基于MODWT-BiLSTM-BiGRU的风电功率预测

郭利进; 刘文哲; 刘彦宾

doi:10.19912/j.0254-0096.tynxb.2024-2256

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (4) : 405-413. DOI: 10.19912/j.0254-0096.tynxb.2024-2256

基于MODWT-BiLSTM-BiGRU的风电功率预测

郭利进^1,2, 刘文哲^1,2, 刘彦宾^1,2

作者信息 +

WIND POWER PREDICTION BASED ON MODWT-BiLSTM-BiGRU

Guo Lijin^1,2, Liu Wenzhe^1,2, Liu Yanbin^1,2

Author information +

文章历史 +

摘要

为提高风电功率预测精度，提出一种基于最大重叠离散小波变换（MODWT）与双向循环神经网络相结合的风电功率预测模型。该模型利用MODWT将风电功率分解为低频分量和高频分量，通过双向长短期记忆网络（BiLSTM）和双向门控循环单元（BiGRU）相互补充，以有效预测短期和长期风电功率数据并达到最准确的预测结果。采用两个风电场的实际数据进行实验，结果证实所提方法相较于基线方法具有一致优越性，预测精度显著提高，误差指标均方根误差（RMSE）和平均绝对误差（MAE）分别降低约16.47%和15.10%，决定系数（R²）达到0.9756。

Abstract

To enhance the accuracy of wind power predictions, a novel model is proposed in this paper that integrates the maximum overlap discrete wavelet transform （MODWT） with a bidirectional recurrent neural network. The MODWT is utilized to decompose wind power into its low-frequency and high-frequency components. In conjunction, the bidirectional long short-term memory （BiLSTM） and bidirectional gated recurrent unit （BiGRU） work synergistically to effectively predict both short-term and long-term wind power data, leading to significantly accurate predictions. Experiments were conducted using the actual data of two wind farms. The results confirm that the proposed method has consistent superiority over the baseline method. A substantial improvement in prediction accuracy is obtained. The error metrics including root mean square error （RMSE） and mean absolute error （MAE） are reduced by approximately 16.47% and 15.10%, respectively, while the coefficient of determination （R²） achieved a value of 0.9756.

导出引用

郭利进, 刘文哲, 刘彦宾. 基于MODWT-BiLSTM-BiGRU的风电功率预测[J]. 太阳能学报. 2026, 47(4): 405-413 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2256

Guo Lijin, Liu Wenzhe, Liu Yanbin. WIND POWER PREDICTION BASED ON MODWT-BiLSTM-BiGRU[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 405-413 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2256

中图分类号： TM614

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