A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks
With the burgeoning development of the wind power industry, the significance of wind power forecasting in enhancing electricity generation efficiency, minimizing energy waste, and improving electrical grid management is increasingly highlighted. To enhance the stability and accuracy of wind power fo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002546 |
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| _version_ | 1849725544980021248 |
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| author | Bingbing Yu Yonggang Wang Jun Wang Yuanchu Ma Wenpeng Li Weigang Zheng |
| author_facet | Bingbing Yu Yonggang Wang Jun Wang Yuanchu Ma Wenpeng Li Weigang Zheng |
| author_sort | Bingbing Yu |
| collection | DOAJ |
| description | With the burgeoning development of the wind power industry, the significance of wind power forecasting in enhancing electricity generation efficiency, minimizing energy waste, and improving electrical grid management is increasingly highlighted. To enhance the stability and accuracy of wind power forecasting, a hybrid model integrating Kepler optimization algorithm (KOA), variational mode decomposition (VMD), and stochastic configuration network (SCN) is proposed. Firstly, the series of wind power data is decomposed using the VMD method optimized by the KOA, aiming to smooth the wind power series while preserving its inherent characteristics. Subsequently, permutation entropy (PE) is employed to order and reconstruct the decomposed wind power subsequences, with the selection of input features by the maximal information coefficient (MIC) and autocorrelation function (ACF). Following this, KOA is utilized to optimize the parameters of the SCN model, further enhancing the predictive performance of the SCN. Finally, a multi-seasonal and multi-scenario wind power forecasting analysis is conducted by using an actual data set from an offshore wind farm in China. Compared with the basic VMD model, the data decomposition efficiency of the optimized VMD model has been improved by 28.86%. Meanwhile, the prediction average error of the proposed model has decreased by 0.1385 compared with the basic prediction model. The results demonstrate that the proposed hybrid model exhibits superior stability and accuracy in short-term wind power prediction. |
| format | Article |
| id | doaj-art-4729b35d0c3a407d9dfc06a7b7f6a0f5 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-4729b35d0c3a407d9dfc06a7b7f6a0f52025-08-20T03:10:27ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-07-0116811070310.1016/j.ijepes.2025.110703A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networksBingbing Yu0Yonggang Wang1Jun Wang2Yuanchu Ma3Wenpeng Li4Weigang Zheng5Shenyang Agricultural University, College of Information and Electrical Engineering, Liaoning, Shenyang 110866, ChinaShenyang Agricultural University, College of Information and Electrical Engineering, Liaoning, Shenyang 110866, China; Corresponding authors.Shenyang Agricultural University, College of Information and Electrical Engineering, Liaoning, Shenyang 110866, China; Corresponding authors.Shenyang Agricultural University, College of Information and Electrical Engineering, Liaoning, Shenyang 110866, ChinaShenyang Agricultural University, College of Information and Electrical Engineering, Liaoning, Shenyang 110866, ChinaShenyang Agricultural University, College of Information and Electrical Engineering, Liaoning, Shenyang 110866, China; Electric Power Supply Research Institute, State Grid Liaoning Electric Power Co., Ltd., Liaoning, Shenyang 110006, ChinaWith the burgeoning development of the wind power industry, the significance of wind power forecasting in enhancing electricity generation efficiency, minimizing energy waste, and improving electrical grid management is increasingly highlighted. To enhance the stability and accuracy of wind power forecasting, a hybrid model integrating Kepler optimization algorithm (KOA), variational mode decomposition (VMD), and stochastic configuration network (SCN) is proposed. Firstly, the series of wind power data is decomposed using the VMD method optimized by the KOA, aiming to smooth the wind power series while preserving its inherent characteristics. Subsequently, permutation entropy (PE) is employed to order and reconstruct the decomposed wind power subsequences, with the selection of input features by the maximal information coefficient (MIC) and autocorrelation function (ACF). Following this, KOA is utilized to optimize the parameters of the SCN model, further enhancing the predictive performance of the SCN. Finally, a multi-seasonal and multi-scenario wind power forecasting analysis is conducted by using an actual data set from an offshore wind farm in China. Compared with the basic VMD model, the data decomposition efficiency of the optimized VMD model has been improved by 28.86%. Meanwhile, the prediction average error of the proposed model has decreased by 0.1385 compared with the basic prediction model. The results demonstrate that the proposed hybrid model exhibits superior stability and accuracy in short-term wind power prediction.http://www.sciencedirect.com/science/article/pii/S0142061525002546Short-term offshore wind power predictionStochastic configuration networkVariational mode decompositionKepler optimization algorithm |
| spellingShingle | Bingbing Yu Yonggang Wang Jun Wang Yuanchu Ma Wenpeng Li Weigang Zheng A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks International Journal of Electrical Power & Energy Systems Short-term offshore wind power prediction Stochastic configuration network Variational mode decomposition Kepler optimization algorithm |
| title | A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks |
| title_full | A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks |
| title_fullStr | A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks |
| title_full_unstemmed | A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks |
| title_short | A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks |
| title_sort | hybrid model for short term offshore wind power prediction combining kepler optimization algorithm with variational mode decomposition and stochastic configuration networks |
| topic | Short-term offshore wind power prediction Stochastic configuration network Variational mode decomposition Kepler optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002546 |
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