Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction
Highly reliable wind power prediction is feasible and promising for smart grids integrated with large amounts of wind power. However, the strong fluctuation features of wind power make wind power less predictable. This paper proposes a novel wind power prediction approach, incorporating wind power e...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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China electric power research institute
2025-01-01
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| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10520160/ |
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| _version_ | 1849237961347956736 |
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| author | Peng Lu Zhuo Li Lin Ye Ming Pei Yingying Zheng Yongning Zhao |
| author_facet | Peng Lu Zhuo Li Lin Ye Ming Pei Yingying Zheng Yongning Zhao |
| author_sort | Peng Lu |
| collection | DOAJ |
| description | Highly reliable wind power prediction is feasible and promising for smart grids integrated with large amounts of wind power. However, the strong fluctuation features of wind power make wind power less predictable. This paper proposes a novel wind power prediction approach, incorporating wind power ex-ante and ex-post decomposition and correction. Firstly, the initial wind power during the wind power decomposition stage is decomposed into trend, fluctuation, and residual data, respectively, and the corresponding preliminary prediction models are developed, respectively. Secondly, in the error correction stage, the errors produced by the preliminary prediction model are corrected by persistence methods to compensate for final prediction errors. Moreover, the proposed model's comprehensive deterministic and probabilistic analysis is investigated in depth. Finally, the outcomes of numerical simulations demonstrate that the proposed approach can achieve good performance since it can reduce wind power forecast errors compared to other established deterministic models and uncertainty models. |
| format | Article |
| id | doaj-art-bcd496a9a38e490c8b73384dd70a2602 |
| institution | Kabale University |
| issn | 2096-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj-art-bcd496a9a38e490c8b73384dd70a26022025-08-20T04:01:48ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422025-01-011141454146510.17775/CSEEJPES.2022.0700010520160Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power PredictionPeng Lu0Zhuo Li1Lin Ye2Ming Pei3Yingying Zheng4Yongning Zhao5China Agricultural University,Department of College of Information and Electrical Engineering,Beijing,China,1000843China Agricultural University,Department of College of Information and Electrical Engineering,Beijing,China,1000843China Agricultural University,Department of College of Information and Electrical Engineering,Beijing,China,1000843China Agricultural University,Department of College of Information and Electrical Engineering,Beijing,China,1000843China Agricultural University,Department of College of Information and Electrical Engineering,Beijing,China,1000843China Agricultural University,Department of College of Information and Electrical Engineering,Beijing,China,1000843Highly reliable wind power prediction is feasible and promising for smart grids integrated with large amounts of wind power. However, the strong fluctuation features of wind power make wind power less predictable. This paper proposes a novel wind power prediction approach, incorporating wind power ex-ante and ex-post decomposition and correction. Firstly, the initial wind power during the wind power decomposition stage is decomposed into trend, fluctuation, and residual data, respectively, and the corresponding preliminary prediction models are developed, respectively. Secondly, in the error correction stage, the errors produced by the preliminary prediction model are corrected by persistence methods to compensate for final prediction errors. Moreover, the proposed model's comprehensive deterministic and probabilistic analysis is investigated in depth. Finally, the outcomes of numerical simulations demonstrate that the proposed approach can achieve good performance since it can reduce wind power forecast errors compared to other established deterministic models and uncertainty models.https://ieeexplore.ieee.org/document/10520160/Data preprocessingerror correctionreservoir neural networkwind power prediction |
| spellingShingle | Peng Lu Zhuo Li Lin Ye Ming Pei Yingying Zheng Yongning Zhao Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction CSEE Journal of Power and Energy Systems Data preprocessing error correction reservoir neural network wind power prediction |
| title | Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction |
| title_full | Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction |
| title_fullStr | Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction |
| title_full_unstemmed | Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction |
| title_short | Ex-Ante and Ex-Post Decomposition Strategy for Ultra-Short-Term Wind Power Prediction |
| title_sort | ex ante and ex post decomposition strategy for ultra short term wind power prediction |
| topic | Data preprocessing error correction reservoir neural network wind power prediction |
| url | https://ieeexplore.ieee.org/document/10520160/ |
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