Wind and Structures

  • 제38권6호
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  • Pages.461-475
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  • 2024
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Multi-step wind speed forecasting synergistically using generalized S-transform and improved grey wolf optimizer

  • Ruwei Ma (School of Civil Engineering, Shanghai Normal University) ;
  • Zhexuan Zhu (Department of Civil Engineering, School of Mechanics and Engineering Science, Shanghai University) ;
  • Chunxiang Li (Department of Civil Engineering, School of Mechanics and Engineering Science, Shanghai University) ;
  • Liyuan Cao (Department of Civil Engineering, School of Mechanics and Engineering Science, Shanghai University)
  • 투고 : 2023.06.17
  • 심사 : 2024.05.22
  • 발행 : 2024.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

키워드

과제정보

This study is supported by the National Natural Science Foundation of China (Grant No. 52108460)

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