An overview of deterministic and probabilistic forecasting methods of wind energy
Yuying Xie,
Chaoshun Li,
Mengying Li,
Fangjie Liu,
Meruyert Taukenova
Affiliations
Yuying Xie
China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China; Department of Mechanical Engineering and Research Institute for Smart Energy, the Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
Chaoshun Li
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China; Corresponding author
Mengying Li
Department of Mechanical Engineering and Research Institute for Smart Energy, the Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
Fangjie Liu
China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China
Meruyert Taukenova
China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China
Summary: In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods to gain insights of the state-of-the-art development status. However, existing literature reviews only focus on a subclass of methods, such as multi-objective optimization and machine learning methods while lacking the full particulars of wind forecasting field. Furthermore, the classification of wind forecasting methods is unclear and incomplete, especially considering the rapid development of this field. Therefore, this article aims to provide a systematic review of the existing deterministic and probabilistic wind forecasting methods, from the perspectives of data source, model evaluation framework, technical background, theoretical basis, and model performance. It is expected that this work will provide junior researchers with broad and detailed information on wind forecasting for their future development of more accurate and practical wind forecasting models.
