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...
Uloženo v:
| Vydáno v: | Information sciences Ročník 720; s. 122531 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.12.2025
|
| Témata: | |
| ISSN: | 0020-0255 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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. |
|---|---|
| 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 |
| BookMark | eNp9kMtqwzAQRbVIoUnaD-hOP2BXUvykqxD6gkA32QtZHtMxtmQkJSXd9serJF11kZWYyz1i5izIzFgDhDxwlnLGi8c-ReNTwUSeciHyFZ-ROWOCJTHJb8nC-54xlpVFMSc_azruh4AJmmkfqDItDZ8OILH7cAq-MCZ-AmjpZNGE2AvgDmqgk4MWdUBrqD_6ACNtlI-1OGtrfHAKTRxHZY6JbXqI1QNQOwUc8VuducnZZoDxjtx0avBw__cuye7lebd5S7Yfr--b9TbRoq5DwkEzoWtVt1nVaJXpTFcxKpRiDSubdlXlWZ1DrfKyq3Snu6yAqquU6EStWbtakvLyrXbWewed1BjOi5x2HSRn8qRP9jLqkyd98qIvkvwfOTkclTteZZ4uDMSLDghOeo1gdJTmogvZWrxC_wK-TJGO |
| CitedBy_id | crossref_primary_10_1016_j_asoc_2025_113829 |
| 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 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier Inc. |
| Copyright_xml | – notice: 2025 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2025.122531 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| ExternalDocumentID | 10_1016_j_ins_2025_122531 S0020025525006632 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 77I 8P~ 9JN 9JO AAAKF AAAKG AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AATTM AAXKI AAXUO AAYFN AAYWO ABAOU ABBOA ABEFU ABFNM ABJNI ABMAC ABUCO ABWVN ABXDB ACDAQ ACGFS ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADGUI ADJOM ADMUD ADNMO ADTZH ADVLN AEBSH AECPX AEIPS AEKER AENEX AEUPX AFFNX AFJKZ AFPUW AFTJW AGHFR AGQPQ AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGII AIGVJ AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APLSM APXCP ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ UHS WH7 WUQ XPP YYP ZMT ZY4 ~02 ~G- ~HD 9DU AAYXX CITATION |
| ID | FETCH-LOGICAL-c299t-1ec02c9a9d48bca4c4c8ec06aa0b07bd385495e9a57f8cfcf46e8f8a2f29c0d3 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001544346000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Sat Nov 29 07:34:39 EST 2025 Tue Nov 18 22:19:47 EST 2025 Sat Oct 04 17:01:56 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Three-output neural network Constrained many-objective optimization problem Advanced optimization algorithm Interval prediction Wind speed point prediction |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c299t-1ec02c9a9d48bca4c4c8ec06aa0b07bd385495e9a57f8cfcf46e8f8a2f29c0d3 |
| ORCID | 0000-0002-6033-7532 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ins_2025_122531 crossref_primary_10_1016_j_ins_2025_122531 elsevier_sciencedirect_doi_10_1016_j_ins_2025_122531 |
| PublicationCentury | 2000 |
| PublicationDate | December 2025 2025-12-00 |
| PublicationDateYYYYMMDD | 2025-12-01 |
| PublicationDate_xml | – month: 12 year: 2025 text: December 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2025 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Heskes (b0020) 1997 Zhu, He, Gao (b0105) 2023; 283 Deb, Jain (b0135) 2014; 18 Harvey, Leybourne, Newbold (b0235) 1997; 13 Zhang, Luo (b0070) 2015 Hochreiter, Schmidhuber (b0195) 1997 G. Bin Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, in: IEEE International Conference on Neural Networks - Conference Proceedings, 2004. doi Shi, Liang, Dinavahi (b0040) 2018 Li, Tang, Xue, Saeed, Hu (b0095) 2020 Rajwar, Deep, Das (b0120) 2023 Wang, Tang, Wen, Ma (b0065) 2019 Rao, Box, Jenkins (b0220) 1972 GWEC, Global Wind Report 2024, Global Wind Energy Council, 2024. www.gwec.net. Kim, Kim (b0110) 2016; 32 Zhang, Zhao, Pan, Zhang (b0080) 2020 Rumelhart, Hinton, Williams (b0185) 1986 Elman (b0210) 1991 Wang, Jiang, Shu, He (b0075) 2025; 320 Wang, Zhang, Siarry, Liu, Królczyk, Hua, Brumercik, Li (b0155) 2024 Specht (b0205) 1991 Phan, Wu, Phan (b0035) 2024 Wang, Lv, Li, Zeng (b0175) 2023 Odell, Anderson, D’Agostino (b0015) 1992 Zhang, Tian, Jin (b0170) 2015; 19 Jaeger (b0190) 2002 Chiu, Yen, Juan (b0165) 2016 Li, Zhang, Wang, Wang, Ishibuchi (b0030) 2022 Liu, Wang, Niu, Ji, Gu (b0090) 2024 Coello Coello, Pulido, Lechuga (b0130) 2004 Cui, Chang, Zhang, Cai, Zhang (b0140) 2019 Nix, Weigend (b0010) 1994 Lv, Wang, Wang, Gao, Guo (b0145) 2024; 670 Chung, Gulcehre, Cho, Bengio (b0200) 2015, 2015. He, Zhu, Wang (b0050) 2024; 54 K. Deb, Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy, 2001. Deb, Pratap, Agarwal, Meyarivan (b0125) 2002 Diebold, Mariano (b0230) 1995; 13 . Yang, Hao, Hao (b0100) 2023 Yu, Ma, Jin, Du, Liu, Zhang (b0150) 2022 Lv, Wang, Wang, Zhao, Gao, Wang (b0045) 2024; 319 Wang, Shu, Xu (b0085) 2025; 244 Yu, Jin, Olhofer, Liu, Du (b0160) 2023 Khosravi, Nahavandi, Creighton, Atiya (b0025) 2011; 22 Peng, Cheng, Zhang, Shao, Wang, Shen (b0060) 2022 Quan, Srinivasan, Khosravi (b0115) 2014; 25 Zhou, Wang, Guo, Watada (b0180) 2021 Fukushima (b0215) 1980 Zhang (10.1016/j.ins.2025.122531_b0080) 2020 Deb (10.1016/j.ins.2025.122531_b0125) 2002 Lv (10.1016/j.ins.2025.122531_b0045) 2024; 319 10.1016/j.ins.2025.122531_b0055 Zhou (10.1016/j.ins.2025.122531_b0180) 2021 Heskes (10.1016/j.ins.2025.122531_b0020) 1997 Phan (10.1016/j.ins.2025.122531_b0035) 2024 Zhang (10.1016/j.ins.2025.122531_b0170) 2015; 19 Lv (10.1016/j.ins.2025.122531_b0145) 2024; 670 Wang (10.1016/j.ins.2025.122531_b0175) 2023 Diebold (10.1016/j.ins.2025.122531_b0230) 1995; 13 Wang (10.1016/j.ins.2025.122531_b0155) 2024 Chiu (10.1016/j.ins.2025.122531_b0165) 2016 Wang (10.1016/j.ins.2025.122531_b0065) 2019 Rao (10.1016/j.ins.2025.122531_b0220) 1972 Peng (10.1016/j.ins.2025.122531_b0060) 2022 Elman (10.1016/j.ins.2025.122531_b0210) 1991 Cui (10.1016/j.ins.2025.122531_b0140) 2019 Yu (10.1016/j.ins.2025.122531_b0160) 2023 Specht (10.1016/j.ins.2025.122531_b0205) 1991 Nix (10.1016/j.ins.2025.122531_b0010) 1994 Yu (10.1016/j.ins.2025.122531_b0150) 2022 10.1016/j.ins.2025.122531_b0005 10.1016/j.ins.2025.122531_b0225 Khosravi (10.1016/j.ins.2025.122531_b0025) 2011; 22 Coello Coello (10.1016/j.ins.2025.122531_b0130) 2004 Kim (10.1016/j.ins.2025.122531_b0110) 2016; 32 Wang (10.1016/j.ins.2025.122531_b0085) 2025; 244 Li (10.1016/j.ins.2025.122531_b0095) 2020 Shi (10.1016/j.ins.2025.122531_b0040) 2018 Rajwar (10.1016/j.ins.2025.122531_b0120) 2023 Odell (10.1016/j.ins.2025.122531_b0015) 1992 Liu (10.1016/j.ins.2025.122531_b0090) 2024 Harvey (10.1016/j.ins.2025.122531_b0235) 1997; 13 Chung (10.1016/j.ins.2025.122531_b0200) 2015 Li (10.1016/j.ins.2025.122531_b0030) 2022 Yang (10.1016/j.ins.2025.122531_b0100) 2023 Jaeger (10.1016/j.ins.2025.122531_b0190) 2002 Zhang (10.1016/j.ins.2025.122531_b0070) 2015 Zhu (10.1016/j.ins.2025.122531_b0105) 2023; 283 Wang (10.1016/j.ins.2025.122531_b0075) 2025; 320 Rumelhart (10.1016/j.ins.2025.122531_b0185) 1986 Fukushima (10.1016/j.ins.2025.122531_b0215) 1980 Quan (10.1016/j.ins.2025.122531_b0115) 2014; 25 He (10.1016/j.ins.2025.122531_b0050) 2024; 54 Hochreiter (10.1016/j.ins.2025.122531_b0195) 1997 Deb (10.1016/j.ins.2025.122531_b0135) 2014; 18 |
| References_xml | – year: 2016 ident: b0165 article-title: Minimum Manhattan distance approach to multiple criteria decision making in Multiobjective optimization problems publication-title: IEEE Trans. Evol. Comput. – year: 1994 ident: b0010 article-title: Estimating the mean and variance of the target probability distribution publication-title: IEEE International Conference on Neural Networks - Conference Proceedings – year: 2023 ident: b0160 article-title: Solution set augmentation for knee identification in multiobjective decision analysis publication-title: IEEE Trans. Cybern. – year: 1997 ident: b0195 article-title: Long short-term memory publication-title: Neural Comput. – year: 2015, 2015. ident: b0200 article-title: Gated feedback recurrent neural networks publication-title: 32nd International Conference on Machine Learning – year: 1991 ident: b0205 article-title: A general regression neural network publication-title: IEEE Trans. Neural Netw. – year: 2018 ident: b0040 article-title: Direct interval forecast of uncertain wind power based on recurrent neural networks publication-title: IEEE Trans. Sustainable Energy – year: 1980 ident: b0215 article-title: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position publication-title: Biol. Cybern. – volume: 13 start-page: 253 year: 1995 end-page: 263 ident: b0230 article-title: Comparing predictive accuracy publication-title: J. Bus. Econ. Stat. – year: 2002 ident: b0190 article-title: Adaptive Nonlinear System Identification with Echo State Networks publication-title: NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems – volume: 54 start-page: 3069 year: 2024 end-page: 3083 ident: b0050 article-title: A novel neural network-based multiobjective evolution lower upper bound estimation method for electricity load interval forecast publication-title: IEEE Trans. Sys., Man, Cybernetics: Syst – year: 2024 ident: b0090 article-title: A point-interval wind speed forecasting system based on fuzzy theory and neural networks architecture searching strategy publication-title: Eng. Appl. Artif. Intel. – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: b0135 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach , part i : solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. – year: 1972 ident: b0220 article-title: Time series analysis forecasting and control publication-title: Econometrica – reference: GWEC, Global Wind Report 2024, Global Wind Energy Council, 2024. www.gwec.net. – volume: 22 start-page: 337 year: 2011 end-page: 346 ident: b0025 article-title: Lower upper bound estimation method for construction of neural network-based prediction intervals publication-title: IEEE Trans. Neural Netw. – year: 2019 ident: b0065 article-title: A hybrid intelligent approach for constructing landslide displacement prediction intervals publication-title: Applied Soft Computing Journal. – reference: G. Bin Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, in: IEEE International Conference on Neural Networks - Conference Proceedings, 2004. doi: – volume: 319 year: 2024 ident: b0045 article-title: A multi-input and dual-output wind speed interval forecasting system based on constrained multi-objective optimization problem and model averaging publication-title: Energ. Conver. Manage. – year: 1986 ident: b0185 article-title: Learning representations by back-propagating errors publication-title: Nature – year: 2023 ident: b0100 article-title: Innovative ensemble system based on mixed frequency modeling for wind speed point and interval forecasting publication-title: Inf. Sci. – year: 2022 ident: b0030 article-title: An evolutionary multiobjective knee-based lower upper bound estimation method for wind speed interval forecast publication-title: IEEE Trans. Evol. Comput. – year: 2019 ident: b0140 article-title: Improved NSGA-III with selection-and-elimination operator publication-title: Swarm Evol. Comput. – year: 2022 ident: b0060 article-title: Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method publication-title: J. Manuf. Syst. – volume: 25 start-page: 303 year: 2014 end-page: 315 ident: b0115 article-title: Short-term load and wind power forecasting using neural network-based prediction intervals publication-title: IEEE Trans. Neural Networks Learn. Syst. – year: 2015 ident: b0070 article-title: Outlier-robust extreme learning machine for regression problems publication-title: Neurocomputing – volume: 320 year: 2025 ident: b0075 article-title: A multi-factor clustering integration paradigm for wind speed point-interval prediction based on feature selection and optimized inverted transformer publication-title: Energy – volume: 19 start-page: 761 year: 2015 end-page: 776 ident: b0170 article-title: A knee point-driven evolutionary algorithm for many-objective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 32 start-page: 669 year: 2016 end-page: 679 ident: b0110 article-title: A new metric of absolute percentage error for intermittent demand forecasts publication-title: Int. J. Forecast. – year: 2022 ident: b0150 article-title: A survey on knee-oriented multiobjective evolutionary optimization publication-title: IEEE Trans. Evol. Comput. – year: 2021 ident: b0180 article-title: Multi-objective prediction intervals for wind power forecast based on deep neural networks publication-title: Inf. Sci. – year: 1997 ident: b0020 article-title: Practical confidence and prediction intervals, in publication-title: Adv. Neural Inf. Proces. Syst. – year: 2023 ident: b0120 article-title: An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges publication-title: Artif. Intell. Rev. – reference: K. Deb, Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy, 2001. – volume: 670 year: 2024 ident: b0145 article-title: Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III publication-title: Inf. Sci. – year: 2002 ident: b0125 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – year: 2023 ident: b0175 article-title: Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm publication-title: Expert Syst. Appl. – volume: 283 year: 2023 ident: b0105 article-title: Wind power interval and point prediction model using neural network based multi-objective optimization publication-title: Energy – reference: . – year: 1991 ident: b0210 article-title: Distributed representations, simple recurrent networks, and grammatical structure publication-title: Mach. Learn. – year: 2024 ident: b0155 article-title: A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy publication-title: Expert Syst. Appl. – year: 2024 ident: b0035 article-title: Enhancing one-day-ahead probabilistic solar power forecast with a hybrid transformer-LUBE model and missing data imputation publication-title: IEEE Trans. Ind. Appl. – year: 2020 ident: b0080 article-title: Wind speed interval prediction based on lorenz disturbance distribution publication-title: IEEE Trans. Sustainable Energy – year: 2004 ident: b0130 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans. Evol. Comput. – year: 1992 ident: b0015 article-title: Maximum likelihood estimation for interval-censored data using a weibull- based accelerated failure time model publication-title: Biometrics – volume: 244 year: 2025 ident: b0085 article-title: A point-interval wind speed prediction model based on entropy clustering and hybrid optimization weighted strategy publication-title: Renew. Energy – year: 2020 ident: b0095 article-title: Short-Term Wind speed Interval Prediction based on Ensemble GRU Model publication-title: IEEE Trans. Sustainable Energy – volume: 13 start-page: 281 year: 1997 end-page: 291 ident: b0235 article-title: Testing the equality of prediction mean squared errors publication-title: Int. J. Forecast. – volume: 670 year: 2024 ident: 10.1016/j.ins.2025.122531_b0145 article-title: Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III publication-title: Inf. Sci. doi: 10.1016/j.ins.2024.120549 – year: 2023 ident: 10.1016/j.ins.2025.122531_b0175 article-title: Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm publication-title: Expert Syst. Appl. – volume: 25 start-page: 303 year: 2014 ident: 10.1016/j.ins.2025.122531_b0115 article-title: Short-term load and wind power forecasting using neural network-based prediction intervals publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2013.2276053 – year: 2015 ident: 10.1016/j.ins.2025.122531_b0070 article-title: Outlier-robust extreme learning machine for regression problems publication-title: Neurocomputing – year: 2002 ident: 10.1016/j.ins.2025.122531_b0190 article-title: Adaptive Nonlinear System Identification with Echo State Networks – year: 2023 ident: 10.1016/j.ins.2025.122531_b0120 article-title: An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-023-10470-y – year: 2020 ident: 10.1016/j.ins.2025.122531_b0080 article-title: Wind speed interval prediction based on lorenz disturbance distribution publication-title: IEEE Trans. Sustainable Energy doi: 10.1109/TSTE.2019.2907699 – volume: 54 start-page: 3069 year: 2024 ident: 10.1016/j.ins.2025.122531_b0050 article-title: A novel neural network-based multiobjective evolution lower upper bound estimation method for electricity load interval forecast publication-title: IEEE Trans. Sys., Man, Cybernetics: Syst doi: 10.1109/TSMC.2024.3352665 – year: 1991 ident: 10.1016/j.ins.2025.122531_b0210 article-title: Distributed representations, simple recurrent networks, and grammatical structure publication-title: Mach. Learn. doi: 10.1023/A:1022699029236 – year: 1972 ident: 10.1016/j.ins.2025.122531_b0220 article-title: Time series analysis forecasting and control publication-title: Econometrica doi: 10.2307/1912100 – year: 2024 ident: 10.1016/j.ins.2025.122531_b0035 article-title: Enhancing one-day-ahead probabilistic solar power forecast with a hybrid transformer-LUBE model and missing data imputation publication-title: IEEE Trans. Ind. Appl. doi: 10.1109/TIA.2023.3325798 – year: 2019 ident: 10.1016/j.ins.2025.122531_b0065 article-title: A hybrid intelligent approach for constructing landslide displacement prediction intervals publication-title: Applied Soft Computing Journal. doi: 10.1016/j.asoc.2019.105506 – year: 2024 ident: 10.1016/j.ins.2025.122531_b0090 article-title: A point-interval wind speed forecasting system based on fuzzy theory and neural networks architecture searching strategy publication-title: Eng. Appl. Artif. Intel. – year: 2023 ident: 10.1016/j.ins.2025.122531_b0160 article-title: Solution set augmentation for knee identification in multiobjective decision analysis publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2021.3125071 – year: 2019 ident: 10.1016/j.ins.2025.122531_b0140 article-title: Improved NSGA-III with selection-and-elimination operator publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2019.05.011 – year: 2022 ident: 10.1016/j.ins.2025.122531_b0060 article-title: Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2022.08.014 – volume: 283 year: 2023 ident: 10.1016/j.ins.2025.122531_b0105 article-title: Wind power interval and point prediction model using neural network based multi-objective optimization publication-title: Energy doi: 10.1016/j.energy.2023.129079 – year: 1980 ident: 10.1016/j.ins.2025.122531_b0215 article-title: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position publication-title: Biol. Cybern. doi: 10.1007/BF00344251 – year: 2023 ident: 10.1016/j.ins.2025.122531_b0100 article-title: Innovative ensemble system based on mixed frequency modeling for wind speed point and interval forecasting publication-title: Inf. Sci. – year: 1991 ident: 10.1016/j.ins.2025.122531_b0205 article-title: A general regression neural network publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.97934 – year: 2022 ident: 10.1016/j.ins.2025.122531_b0150 article-title: A survey on knee-oriented multiobjective evolutionary optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2022.3144880 – year: 2022 ident: 10.1016/j.ins.2025.122531_b0030 article-title: An evolutionary multiobjective knee-based lower upper bound estimation method for wind speed interval forecast publication-title: IEEE Trans. Evol. Comput. – year: 2004 ident: 10.1016/j.ins.2025.122531_b0130 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans. Evol. Comput. – volume: 19 start-page: 761 year: 2015 ident: 10.1016/j.ins.2025.122531_b0170 article-title: A knee point-driven evolutionary algorithm for many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2014.2378512 – year: 2016 ident: 10.1016/j.ins.2025.122531_b0165 article-title: Minimum Manhattan distance approach to multiple criteria decision making in Multiobjective optimization problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2016.2564158 – year: 2021 ident: 10.1016/j.ins.2025.122531_b0180 article-title: Multi-objective prediction intervals for wind power forecast based on deep neural networks publication-title: Inf. Sci. – year: 1997 ident: 10.1016/j.ins.2025.122531_b0020 article-title: Practical confidence and prediction intervals, in publication-title: Adv. Neural Inf. Proces. Syst. – year: 1986 ident: 10.1016/j.ins.2025.122531_b0185 article-title: Learning representations by back-propagating errors publication-title: Nature doi: 10.1038/323533a0 – ident: 10.1016/j.ins.2025.122531_b0225 – volume: 32 start-page: 669 year: 2016 ident: 10.1016/j.ins.2025.122531_b0110 article-title: A new metric of absolute percentage error for intermittent demand forecasts publication-title: Int. J. Forecast. doi: 10.1016/j.ijforecast.2015.12.003 – year: 2020 ident: 10.1016/j.ins.2025.122531_b0095 article-title: Short-Term Wind speed Interval Prediction based on Ensemble GRU Model publication-title: IEEE Trans. Sustainable Energy doi: 10.1109/TSTE.2019.2926147 – volume: 13 start-page: 281 year: 1997 ident: 10.1016/j.ins.2025.122531_b0235 article-title: Testing the equality of prediction mean squared errors publication-title: Int. J. Forecast. doi: 10.1016/S0169-2070(96)00719-4 – volume: 22 start-page: 337 year: 2011 ident: 10.1016/j.ins.2025.122531_b0025 article-title: Lower upper bound estimation method for construction of neural network-based prediction intervals publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2010.2096824 – ident: 10.1016/j.ins.2025.122531_b0005 – year: 1994 ident: 10.1016/j.ins.2025.122531_b0010 article-title: Estimating the mean and variance of the target probability distribution publication-title: IEEE International Conference on Neural Networks - Conference Proceedings – ident: 10.1016/j.ins.2025.122531_b0055 doi: 10.1109/IJCNN.2004.1380068 – volume: 244 year: 2025 ident: 10.1016/j.ins.2025.122531_b0085 article-title: A point-interval wind speed prediction model based on entropy clustering and hybrid optimization weighted strategy publication-title: Renew. Energy doi: 10.1016/j.renene.2025.122653 – year: 2002 ident: 10.1016/j.ins.2025.122531_b0125 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 320 year: 2025 ident: 10.1016/j.ins.2025.122531_b0075 article-title: A multi-factor clustering integration paradigm for wind speed point-interval prediction based on feature selection and optimized inverted transformer publication-title: Energy doi: 10.1016/j.energy.2025.135210 – year: 1997 ident: 10.1016/j.ins.2025.122531_b0195 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – year: 2024 ident: 10.1016/j.ins.2025.122531_b0155 article-title: A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy publication-title: Expert Syst. Appl. – year: 1992 ident: 10.1016/j.ins.2025.122531_b0015 article-title: Maximum likelihood estimation for interval-censored data using a weibull- based accelerated failure time model publication-title: Biometrics doi: 10.2307/2532360 – volume: 18 start-page: 577 year: 2014 ident: 10.1016/j.ins.2025.122531_b0135 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach , part i : solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2281535 – volume: 319 year: 2024 ident: 10.1016/j.ins.2025.122531_b0045 article-title: A multi-input and dual-output wind speed interval forecasting system based on constrained multi-objective optimization problem and model averaging publication-title: Energ. Conver. Manage. doi: 10.1016/j.enconman.2024.118909 – year: 2018 ident: 10.1016/j.ins.2025.122531_b0040 article-title: Direct interval forecast of uncertain wind power based on recurrent neural networks publication-title: IEEE Trans. Sustainable Energy doi: 10.1109/TSTE.2017.2774195 – year: 2015 ident: 10.1016/j.ins.2025.122531_b0200 article-title: Gated feedback recurrent neural networks – volume: 13 start-page: 253 year: 1995 ident: 10.1016/j.ins.2025.122531_b0230 article-title: Comparing predictive accuracy publication-title: J. Bus. Econ. Stat. doi: 10.1080/07350015.1995.10524599 |
| SSID | ssj0004766 |
| Score | 2.4893172 |
| Snippet | •The first three-output neural network for wind speed point-interval forecast.•A many-objective optimization problem with coverage constraint is developed.•The... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 122531 |
| 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 |
| URI | https://dx.doi.org/10.1016/j.ins.2025.122531 |
| Volume | 720 |
| WOSCitedRecordID | wos001544346000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0004766 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELaqLgc4IFhALLuLfEAcqIzycBr7WKFdLQ-tkOihtyh2HJpqN4lKUla97h_f8SNtWh4CJC5RM6rdqPNl5ht7ZozQq4x6EkgbIyoIFKHgkokYC0rUWEZZ6GdcKGEOm4gvL9lsxj8PBuuuFmZ1FZclu7nh9X9VNchA2bp09i_UvZkUBPAZlA5XUDtc_0jxE5skSIqybhuXILlUilRtowXfC71SXoPTGtVVUTakMFmPuiBrqTdtDB5sf-eRdnGZ3k6QmkXqwyTg9hrMB6nEwlrKUQU259oVc47c8TR9xuvqney01pBsaPynlVmPVeXX9Vw5F2oW912aMEB3Pa_affmXeZsW-8KPqZvALWEE0V46yKa2Zif1UxNZoiOevq2OTeXcj3bfLkEsIFjRLdiD6K0Pdsq5l9122np32gRSwP003QL3fRDEEWdDdDB5fzb7sK2qje1Od_cc3Z64yQ7c-6Gfs5oeU5k-Qg9diIEnFhqP0UCVh-hBr_HkITp15Sr4Ne7pBztD_wTdTnAPRBhAhPsgwhpE2IAI74IIb0GELYiwARGG-x6I8C6IcB9E2IHoKZqen03fXRB3XAeRwGka4ivpBZKnPKNMyJRKKhmIxmnqCS8WWcgiiMYVT6M4ZzKXOR0rlrM0yAMuvSx8hoZlVarnCPM48qUKgermgsZhIDyqgDVFHvXzNMq8I-R1_3YiXSt7_fxXSZezuEhAQYlWUGIVdITebIbUto_L775MOxUm7s2wBDMBvP162It_G3aM7m9fihM0bJatOkX35Kopvi1fOlTeATBdtLM |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+multi-input+and+three-output+wind+speed+point-interval+prediction+system+based+on+constrained+many-objective+optimization+problem&rft.jtitle=Information+sciences&rft.au=Lv%2C+Mengzheng&rft.au=Wang%2C+Jianzhou&rft.au=Wang%2C+Shuai&rft.au=Wang%2C+Kang&rft.date=2025-12-01&rft.pub=Elsevier+Inc&rft.issn=0020-0255&rft.volume=720&rft_id=info:doi/10.1016%2Fj.ins.2025.122531&rft.externalDocID=S0020025525006632 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |