A Review of Wind Power Prediction Methods Based on Multi-Time Scales

Fan Li, Hongzhen Wang (), Dan Wang, Dong Liu and Ke Sun
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Fan Li: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Hongzhen Wang: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Dan Wang: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Dong Liu: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Ke Sun: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China

Energies, 2025, vol. 18, issue 7, 1-50

Abstract: In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind power prediction methods. Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. The analysis reveals that the data-driven prediction model dominates ultra-short-term predictions through rapid response to volatility, while the hybrid method enhances short-term precision. Mid-term predictions increasingly integrate climate dynamics to address seasonal variability. A key contribution lies in unifying fragmented methodologies into a decision support framework that prioritizes the time scale, model adaptability, and spatial constraints. This work enables practitioners to systematically select optimal strategies and advance the development of forecasting systems that are critical for highly renewable energy systems.

Keywords: wind power; prediction method; multi-time scale; prediction model (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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