Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE
In order to further improve the accuracy of wind power forecasting, a combined forecasting method based on sparrow search algorithm (SSA) optimizing variational mode decomposition (VMD) parameters was proposed. Firstly, the SSA was used to optimize the VMD parameters, then the optimized VMD was used...
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| Format: | Article |
| Language: | zho |
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Hebei University of Science and Technology
2023-08-01
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| Series: | Journal of Hebei University of Science and Technology |
| Subjects: | |
| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202304001?st=article_issue |
| _version_ | 1797435633320329216 |
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| author | Xiaozhi GAO Wang GUO Yingjun GUO Jingran SONG Hexu SUN |
| author_facet | Xiaozhi GAO Wang GUO Yingjun GUO Jingran SONG Hexu SUN |
| author_sort | Xiaozhi GAO |
| collection | DOAJ |
| description | In order to further improve the accuracy of wind power forecasting, a combined forecasting method based on sparrow search algorithm (SSA) optimizing variational mode decomposition (VMD) parameters was proposed. Firstly, the SSA was used to optimize the VMD parameters, then the optimized VMD was used to decompose the data. Secondly, the entropy weight method and grey relational analysis were combined to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components were selected as the input of the LSTM prediction model to obtain more accurate prediction results. Finally, a wind power probability prediction model based on NKDE was established to effectively quantify the uncertainty of wind power prediction results. The results show that the MAE, RMSE and MAPE of the proposed combination model decrease by 3951%, 33.22% and 40.39%, respectively, compared with the VMD-LSTM model. The SSA-VMD-LSTM-NKDE combination model can not only effectively improve the accuracy of deterministic prediction, but also effectively quantify the uncertainty of wind power prediction results, which provides scientific decision-making basis for wind power prediction. |
| first_indexed | 2024-03-09T10:50:02Z |
| format | Article |
| id | doaj.art-0b3af61b13654fd39c0e48e12b3ab2f9 |
| institution | Directory Open Access Journal |
| issn | 1008-1542 |
| language | zho |
| last_indexed | 2024-03-09T10:50:02Z |
| publishDate | 2023-08-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj.art-0b3af61b13654fd39c0e48e12b3ab2f92023-12-01T07:27:14ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422023-08-0144432333410.7535/hbkd.2023yx04001b202304001Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDEXiaozhi GAO0Wang GUO1Yingjun GUO2Jingran SONG3Hexu SUN4School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaIn order to further improve the accuracy of wind power forecasting, a combined forecasting method based on sparrow search algorithm (SSA) optimizing variational mode decomposition (VMD) parameters was proposed. Firstly, the SSA was used to optimize the VMD parameters, then the optimized VMD was used to decompose the data. Secondly, the entropy weight method and grey relational analysis were combined to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components were selected as the input of the LSTM prediction model to obtain more accurate prediction results. Finally, a wind power probability prediction model based on NKDE was established to effectively quantify the uncertainty of wind power prediction results. The results show that the MAE, RMSE and MAPE of the proposed combination model decrease by 3951%, 33.22% and 40.39%, respectively, compared with the VMD-LSTM model. The SSA-VMD-LSTM-NKDE combination model can not only effectively improve the accuracy of deterministic prediction, but also effectively quantify the uncertainty of wind power prediction results, which provides scientific decision-making basis for wind power prediction.https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202304001?st=article_issuewind energy; sparrow search algorithm; variational mode decomposition; entropy weight method; grey relational analysis; combined prediction mode |
| spellingShingle | Xiaozhi GAO Wang GUO Yingjun GUO Jingran SONG Hexu SUN Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE Journal of Hebei University of Science and Technology wind energy; sparrow search algorithm; variational mode decomposition; entropy weight method; grey relational analysis; combined prediction mode |
| title | Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE |
| title_full | Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE |
| title_fullStr | Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE |
| title_full_unstemmed | Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE |
| title_short | Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE |
| title_sort | short term wind power probabilistic forecasting based on ssa vmd lstm nkde |
| topic | wind energy; sparrow search algorithm; variational mode decomposition; entropy weight method; grey relational analysis; combined prediction mode |
| url | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202304001?st=article_issue |
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