SHORT-TERM WIND POWER PREDICTION USING TRANSFORMER WITH MULTI-SCALE FEATURE EXTRACTION

Xu Wu; Fan Xinhao; Shen Zhifang; Liu Yang

doi:10.19912/j.0254-0096.tynxb.2023-1604

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 640-648. DOI: 10.19912/j.0254-0096.tynxb.2023-1604

SHORT-TERM WIND POWER PREDICTION USING TRANSFORMER WITH MULTI-SCALE FEATURE EXTRACTION

Xu Wu, Fan Xinhao, Shen Zhifang, Liu Yang

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Abstract

Upon addressing the issue of single-scale feature extraction for short-term wind power forecasting, a Transformer-based model known as “MTPNet” （multi-scale transformer power network） is developed. Firstly, based on the Transformer architecture, dimension-invariant embeddings is employed to design a multi-scale feature extraction network to mine the temporal characteristics within the wind power sequence, to ensure that the feature dimensions remain unchanged during feature extraction. Secondly, long short-term memory （LSTM） network with a fusion self-attention mechanism is used to capture the global dependencies between meteorological conditions and power output. Finally, the temporal characteristics of the wind power sequence and the dependencies on meteorological conditions are integrated to achieve short-term wind power prediction. Simulation results demonstrate that the prediction accuracy of MTPNet model is improved. Further ablation experiments confirm the reliability and effectiveness of each module in the model, highlighting its practical value.

Key words

wind power forecast / Transformer / attention mechanism / feature extraction / long short-term memory network / dimension invariant embedding

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Xu Wu, Fan Xinhao, Shen Zhifang, Liu Yang. SHORT-TERM WIND POWER PREDICTION USING TRANSFORMER WITH MULTI-SCALE FEATURE EXTRACTION[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 640-648 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1604

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