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Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Author

Listed:
  • Dongran Song

    (School of Automation, Central South University, Changsha 410083, China)

  • Xiao Tan

    (School of Automation, Central South University, Changsha 410083, China)

  • Qian Huang

    (School of Automation, Central South University, Changsha 410083, China)

  • Li Wang

    (School of Automation, Central South University, Changsha 410083, China)

  • Mi Dong

    (School of Automation, Central South University, Changsha 410083, China)

  • Jian Yang

    (School of Automation, Central South University, Changsha 410083, China)

  • Solomin Evgeny

    (Department of Electric Stations, Grids and Power, Supply Systems, South Ural State University, 76 Prospekt Lenina, 454080 Chelyabinsk, Russia)

Abstract

Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

Suggested Citation

  • Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1270-:d:1352673
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