Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance
Wind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new informat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9154667/ |
| _version_ | 1818932186832699392 |
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| author | Yong Sun Zhenyuan Li Xinnan Yu Baoju Li Mao Yang |
| author_facet | Yong Sun Zhenyuan Li Xinnan Yu Baoju Li Mao Yang |
| author_sort | Yong Sun |
| collection | DOAJ |
| description | Wind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new information, the accuracy of ultra-short-term wind power prediction (WPP) is still not satisfactory. Therefore, a combined prediction method based on the day-ahead Numerical Weather Prediction (NWP) location technology is proposed. Firstly, the time points with low forecasting accuracy of rolling WPP are approximately located by the NWP information and time windows, and then the hybrid approach combined with neural network and persistence method is presented to predict the future wind power output. The results of the case study show that compared with other classical prediction methods, this method can effectively improve the ultra-short-term prediction accuracy of wind power and verify the effectiveness of the proposed method. |
| first_indexed | 2024-12-20T04:28:29Z |
| format | Article |
| id | doaj.art-b625bd637ef349f2840bbc1cc72a4933 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:28:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b625bd637ef349f2840bbc1cc72a49332022-12-21T19:53:27ZengIEEEIEEE Access2169-35362020-01-01814770314771010.1109/ACCESS.2020.30123069154667Research on Ultra-Short-Term Wind Power Prediction Considering Source RelevanceYong Sun0Zhenyuan Li1Xinnan Yu2https://orcid.org/0000-0002-4771-2190Baoju Li3Mao Yang4https://orcid.org/0000-0002-1535-0498State Grid Jilin Electric Power Company Ltd., Changchun, ChinaState Grid Jilin Electric Power Company Ltd., Changchun, ChinaKey Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Changchun, ChinaState Grid Jilin Electric Power Company Ltd., Changchun, ChinaKey Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Changchun, ChinaWind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new information, the accuracy of ultra-short-term wind power prediction (WPP) is still not satisfactory. Therefore, a combined prediction method based on the day-ahead Numerical Weather Prediction (NWP) location technology is proposed. Firstly, the time points with low forecasting accuracy of rolling WPP are approximately located by the NWP information and time windows, and then the hybrid approach combined with neural network and persistence method is presented to predict the future wind power output. The results of the case study show that compared with other classical prediction methods, this method can effectively improve the ultra-short-term prediction accuracy of wind power and verify the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/9154667/Wind power predictionneural networkpersistence forecastingtime windownumerical weather prediction |
| spellingShingle | Yong Sun Zhenyuan Li Xinnan Yu Baoju Li Mao Yang Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance IEEE Access Wind power prediction neural network persistence forecasting time window numerical weather prediction |
| title | Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance |
| title_full | Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance |
| title_fullStr | Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance |
| title_full_unstemmed | Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance |
| title_short | Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance |
| title_sort | research on ultra short term wind power prediction considering source relevance |
| topic | Wind power prediction neural network persistence forecasting time window numerical weather prediction |
| url | https://ieeexplore.ieee.org/document/9154667/ |
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