RESEARCH ON DATA DRIVEN WIND FARM POWER TRANSFER GENERATION MIGRATION FORECASTING METHOD

Yan Runzhen; Su Rui; Yan Liang

doi:10.19912/j.0254-0096.tynxb.2024-1583

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 567-574. DOI: 10.19912/j.0254-0096.tynxb.2024-1583

RESEARCH ON DATA DRIVEN WIND FARM POWER TRANSFER GENERATION MIGRATION FORECASTING METHOD

Yan Runzhen, Su Rui, Yan Liang

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Abstract

In response to the current scarcity of historical operating data for some wind farms in China, this paper proposes a data-driven wind farm power generation migration prediction model based on a convolutional neural network gated-recurrent unit（CNN-GRU） model. Firstly, based on the advantages of CNN and GRU models, a CNN-GRU combined model is constructed to eliminate overfitting problems and reduce training cycles. Secondly, the K-means clustering algorithm is used to cluster the historical operation data of wind farms, reflecting the features of large-scale scenes with a few typical scenes, reducing computational complexity while improving training accuracy. Thirdly, the migration conditions in each migration prediction scenario are further clarified, which not only avoids overfitting problems during the migration process, but also provides decision-making for parameter migration between the source domain and the target domain. Finally, compare the performance of the different migration prediction models is and compared the best migration prediction method is selected. Based on the training results, it can be concluded that the transfer prediction accuracy of the modified CNN-GRU model is significantly higher than that of the traditional LSTM model and the uncorrected CNN-GRU model. The K-means clustering algorithm can be used to optimize and identify reference wind farms, and to further improve the accuracy of transfer prediction results of the CNN-GRU combined model.

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data driven / wind farms / power generation / convolutional neural network / gate recurrent unit / transfer learning

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Yan Runzhen, Su Rui, Yan Liang. RESEARCH ON DATA DRIVEN WIND FARM POWER TRANSFER GENERATION MIGRATION FORECASTING METHOD[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 567-574 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1583

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