基于数据驱动的风电场发电功率迁移预测方法研究

闫润珍; 苏蕊; 延亮

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

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (1) : 567-574. DOI: 10.19912/j.0254-0096.tynxb.2024-1583

基于数据驱动的风电场发电功率迁移预测方法研究

闫润珍, 苏蕊, 延亮

作者信息 +

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

Yan Runzhen, Su Rui, Yan Liang

Author information +

文章历史 +

摘要

针对现阶段中国部分风电场历史运行数据较为稀缺的情况,基于数据驱动方式提出一种卷积神经网络（CNN）-门控循环单元（GRU）的风电场发电功率迁移预测模型。首先,基于CNN与GRU模型优势,构建CNN-GRU组合模型,以消除过拟合问题并减少训练周期。其次,利用K-均值特征聚类算法对风电场历史运行数据进行聚类,以少数典型场景反映大规模场景中特征,减少计算复杂度的同时提升训练精度。再次,进一步明确各迁移预测场景中的迁移条件,既能避免迁移过程中过拟合问题又可为源域与目标域间参数迁移提供决策依据。最后,对比不同迁移预测模型的性能,选择最佳迁移预测方式。一系列训练结果表明：经过修正后的CNN-GRU模型迁移预测结果精确度明显高于传统LSTM模型以及未修正的CNN-GRU模型预测结果;通过K-均值特征聚类算法可对参考风电场进行优化识别,并进一步提升CNN-GRU组合模型迁移预测结果精度。

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.

导出引用

闫润珍, 苏蕊, 延亮. 基于数据驱动的风电场发电功率迁移预测方法研究[J]. 太阳能学报. 2026, 47(1): 567-574 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1583

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

中图分类号： TM614

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