Short-term multi-step wind power prediction model based on Pt-Transformer neural network integrating spatio-temporal feature and sparse attention
•A wind power forecasting method based on the time series Transformer model is proposed.•Novel model improves noisy data handling, time-series dependency capture, and efficiency.•Introduce a multi-step autoregressive prediction framework using Teacher forcing. The accuracy and stability of wind powe...
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| Vydáno v: | Electric power systems research Ročník 248; s. 111970 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.11.2025
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| ISSN: | 0378-7796 |
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| Abstract | •A wind power forecasting method based on the time series Transformer model is proposed.•Novel model improves noisy data handling, time-series dependency capture, and efficiency.•Introduce a multi-step autoregressive prediction framework using Teacher forcing.
The accuracy and stability of wind power prediction are crucial for grid dispatching. However, precise wind power prediction faces three major challenges: effective mitigating data noise, representing complex spatio-temporal features, and selecting an appropriate prediction model. To address these challenges, this paper proposes a new wind power prediction model called process temporal transformer (Pt-Transformer). Firstly, an exponential weighted moving average (EWMA) method is employed to filter the noise of original wind characteristics while maintaining data complexity. Secondly, a seasonal trend decomposition (STD) model combined with a temporal autoencoder (TAE) is implemented to efficiently extract complex spatio-temporal characteristics. Furthermore, a sparse attention mechanism combined with the Transformer architecture is introduced to effectively extract high-dimensional latent space representations. Finally, two case studies are conducted using data from a cluster of wind farms and a single wind farm in central China, and three models, namely Transformer, Prob-Transformer, and Pt-Transformer, are used for prediction. The results show that the Pt-Transformer model outperforms the other two models in prediction accuracy and stability, reflecting its excellent performance in short-term multi-step wind power generation prediction. |
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| AbstractList | •A wind power forecasting method based on the time series Transformer model is proposed.•Novel model improves noisy data handling, time-series dependency capture, and efficiency.•Introduce a multi-step autoregressive prediction framework using Teacher forcing.
The accuracy and stability of wind power prediction are crucial for grid dispatching. However, precise wind power prediction faces three major challenges: effective mitigating data noise, representing complex spatio-temporal features, and selecting an appropriate prediction model. To address these challenges, this paper proposes a new wind power prediction model called process temporal transformer (Pt-Transformer). Firstly, an exponential weighted moving average (EWMA) method is employed to filter the noise of original wind characteristics while maintaining data complexity. Secondly, a seasonal trend decomposition (STD) model combined with a temporal autoencoder (TAE) is implemented to efficiently extract complex spatio-temporal characteristics. Furthermore, a sparse attention mechanism combined with the Transformer architecture is introduced to effectively extract high-dimensional latent space representations. Finally, two case studies are conducted using data from a cluster of wind farms and a single wind farm in central China, and three models, namely Transformer, Prob-Transformer, and Pt-Transformer, are used for prediction. The results show that the Pt-Transformer model outperforms the other two models in prediction accuracy and stability, reflecting its excellent performance in short-term multi-step wind power generation prediction. |
| ArticleNumber | 111970 |
| Author | Guo, Xianghui Guo, Ruipeng Yang, Pengju Yang, Xiaoliang Sun, Zhenyang Zhang, Zhiyan |
| Author_xml | – sequence: 1 givenname: Zhiyan surname: Zhang fullname: Zhang, Zhiyan email: 2004074@zzuli.edu.cn organization: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China – sequence: 2 givenname: Zhenyang surname: Sun fullname: Sun, Zhenyang email: 2020994906@qq.com organization: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China – sequence: 3 givenname: Xianghui surname: Guo fullname: Guo, Xianghui email: 1781330452@qq.com organization: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China – sequence: 4 givenname: Ruipeng surname: Guo fullname: Guo, Ruipeng organization: School of Mechanical and Electrical Engineering, Henan Vocational College of Applied Technology, Zhengzhou, Henan Province 450042, China – sequence: 5 givenname: Xiaoliang orcidid: 0000-0003-4579-322X surname: Yang fullname: Yang, Xiaoliang email: yangxl@hnu.edu.cn organization: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China – sequence: 6 givenname: Pengju surname: Yang fullname: Yang, Pengju email: 569197581@qq.com organization: Senior Engineer Jinshan Power Supply Company, State Grid Shanghai Municipal Electric Power Company, Shanghai 200540, China |
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| Keywords | Seasonal trend decomposition Sparse attention mechanism Wind power prediction Transformer Temporal autoencoder |
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| SubjectTerms | Seasonal trend decomposition Sparse attention mechanism Temporal autoencoder Transformer Wind power prediction |
| Title | Short-term multi-step wind power prediction model based on Pt-Transformer neural network integrating spatio-temporal feature and sparse attention |
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