; Chen Huixia; Sun Wantong; Guo Lijuan; Li Bin

doi:10.19912/j.0254-0096.tynxb.2023-1122

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 296-302. DOI: 10.19912/j.0254-0096.tynxb.2023-1122

RESEARCH ON PHOTOVOITAIC POWER PREDICTION METHOD BASED ON IMPROVED LSTM

Peng Shurong, Chen Huixia, Sun Wantong, Guo Lijuan, Li Bin

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Abstract

In response to the significant randomness and uncertainty issues in photovoltaic power, this paper proposes an improved LSTM based photovoltaic power prediction method to improve the accuracy of photovoltaic power prediction. Firstly, meteorological features with strong correlation with photovoltaic output were analyzed, and the t-distribution nearest neighbor embedding dimensionality reduction technique was used to reduce the selected feature data to 2D to reduce data complexity. Then, the reduced dimensionality data is automatically clustered into three categories through density peak clustering to help train the LSTM prediction model. Compared with the traditional Recurrent neural network and Long short-term memory neural network models, the model proposed in this paper shows higher prediction accuracy in photovoltaic power prediction. MSE decreases by 49.00% and 31.77%, RMSE decreases by 28.59% and 17.41%, and MAE decreases by 62.35% and 53.52%. The research results indicate that the model has good applicability in photovoltaic power prediction, providing valuable reference for the optimization and scheduling of integrated energy systems.

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photovoltaic output / prediction / neural network / cluster analysis / t-distribution nearest neighbor embedding

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Peng Shurong, Chen Huixia, Sun Wantong, Guo Lijuan, Li Bin. RESEARCH ON PHOTOVOITAIC POWER PREDICTION METHOD BASED ON IMPROVED LSTM[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 296-302 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1122

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