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

Dongran Song, Xiao Tan, Qian Huang, Li Wang, Mi Dong (), Jian Yang and Solomin Evgeny
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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

Energies, 2024, vol. 17, issue 6, 1-22

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.

Keywords: wind prediction; artificial intelligence; data preprocessing; feature extraction; parameter optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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