ResNet-UNet SHORT-TERM WIND POWER PREDICTION WITH INCORPORATION OF MULTI-HEAD ATTENTION MECHANISM

Gu Tingting; Huang Yilu; Wang Ya&#x02019;nan; Ren Chenping; Zheng Qiantong

doi:10.19912/j.0254-0096.tynxb.2024-2351

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 474-480. DOI: 10.19912/j.0254-0096.tynxb.2024-2351

ResNet-UNet SHORT-TERM WIND POWER PREDICTION WITH INCORPORATION OF MULTI-HEAD ATTENTION MECHANISM

Gu Tingting, Huang Yilu, Wang Ya’nan, Ren Chenping, Zheng Qiantong

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Abstract

Numerical weather prediction has an important impact on the accuracy of short-term wind power prediction models. In order to fully mine the deep mapping relationship between the information of numerical weather prediction and actual wind power,this paper proposes a short- term wind power forecasting model based on ResNet-UNet model with incorporation of multi-head attention mechanism. Firstly,considering the meteorological factors such as wind direction,wind speed, air pressure, temperature, relative humidity at different altitude levels,the features of numerical weather prediction information are extracted using the grid as a unit and then form the high-dimensional feature vector. Secondly, a wind power prediction model is constructed by fusing the UNet model and ResNet model, in which a multi-head attention mechanism is introduced to capture the spatial correlation characteristics of numerical weather prediction. Finally, the actual data of a wind farm in Zhejiang province is used to verify the model and compared with the prediction accuracy of the UNet, the ResNet, the LSTM, the BP models. The results indicate that the proposed method can effectively impove prediction accuracy.

Key words

wind power / forecasting / convolutional neural networks / numerical weather prediction / multi-head attention

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Gu Tingting, Huang Yilu, Wang Ya’nan, Ren Chenping, Zheng Qiantong. ResNet-UNet SHORT-TERM WIND POWER PREDICTION WITH INCORPORATION OF MULTI-HEAD ATTENTION MECHANISM[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 474-480 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2351

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