基于CBAM-DSC-UNet模型的时空风速预测算法

赵陆阳; 刘长良; 刘卫亮; 李洋; 王昕; 康佳垚

doi:10.19912/j.0254-0096.tynxb.2023-0914

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (10) : 497-505. DOI: 10.19912/j.0254-0096.tynxb.2023-0914

基于CBAM-DSC-UNet模型的时空风速预测算法

赵陆阳¹, 刘长良^1,2, 刘卫亮^1,2, 李洋¹, 王昕³, 康佳垚³

作者信息 +

SPATIO-TEMPORAL WIND SPEED PREDICTION ALGORITHM BASED ON CBAM-DSC-UNet MODEL

Zhao Luyang¹, Liu Changliang^1,2, Liu Weiliang^1,2, Li Yang¹, Wang Xin³, Kang Jiayao³

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文章历史 +

摘要

针对时空风速预测任务通常使用的卷积神经网络（CNN）和循环神经网络（RNN）联合建模方法中空间信息损失的问题,提出一种基于CBAM-DSC-UNet模型的时空风速预测算法,用于提升空间信息利用率与模型预测精度。该算法将时空风速预测问题视为视频预测问题,在提取时空相关性的同时保持空间信息,进而直接输出未来多步的空间风速矩阵。以美国怀俄明州某风电场实际数据为算例进行实验,结果表明,相比其他对比算法,基于CBAM-DSC-UNet模型的时空风速预测算法的平均绝对误差下降8.4%~15.9%,精度有较大提升。

Abstract

In response to the problem of spatial information loss in the joint modeling methods of convolutional neural networks （CNN） and recurrent neural networks （RNN） commonly used for spatial-temporal wind speed prediction tasks, we propose a spatial-temporal wind speed prediction algorithm based on the CBAM-DSC-UNet model. This algorithm aims to enhance the utilization of spatial information and improve the accuracy of model predictions. We treat the spatial-temporal wind speed prediction problem as a video prediction problem in order to preserve spatial information while extracting spatial-temporal correlations, thereby directly outputting the spatial wind speed matrix for multiple future steps. We conducted a calculating using actual data from a wind farm in Wyoming, USA as a case study. The results show that to other algorithms, the average absolute error of the spatial-temporal wind speed prediction algorithm based on the CBAM-DSC-UNet model reduces by 8.4% to 15.9%, demonstrating a significant improvement in prediction accuracy.

导出引用

赵陆阳, 刘长良, 刘卫亮, 李洋, 王昕, 康佳垚. 基于CBAM-DSC-UNet模型的时空风速预测算法[J]. 太阳能学报. 2024, 45(10): 497-505 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0914

Zhao Luyang, Liu Changliang, Liu Weiliang, Li Yang, Wang Xin, Kang Jiayao. SPATIO-TEMPORAL WIND SPEED PREDICTION ALGORITHM BASED ON CBAM-DSC-UNet MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 497-505 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0914

中图分类号： TM614

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