An Improved Regularization Stochastic Configuration Network for Robust Wind Speed Prediction.
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| Title: | An Improved Regularization Stochastic Configuration Network for Robust Wind Speed Prediction. |
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| Authors: | Jin, Fuguo1 (AUTHOR), Chen, Xinyu1,2 (AUTHOR), Yu, Yuanhao2 (AUTHOR), Li, Kun2 (AUTHOR) likun@lntu.edu.cn |
| Source: | Energies (19961073). Dec2025, Vol. 18 Issue 23, p6170. 20p. |
| Subjects: | Wind forecasting, Regularization parameter, Machine learning, Feature selection |
| Abstract: | To address the limitations of Stochastic Configured Networks (SCNs) in wind speed prediction, specifically insufficient regularization capability and a high risk of overfitting, this paper proposes a novel Regularized Stochastic Configured Network (RSCN). By integrating L1 and L2 regularization techniques from Elastic Net, RSCNs achieve feature sparsity while preserving prediction accuracy. Furthermore, a dynamic loss coefficient and a penalty term based on historical training loss are introduced to adaptively modulate the regularization strength during model training. Experimental results demonstrate that RSCNs achieve superior prediction performance and enhanced stability across four benchmark regression datasets and two real-world wind speed datasets. Compared with conventional SCNs and the swarm intelligence optimization-based variant HPO-SCNs, RSCNs significantly reduce the performance gap between training and test sets while maintaining high predictive accuracy. On average, improvements in R 2 , MAE, and RMSE exceed 50% reduction in error discrepancies. The proposed method offers an effective solution for wind power forecasting by effectively balancing generalization ability and computational efficiency, thereby holding practical significance for real-world applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 190517897 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Improved Regularization Stochastic Configuration Network for Robust Wind Speed Prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jin%2C+Fuguo%22">Jin, Fuguo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xinyu%22">Chen, Xinyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Yuanhao%22">Yu, Yuanhao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Kun%22">Li, Kun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> likun@lntu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Dec2025, Vol. 18 Issue 23, p6170. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Regularization+parameter%22">Regularization parameter</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the limitations of Stochastic Configured Networks (SCNs) in wind speed prediction, specifically insufficient regularization capability and a high risk of overfitting, this paper proposes a novel Regularized Stochastic Configured Network (RSCN). By integrating L1 and L2 regularization techniques from Elastic Net, RSCNs achieve feature sparsity while preserving prediction accuracy. Furthermore, a dynamic loss coefficient and a penalty term based on historical training loss are introduced to adaptively modulate the regularization strength during model training. Experimental results demonstrate that RSCNs achieve superior prediction performance and enhanced stability across four benchmark regression datasets and two real-world wind speed datasets. Compared with conventional SCNs and the swarm intelligence optimization-based variant HPO-SCNs, RSCNs significantly reduce the performance gap between training and test sets while maintaining high predictive accuracy. On average, improvements in R 2 , MAE, and RMSE exceed 50% reduction in error discrepancies. The proposed method offers an effective solution for wind power forecasting by effectively balancing generalization ability and computational efficiency, thereby holding practical significance for real-world applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18236170 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 6170 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Regularization parameter Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: An Improved Regularization Stochastic Configuration Network for Robust Wind Speed Prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jin, Fuguo – PersonEntity: Name: NameFull: Chen, Xinyu – PersonEntity: Name: NameFull: Yu, Yuanhao – PersonEntity: Name: NameFull: Li, Kun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 23 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
