A multi-input and three-output wind speed point-interval prediction system based on constrained many-objective optimization problem
•The first three-output neural network for wind speed point-interval forecast.•A many-objective optimization problem with coverage constraint is developed.•The proposed CKNSGA-III perfectly solves many-objective optimization problem.•The theoretical proof of the Pareto front existence in CKNSGA-III...
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| Vydáno v: | Information sciences Ročník 720; s. 122531 |
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01.12.2025
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| ISSN: | 0020-0255 |
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| Abstract | •The first three-output neural network for wind speed point-interval forecast.•A many-objective optimization problem with coverage constraint is developed.•The proposed CKNSGA-III perfectly solves many-objective optimization problem.•The theoretical proof of the Pareto front existence in CKNSGA-III is given.
Neural networks play a key role in wind speed deterministic and uncertainty analysis, significantly improving wind energy utilization efficiency and reducing power system costs. However, existing studies often rely on complex neural architectures, leading to excessive computational time, and fail to integrate point and interval predictions, disconnecting deterministic and uncertainty analysis, thereby affecting prediction efficiency. To address these issues, this paper presents a novel three-output wind speed prediction system via constrained multi-objective optimization. The framework minimizes mean squared error (MSE), mean absolute error (MAE), and prediction interval normalized average width (PINAW) while maximizing prediction interval coverage (PICP), with adjustable coverage constraints for diverse interval demands. Leveraging the outlier-robust extreme learning machine (ORELM) as the predictor, the system outputs point values and interval bounds simultaneously, addressing the volatility of wind speed time series and multi-output complexity. To solve the optimization problem, an improved non-dominated sorting genetic algorithm (CKNSGA-III) is proposed, integrating Henon chaotic mapping and a knee-oriented mechanism to boost optimization efficiency and accuracy. Experimental results show that, compared with existing methods, the proposed prediction system has significant advantages in interval prediction performance, point prediction accuracy, and runtime, and has passed significance and robustness tests. |
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| AbstractList | •The first three-output neural network for wind speed point-interval forecast.•A many-objective optimization problem with coverage constraint is developed.•The proposed CKNSGA-III perfectly solves many-objective optimization problem.•The theoretical proof of the Pareto front existence in CKNSGA-III is given.
Neural networks play a key role in wind speed deterministic and uncertainty analysis, significantly improving wind energy utilization efficiency and reducing power system costs. However, existing studies often rely on complex neural architectures, leading to excessive computational time, and fail to integrate point and interval predictions, disconnecting deterministic and uncertainty analysis, thereby affecting prediction efficiency. To address these issues, this paper presents a novel three-output wind speed prediction system via constrained multi-objective optimization. The framework minimizes mean squared error (MSE), mean absolute error (MAE), and prediction interval normalized average width (PINAW) while maximizing prediction interval coverage (PICP), with adjustable coverage constraints for diverse interval demands. Leveraging the outlier-robust extreme learning machine (ORELM) as the predictor, the system outputs point values and interval bounds simultaneously, addressing the volatility of wind speed time series and multi-output complexity. To solve the optimization problem, an improved non-dominated sorting genetic algorithm (CKNSGA-III) is proposed, integrating Henon chaotic mapping and a knee-oriented mechanism to boost optimization efficiency and accuracy. Experimental results show that, compared with existing methods, the proposed prediction system has significant advantages in interval prediction performance, point prediction accuracy, and runtime, and has passed significance and robustness tests. |
| ArticleNumber | 122531 |
| Author | Lv, Mengzheng Gao, Jialu Wang, Jianzhou Wang, Kang Wang, Shuai Zhao, Yang |
| Author_xml | – sequence: 1 givenname: Mengzheng surname: Lv fullname: Lv, Mengzheng organization: School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China – sequence: 2 givenname: Jianzhou surname: Wang fullname: Wang, Jianzhou email: wangjianzhou@lzu.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 3 givenname: Shuai surname: Wang fullname: Wang, Shuai organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 4 givenname: Kang surname: Wang fullname: Wang, Kang organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 5 givenname: Yang surname: Zhao fullname: Zhao, Yang organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 6 givenname: Jialu orcidid: 0000-0002-6033-7532 surname: Gao fullname: Gao, Jialu organization: Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China |
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| Cites_doi | 10.1016/j.ins.2024.120549 10.1109/TNNLS.2013.2276053 10.1007/s10462-023-10470-y 10.1109/TSTE.2019.2907699 10.1109/TSMC.2024.3352665 10.1023/A:1022699029236 10.2307/1912100 10.1109/TIA.2023.3325798 10.1016/j.asoc.2019.105506 10.1109/TCYB.2021.3125071 10.1016/j.swevo.2019.05.011 10.1016/j.jmsy.2022.08.014 10.1016/j.energy.2023.129079 10.1007/BF00344251 10.1109/72.97934 10.1109/TEVC.2022.3144880 10.1109/TEVC.2014.2378512 10.1109/TEVC.2016.2564158 10.1038/323533a0 10.1016/j.ijforecast.2015.12.003 10.1109/TSTE.2019.2926147 10.1016/S0169-2070(96)00719-4 10.1109/TNN.2010.2096824 10.1109/IJCNN.2004.1380068 10.1016/j.renene.2025.122653 10.1109/4235.996017 10.1016/j.energy.2025.135210 10.1162/neco.1997.9.8.1735 10.2307/2532360 10.1109/TEVC.2013.2281535 10.1016/j.enconman.2024.118909 10.1109/TSTE.2017.2774195 10.1080/07350015.1995.10524599 |
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| Keywords | Three-output neural network Constrained many-objective optimization problem Advanced optimization algorithm Interval prediction Wind speed point prediction |
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| SubjectTerms | Advanced optimization algorithm Constrained many-objective optimization problem Interval prediction Three-output neural network Wind speed point prediction |
| Title | A multi-input and three-output wind speed point-interval prediction system based on constrained many-objective optimization problem |
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