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A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition

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  • Jiang, Zheyong
  • Che, Jinxing
  • He, Mingjun
  • Yuan, Fang

Abstract

Accurate wind speed forecasting is beneficial to reduce the risk of power system caused by wind power uncertainty, which is of great significance for wind power grid connection. However, the randomness and intermittence of wind speed bring great challenges to the accurate wind speed forecasting. In this study, a CGRU multi-step wind speed forecasting model based on secondary decomposition (SD) and multi-label specific XGBoost feature selection method is proposed, which achieves good forecasting performance. By using the secondary decomposition and sample entropy analysis, the original wind speed series is decomposed into multiple sub-series, and the frequency division characteristics of wind speed fluctuations are further extracted. Previous studies rarely focused on feature selection for multi-step wind speed forecasting, the feature selection method suitable for multi-step wind speed forecasting is proposed in this study. By using this method, the optimal input features of each sub-series are selected, which simplifies the data structure and improves the modeling efficiency. An efficient CGRU forecasting model is developed in this study, which can capture the depth characteristics and obtain the time-dependent relationship of wind speed. Experiments conducted in four seasons show that the model can adapt to long-term dependence and extract effective information from original data.

Suggested Citation

  • Jiang, Zheyong & Che, Jinxing & He, Mingjun & Yuan, Fang, 2023. "A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition," Renewable Energy, Elsevier, vol. 203(C), pages 802-827.
  • Handle: RePEc:eee:renene:v:203:y:2023:i:c:p:802-827
    DOI: 10.1016/j.renene.2022.12.124
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    References listed on IDEAS

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    1. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    2. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    3. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    4. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    5. Liu, Hui & Duan, Zhu & Li, Yanfei & Lu, Haibo, 2018. "A novel ensemble model of different mother wavelets for wind speed multi-step forecasting," Applied Energy, Elsevier, vol. 228(C), pages 1783-1800.
    6. Li, Chen & Zhu, Zhijie & Yang, Hufang & Li, Ranran, 2019. "An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization," Energy, Elsevier, vol. 174(C), pages 1219-1237.
    7. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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    Cited by:

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