Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system
Abstract The inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal‐preserving embedding broad learning system (MTPE‐BLS)...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Energy Systems Integration |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/esi2.12178 |
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| _version_ | 1832582977057456128 |
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| author | Jiayi Qiu Yatao Shen Ziwen Gu Zijian Wang Wenmei Li Ziqian Tao Ziwen Guo Yaqun Jiang Chun Huang |
| author_facet | Jiayi Qiu Yatao Shen Ziwen Gu Zijian Wang Wenmei Li Ziqian Tao Ziwen Guo Yaqun Jiang Chun Huang |
| author_sort | Jiayi Qiu |
| collection | DOAJ |
| description | Abstract The inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal‐preserving embedding broad learning system (MTPE‐BLS) is proposed. MTPE‐BLS used the localised behaviour of wind speed data, which is simpler to model and analyse than global patterns. Firstly, frequency clustering‐based variational mode decomposition (FC‐VMD) is proposed to deal with the non‐stationary wind speed data into multiple intrinsic mode functions (IMFs). Then, temporal‐preserving embedding (TPE) is proposed to extract the underlying temporal manifold structure from the decomposed IMFs. Finally, the extracted features are mapped into the broad learning system (BLS) to establish an accurate prediction model. Experimental results on two real‐world wind speed datasets demonstrate the best performance of the proposed MTPE‐BLS model compared to that of others. Compared to the original BLS, the MTPE‐BLS achieves significant improvements, reducing the root mean square error (RMSE) and mean absolute error (MAE) by an average of 48.57% and 47.72%, respectively. |
| format | Article |
| id | doaj-art-cb42cd30743a420594220e0c687825e9 |
| institution | Kabale University |
| issn | 2516-8401 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj-art-cb42cd30743a420594220e0c687825e92025-01-29T05:18:54ZengWileyIET Energy Systems Integration2516-84012024-12-016S191893110.1049/esi2.12178Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning systemJiayi Qiu0Yatao Shen1Ziwen Gu2Zijian Wang3Wenmei Li4Ziqian Tao5Ziwen Guo6Yaqun Jiang7Chun Huang8College of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaCollege of Electrical and Information Engineering Hunan University Changsha ChinaAbstract The inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal‐preserving embedding broad learning system (MTPE‐BLS) is proposed. MTPE‐BLS used the localised behaviour of wind speed data, which is simpler to model and analyse than global patterns. Firstly, frequency clustering‐based variational mode decomposition (FC‐VMD) is proposed to deal with the non‐stationary wind speed data into multiple intrinsic mode functions (IMFs). Then, temporal‐preserving embedding (TPE) is proposed to extract the underlying temporal manifold structure from the decomposed IMFs. Finally, the extracted features are mapped into the broad learning system (BLS) to establish an accurate prediction model. Experimental results on two real‐world wind speed datasets demonstrate the best performance of the proposed MTPE‐BLS model compared to that of others. Compared to the original BLS, the MTPE‐BLS achieves significant improvements, reducing the root mean square error (RMSE) and mean absolute error (MAE) by an average of 48.57% and 47.72%, respectively.https://doi.org/10.1049/esi2.12178pattern clusteringrenewable energy sourcessmart power gridswind power |
| spellingShingle | Jiayi Qiu Yatao Shen Ziwen Gu Zijian Wang Wenmei Li Ziqian Tao Ziwen Guo Yaqun Jiang Chun Huang Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system IET Energy Systems Integration pattern clustering renewable energy sources smart power grids wind power |
| title | Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system |
| title_full | Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system |
| title_fullStr | Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system |
| title_full_unstemmed | Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system |
| title_short | Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system |
| title_sort | wind speed prediction model based on multiscale temporal preserving embedding broad learning system |
| topic | pattern clustering renewable energy sources smart power grids wind power |
| url | https://doi.org/10.1049/esi2.12178 |
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