HYBRID MODEL FOR ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON TIME EMBEDDING AND ATTENTION MECHANISM

Tian Jianhui; Huang Guoyong; Deng Weiquan; Liu Fabing

doi:10.19912/j.0254-0096.tynxb.2024-1118

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 746-752. DOI: 10.19912/j.0254-0096.tynxb.2024-1118

HYBRID MODEL FOR ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON TIME EMBEDDING AND ATTENTION MECHANISM

Tian Jianhui¹, Huang Guoyong¹, Deng Weiquan¹, Liu Fabing²

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Abstract

This paper proposes a hybrid model for ultra-short term wind speed prediction based on UAV-based wind measurement. The model firstly integrates Time2Vec temporal embedding layer to extract the complex non-linear temporal information, and secondly adopts deep convolutional neural networks with wide first-layer kernels（WDCNN） to accurately extract the wind speed features while further reducing the influence of high-frequency noise, and on the basis of the accurate feature extraction, the accurate prediction of short-term wind speed is achiered by combining bi-directional gating unit （BiGRU） and the attention mechanism to achieve the accurate prediction of short-term wind speed. The accuracy of the hybrid model proposed in this paper is superior to the comparative method for wind speed prediction at all altitude layers when wind speed data acquired by a UAV mounted at 5-25 m is used for validation. Furthermore, at the 25 m altitude layer, where wind speed volatility is the greatest, the model's wind speed prediction demonstrates a lower mean absolute error （E_MAE）, root mean square error （E_RMSE）, and mean square error （E_MSE） than that of the comparative method. The values are 0.1455, 0.4124, and 0.1700 m/s, respectively, which are lower than those of the comparison model by 45%, 25%, and 43%, respectively. These values have been reduced by more than 45%, 25%, and 43%, respectively, in comparison with the values obtained from the comparative model.

Key words

wind power / wind speed / neural networks / time series / Time2Vec / attentional mechanism / unmanned aerial vehicle （UAV） wind measurement

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Tian Jianhui, Huang Guoyong, Deng Weiquan, Liu Fabing. HYBRID MODEL FOR ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON TIME EMBEDDING AND ATTENTION MECHANISM[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 746-752 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1118

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