Short-Term Wind Power Forecasting Based on Dual-Optimized VMD-CNN-BiLSTM.
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| Title: | Short-Term Wind Power Forecasting Based on Dual-Optimized VMD-CNN-BiLSTM. |
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| Authors: | Sun, Xiaohan1 (AUTHOR), Han, Bing1,2 (AUTHOR), Song, Yuting1,3 (AUTHOR), Wang, Youxin1 (AUTHOR), Hou, Enguang1,2 (AUTHOR), Wang, Jiangang3 (AUTHOR), Xu, Yanliang2 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2317. 24p. |
| Subject Terms: | *Wind forecasting, *Optimization algorithms, *Deep learning, *Metaheuristic algorithms, *Long short-term memory, *Convolutional neural networks |
| Abstract: | To tackle issues such as high data volatility, temporal dependencies, complex feature extraction, and low parameter tuning efficiency in wind power forecasting, this paper proposes a dual-optimization model for short-term wind power forecasting based on RIME-VMD and MSSA-CNN-BiLSTM. First, the Rime Optimization Algorithm (RIME) is employed to adaptively refine the key parameters of Variational Mode Decomposition (VMD), decomposing wind power into intrinsic modal functions (IMFs) of different frequencies to reduce signal complexity. Second, by integrating the local feature extraction capabilities of Convolutional Neural Network (CNN) with the bidirectional temporal dependency capture capabilities of Bidirectional Long Short-Term Memory Network (BiLSTM), a hybrid deep learning architecture is constructed. Additionally, the Multi-strategy Sparrow Search Algorithm (MSSA) is introduced to perform global hyperparameter optimization, thereby addressing the shortcomings of manual parameter tuning. The final power forecast is obtained through the prediction of each IMF component and the reconstruction of the results. Experiments demonstrate that the presented prediction model attains a root mean square error (RMSE) of 0.0333, a mean absolute error (MAE) of 0.0265, and a coefficient of determination (R2) of 0.9901. Seasonal validation shows that the model's R2 exceeds 0.983 in all four seasons—spring, summer, autumn, and winter—demonstrating good generalization capability. Relative to the BiLSTM model, its RMSE and MAE are reduced by 50.52% and 46.57%, respectively, while R2 increases by 3.36%, effectively addressing the issue of insufficient accuracy in short-term wind power forecasting. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194141432 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Short-Term Wind Power Forecasting Based on Dual-Optimized VMD-CNN-BiLSTM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Xiaohan%22">Sun, Xiaohan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Bing%22">Han, Bing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Yuting%22">Song, Yuting</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Youxin%22">Wang, Youxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hou%2C+Enguang%22">Hou, Enguang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jiangang%22">Wang, Jiangang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Yanliang%22">Xu, Yanliang</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2317. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To tackle issues such as high data volatility, temporal dependencies, complex feature extraction, and low parameter tuning efficiency in wind power forecasting, this paper proposes a dual-optimization model for short-term wind power forecasting based on RIME-VMD and MSSA-CNN-BiLSTM. First, the Rime Optimization Algorithm (RIME) is employed to adaptively refine the key parameters of Variational Mode Decomposition (VMD), decomposing wind power into intrinsic modal functions (IMFs) of different frequencies to reduce signal complexity. Second, by integrating the local feature extraction capabilities of Convolutional Neural Network (CNN) with the bidirectional temporal dependency capture capabilities of Bidirectional Long Short-Term Memory Network (BiLSTM), a hybrid deep learning architecture is constructed. Additionally, the Multi-strategy Sparrow Search Algorithm (MSSA) is introduced to perform global hyperparameter optimization, thereby addressing the shortcomings of manual parameter tuning. The final power forecast is obtained through the prediction of each IMF component and the reconstruction of the results. Experiments demonstrate that the presented prediction model attains a root mean square error (RMSE) of 0.0333, a mean absolute error (MAE) of 0.0265, and a coefficient of determination (R2) of 0.9901. Seasonal validation shows that the model's R2 exceeds 0.983 in all four seasons—spring, summer, autumn, and winter—demonstrating good generalization capability. Relative to the BiLSTM model, its RMSE and MAE are reduced by 50.52% and 46.57%, respectively, while R2 increases by 3.36%, effectively addressing the issue of insufficient accuracy in short-term wind power forecasting. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141432 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19102317 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 2317 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Convolutional neural networks Type: general Titles: – TitleFull: Short-Term Wind Power Forecasting Based on Dual-Optimized VMD-CNN-BiLSTM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Xiaohan – PersonEntity: Name: NameFull: Han, Bing – PersonEntity: Name: NameFull: Song, Yuting – PersonEntity: Name: NameFull: Wang, Youxin – PersonEntity: Name: NameFull: Hou, Enguang – PersonEntity: Name: NameFull: Wang, Jiangang – PersonEntity: Name: NameFull: Xu, Yanliang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 10 Titles: – TitleFull: Energies (19961073) Type: main |
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