ULTRA-SHORT-TERM MULTI-STEP PREDICTION OF WIND POWER BASED ON LOSS FUNCTION IMPROVEMENT AND PATCH TIMING TRANSFORMER NETWORK

Yan Wuyuxin; Zhang Haibo; Liu Tonghui; Huang Songtao; Shang Guozheng

doi:10.19912/j.0254-0096.tynxb.2024-1903

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 510-521. DOI: 10.19912/j.0254-0096.tynxb.2024-1903

ULTRA-SHORT-TERM MULTI-STEP PREDICTION OF WIND POWER BASED ON LOSS FUNCTION IMPROVEMENT AND PATCH TIMING TRANSFORMER NETWORK

Yan Wuyuxin¹, Zhang Haibo¹, Liu Tonghui¹, Huang Songtao², Shang Guozheng²

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Abstract

To enhance the accuracy of ultra-short-term multi-step wind power forecasting, this study proposes a novel model that integrates improvements in the loss function with a patch-based temporal Transformer network. Specifically, an image-based anomaly detection and cleaning algorithm is firstly employed for data preprocessing, thereby enhancing the quality of the wind power data. Subsequently, to improve the robustness of the Transformer architecture and to strengthen its ability to capture local sequential dependencies, a patch module and a channel-independent strategy are incorporated into the standard Transformer framework. Finally, a novel multivariate nonlinear loss function is designed to effectively filter noise and to enhance the model's sensitivity to shape variations during sequence prediction. Extensive experimental results demonstrate that the proposed approach significantly outperforms several baseline models across multiple error metrics, thereby achieving substantial improvements in ultra-short-term multi-step wind power forecasting accuracy.

Key words

wind forecasting / data processing / Transformer / loss function / multi-step prediction

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Yan Wuyuxin, Zhang Haibo, Liu Tonghui, Huang Songtao, Shang Guozheng. ULTRA-SHORT-TERM MULTI-STEP PREDICTION OF WIND POWER BASED ON LOSS FUNCTION IMPROVEMENT AND PATCH TIMING TRANSFORMER NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 510-521 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1903

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