Wind Power Prediction Method and Outlook in Microtopographic Microclimate

Jia He, Fangchun Tang, Junxin Feng, Chaoyang Liu, Mengyan Ni, Youguang Chen, Hongdeng Mei, Qin Hu () and Xingliang Jiang
Additional contact information
Jia He: China Resources Power Technology and Research Institute Co., Ltd., Shenzhen 523808, China
Fangchun Tang: China Resources Power Technology and Research Institute Co., Ltd., Shenzhen 523808, China
Junxin Feng: China Resources Power Technology and Research Institute Co., Ltd., Shenzhen 523808, China
Chaoyang Liu: China Resources New Energy (Lianzhou) Wind Energy Co., Ltd., Lianzhou 513444, China
Mengyan Ni: China Resources New Energy (Liping) Wind Energy Co., Ltd., Liping 557300, China
Youguang Chen: China Resources New Energy (Lianzhou) Wind Energy Co., Ltd., Lianzhou 513444, China
Hongdeng Mei: China Resources New Energy (Liping) Wind Energy Co., Ltd., Liping 557300, China
Qin Hu: Xuefeng Mountain National Field Scientific Observation and Research Station on Energy and Equipment Safety, Chongqing University, Chongqing 400044, China
Xingliang Jiang: Xuefeng Mountain National Field Scientific Observation and Research Station on Energy and Equipment Safety, Chongqing University, Chongqing 400044, China

Energies, 2025, vol. 18, issue 7, 1-20

Abstract: With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of the power grid. This paper first introduces the current status of wind power prediction methods under normal weather, and introduces them in detail from three aspects: physical model method, statistical prediction method and combined prediction method. Then, from the perspectives of numerical simulation analysis and statistical prediction methods, the wind power prediction method under icy conditions is introduced, and the problems faced by the existing methods are pointed out. Then, the accurate prediction of wind power under icing weather is considered, and two possible research directions for wind power prediction under icy weather are proposed: a statistical prediction method for classifying and clustering wind turbines according to microtopography, combining large-scale meteorological parameters with small-scale meteorological parameter correlation models and using machine learning for cluster power prediction, and a power prediction model converted from the power prediction model during normal operation of the wind turbine to the power prediction model during icing. Finally, the research on wind power prediction under ice-covered weather is summarized, and further research in this area is prospected.

Keywords: wind power prediction; ice-covered weather; machine learning; micro-terrain (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: 2025
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