Short-term wind power forecasting based on SSA-VMD-LSTM
Wind power forecasting plays a key role in balancing the power supply and load demand of the system. To achieve reasonable processing and decomposition of input data for power prediction, a combined forecasting model is proposed, in which Sparrow Search Algorithm (SSA) is adopted to optimize Variati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723009368 |
| _version_ | 1797388797545021440 |
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| author | Xiaozhi Gao Wang Guo Chunxiao Mei Jitong Sha Yingjun Guo Hexu Sun |
| author_facet | Xiaozhi Gao Wang Guo Chunxiao Mei Jitong Sha Yingjun Guo Hexu Sun |
| author_sort | Xiaozhi Gao |
| collection | DOAJ |
| description | Wind power forecasting plays a key role in balancing the power supply and load demand of the system. To achieve reasonable processing and decomposition of input data for power prediction, a combined forecasting model is proposed, in which Sparrow Search Algorithm (SSA) is adopted to optimize Variational Mode Decomposition (VMD) parameters to solve the problem that VMD is difficult to achieve optimal decomposition by manually setting parameters. Firstly, the SSA is used to optimize the VMD parameters, and then the optimized VMD is used to decompose the data. At the same time, the entropy weight-grey relational analysis method is used to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components is selected as the input of the LSTM prediction model to obtain more accurate prediction results. The example results show that the SSA-VMD-LSTM method can effectively improve the prediction accuracy and reduce the wind power prediction error compared with other methods, which verifies the effectiveness of the prediction model. |
| first_indexed | 2024-03-08T22:46:02Z |
| format | Article |
| id | doaj.art-2ab6d9b8f444402ab8804528e6eb2eb5 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:46:02Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-2ab6d9b8f444402ab8804528e6eb2eb52023-12-17T06:39:22ZengElsevierEnergy Reports2352-48472023-10-019335344Short-term wind power forecasting based on SSA-VMD-LSTMXiaozhi Gao0Wang Guo1Chunxiao Mei2Jitong Sha3Yingjun Guo4Hexu Sun5College of Electrical Engineering, Hebei University of Science and Technology, ShiJiazhuang 050091, ChinaCollege of Electrical Engineering, Hebei University of Science and Technology, ShiJiazhuang 050091, ChinaChina Suntien Green Energy Corporation Limited, Shijiazhuang 050001, ChinaChina Suntien Green Energy Corporation Limited, Shijiazhuang 050001, ChinaCollege of Electrical Engineering, Hebei University of Science and Technology, ShiJiazhuang 050091, ChinaCollege of Electrical Engineering, Hebei University of Science and Technology, ShiJiazhuang 050091, China; Corresponding author.Wind power forecasting plays a key role in balancing the power supply and load demand of the system. To achieve reasonable processing and decomposition of input data for power prediction, a combined forecasting model is proposed, in which Sparrow Search Algorithm (SSA) is adopted to optimize Variational Mode Decomposition (VMD) parameters to solve the problem that VMD is difficult to achieve optimal decomposition by manually setting parameters. Firstly, the SSA is used to optimize the VMD parameters, and then the optimized VMD is used to decompose the data. At the same time, the entropy weight-grey relational analysis method is used to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components is selected as the input of the LSTM prediction model to obtain more accurate prediction results. The example results show that the SSA-VMD-LSTM method can effectively improve the prediction accuracy and reduce the wind power prediction error compared with other methods, which verifies the effectiveness of the prediction model.http://www.sciencedirect.com/science/article/pii/S2352484723009368Wind power forecastingSparrow search algorithmVariational mode decompositionEntropy weight methodGrey relational analysisCombined prediction mode |
| spellingShingle | Xiaozhi Gao Wang Guo Chunxiao Mei Jitong Sha Yingjun Guo Hexu Sun Short-term wind power forecasting based on SSA-VMD-LSTM Energy Reports Wind power forecasting Sparrow search algorithm Variational mode decomposition Entropy weight method Grey relational analysis Combined prediction mode |
| title | Short-term wind power forecasting based on SSA-VMD-LSTM |
| title_full | Short-term wind power forecasting based on SSA-VMD-LSTM |
| title_fullStr | Short-term wind power forecasting based on SSA-VMD-LSTM |
| title_full_unstemmed | Short-term wind power forecasting based on SSA-VMD-LSTM |
| title_short | Short-term wind power forecasting based on SSA-VMD-LSTM |
| title_sort | short term wind power forecasting based on ssa vmd lstm |
| topic | Wind power forecasting Sparrow search algorithm Variational mode decomposition Entropy weight method Grey relational analysis Combined prediction mode |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723009368 |
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