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Short-term wind power forecasting model based on temporal convolutional network and Informer

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  • Gong, Mingju
  • Yan, Changcheng
  • Xu, Wei
  • Zhao, Zhixuan
  • Li, Wenxiang
  • Liu, Yan
  • Li, Sheng

Abstract

Wind power forecast remains challenging owing to the unpredictable peculiarity of wind. The accuracy of wind power predictions is critical to the stability of the whole system. This research proposes a hybrid prediction model based on a temporal convolutional network and an Informer to increase the accuracy of wind power forecasting. The hidden temporal features in the dataset are first extracted using TCN, and the Informer is then employed to predict wind power. Additionally, a cutting-edge AdaBelief optimizer is used to boost prediction accuracy even more. The validity of the model is verified by comparing with other wind speed prediction methods. The findings reveal that the proposed model has the highest prediction accuracy and the best forecast effect.

Suggested Citation

  • Gong, Mingju & Yan, Changcheng & Xu, Wei & Zhao, Zhixuan & Li, Wenxiang & Liu, Yan & Li, Sheng, 2023. "Short-term wind power forecasting model based on temporal convolutional network and Informer," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025653
    DOI: 10.1016/j.energy.2023.129171
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    References listed on IDEAS

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    1. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
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    Cited by:

    1. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    2. Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).

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