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Rolling decomposition method in fusion with echo state network for wind speed forecasting

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  • Hu, Huanling
  • Wang, Lin
  • Zhang, Dabin
  • Ling, Liwen

Abstract

Accurate wind speed forecasting is beneficial to ensure the safe and stable operation of power systems, improve economic benefits, and promote the healthy development of the wind power industry. This study develops a novel hybrid model called VMD-ESN-STO combining the rolling variational mode decomposition (VMD), echo state network (ESN), and subseries to original series (STO) structure for wind speed forecasting. In this model, the rolling method is not only used in decomposition, but also in training and forecasting. The rolling VMD is used to decompose the original wind speed series into several subseries according to the rolling schema, ESN is used to forecast, and STO structure determines the input and output of the forecasting model. Four wind speed datasets are utilized for wind speed forecasting experiments to validate the applicability and accuracy of the developed model. Mean absolute percentage errors of VMD-ESN-STO in the four datasets are 4.4511%, 2.4451%, 4.1400%, and 2.6178%, respectively, which are far less than the errors for the six comparative models. The developed VMD-ESN-STO is an appropriate tool for wind speed forecasting due to its superior forecasting performance.

Suggested Citation

  • Hu, Huanling & Wang, Lin & Zhang, Dabin & Ling, Liwen, 2023. "Rolling decomposition method in fusion with echo state network for wind speed forecasting," Renewable Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123010157
    DOI: 10.1016/j.renene.2023.119101
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    3. Xuejun Chen & Ying Wang & Haitao Zhang & Jianzhou Wang, 2024. "A novel hybrid forecasting model with feature selection and deep learning for wind speed research," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1682-1705, August.
    4. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    5. Lin, Qingcheng & Cai, Huiling & Liu, Hanwei & Li, Xuefeng & Xiao, Hui, 2024. "A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection," Energy, Elsevier, vol. 288(C).

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