A Review of Wind Power Prediction Methods Based on Multi-Time Scales
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 fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/7/1713 |
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| author | Fan Li Hongzhen Wang Dan Wang Dong Liu Ke Sun |
| author_facet | Fan Li Hongzhen Wang Dan Wang Dong Liu Ke Sun |
| author_sort | Fan Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eab6e49e36954c65987019cd69901164 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-eab6e49e36954c65987019cd699011642025-08-20T03:06:27ZengMDPI AGEnergies1996-10732025-03-01187171310.3390/en18071713A Review of Wind Power Prediction Methods Based on Multi-Time ScalesFan Li0Hongzhen Wang1Dan Wang2Dong Liu3Ke Sun4State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaSchool of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaIn 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.https://www.mdpi.com/1996-1073/18/7/1713wind powerprediction methodmulti-time scaleprediction model |
| spellingShingle | Fan Li Hongzhen Wang Dan Wang Dong Liu Ke Sun A Review of Wind Power Prediction Methods Based on Multi-Time Scales Energies wind power prediction method multi-time scale prediction model |
| title | A Review of Wind Power Prediction Methods Based on Multi-Time Scales |
| title_full | A Review of Wind Power Prediction Methods Based on Multi-Time Scales |
| title_fullStr | A Review of Wind Power Prediction Methods Based on Multi-Time Scales |
| title_full_unstemmed | A Review of Wind Power Prediction Methods Based on Multi-Time Scales |
| title_short | A Review of Wind Power Prediction Methods Based on Multi-Time Scales |
| title_sort | review of wind power prediction methods based on multi time scales |
| topic | wind power prediction method multi-time scale prediction model |
| url | https://www.mdpi.com/1996-1073/18/7/1713 |
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