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

Zhao Luyang; Liu Changliang; Liu Weiliang; Li Yang; Wang Xin; Kang Jiayao

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

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 497-505. DOI: 10.19912/j.0254-0096.tynxb.2023-0914

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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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.

Key words

wind forecasting / convolutional neural networks / spatial-temporal data / UNet / multi-wind turbine units

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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

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