NEW ULTRA-SHORT TERM WIND POWER COMBINATION FORECASTING METHOD BASED ON TCN-SENet-Transformer

Yin Yupeng; Kuang Honghai; Li Xingyu; Cao Shipeng; Yang Huixian

doi:10.19912/j.0254-0096.tynxb.2024-1314

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 735-741. DOI: 10.19912/j.0254-0096.tynxb.2024-1314

NEW ULTRA-SHORT TERM WIND POWER COMBINATION FORECASTING METHOD BASED ON TCN-SENet-Transformer

Yin Yupeng, Kuang Honghai, Li Xingyu, Cao Shipeng, Yang Huixian

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Abstract

Aiming at the problems that traditional wind power forecasting methods have few considerations in terms of the close relationship between time series and spatial global features and parallel processing, and the prediction reliability is limited, an ultra-short term wind power forecasting method based on cross-attention fusion of spatial-temporal features of TCN-SENet-Transformer is proposed. Firstly, squeeze and excitation networks（SENet） are used to adjust the channel feature weights, and temporal convolutional networks（TCN） are used to capture the spatial features of the data. Meanwhile, Transformer is used to identify the long-term timing characteristics of multi-feature data. Then, cross-attention（CA） is introduced to integrate temporal and spatial features. Finally, the actual data of a wind farm in China is used to forecast ultra-short term wind power, and the comparison is made with other prediction models. The results of example analysis show that the proposed combined prediction model effectively improves the prediction accuracy.

Key words

wind power forecast / time-domain convolutional network（TCN） / squeeze-and-excitation networks（SENet） / Transformer model / cross-attention（mechanism） / ultra-short-term

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Yin Yupeng, Kuang Honghai, Li Xingyu, Cao Shipeng, Yang Huixian. NEW ULTRA-SHORT TERM WIND POWER COMBINATION FORECASTING METHOD BASED ON TCN-SENet-Transformer[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 735-741 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1314

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