{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:01:17Z","timestamp":1772042477004,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72201052"],"award-info":[{"award-number":["72201052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01730-7","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T09:30:26Z","timestamp":1735983026000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A quadratic $$\\nu $$-support vector regression approach for load forecasting"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8045-8438","authenticated-orcid":false,"given":"Yanhe","family":"Jia","sequence":"first","affiliation":[]},{"given":"Shuaiguang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yiwen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fengming","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Zheming","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"1730_CR1","unstructured":"The ISO New England load data set. https:\/\/www.iso-ne.com\/isoexpress\/web\/reports\/pricing\/-\/tree\/zone-info"},{"key":"1730_CR2","doi-asserted-by":"crossref","unstructured":"Basu Anjan, Paliwal KK (1989) Robust m-estimates and generalized m-estimates for autoregressive parameter estimation. In Fourth IEEE Region 10 International Conference TENCON, pages 355\u2013358. IEEE","DOI":"10.1109\/TENCON.1989.176958"},{"issue":"8","key":"1730_CR3","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1162\/089976602760128081","volume":"14","author":"C-C Chang","year":"2002","unstructured":"Chang C-C, Lin C-J (2002) Training v-support vector regression: theory and algorithms. Neural Comput 14(8):1959\u20131977","journal-title":"Neural Comput"},{"issue":"4","key":"1730_CR4","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1109\/TPWRS.2004.835679","volume":"19","author":"B-J Chen","year":"2004","unstructured":"Chen B-J, Chang M-W et al (2004) Load forecasting using support vector machines: A study on eunite competition 2001. IEEE Trans Power Syst 19(4):1821\u20131830","journal-title":"IEEE Trans Power Syst"},{"issue":"1","key":"1730_CR5","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/S0893-6080(03)00169-2","volume":"17","author":"V Cherkassky","year":"2004","unstructured":"Cherkassky V, Ma Y (2004) Practical selection of svm parameters and noise estimation for svm regression. Neural Netw 17(1):113\u2013126","journal-title":"Neural Netw"},{"issue":"3","key":"1730_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"1","key":"1730_CR7","first-page":"1","volume":"1","author":"J Deng","year":"1989","unstructured":"Deng J (1989) Introduction to grey system theory. Journal of Grey System 1(1):1\u201324","journal-title":"Journal of Grey System"},{"key":"1730_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.epsr.2015.09.001","volume":"130","author":"G Dudek","year":"2016","unstructured":"Dudek G (2016) Pattern-based local linear regression models for short-term load forecasting. Electric Power Systems Research 130:139\u2013147","journal-title":"Electric Power Systems Research"},{"key":"1730_CR9","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1023\/A:1012474916001","volume":"46","author":"Gary William Flake and Steve Lawrence","year":"2002","unstructured":"Gary William Flake and Steve Lawrence (2002) Efficient svm regression training with smo. Mach Learn 46:271\u2013290","journal-title":"Mach Learn"},{"issue":"1","key":"1730_CR10","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.ejor.2020.10.040","volume":"290","author":"Z Gao","year":"2021","unstructured":"Gao Z, Fang S-C, Luo J, Medhin N (2021) A kernel-free double well potential support vector machine with applications. Eur J Oper Res 290(1):248\u2013262","journal-title":"Eur J Oper Res"},{"key":"1730_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109390","volume":"127","author":"Z Gao","year":"2022","unstructured":"Gao Z, Wang Y, Huang M, Luo J, Tang S (2022) A kernel-free fuzzy reduced quadratic surface $$\\nu $$-support vector machine with applications. Appl Soft Comput 127:109390","journal-title":"Appl Soft Comput"},{"issue":"7","key":"1730_CR12","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1016\/j.automatica.2005.02.002","volume":"41","author":"I Goethals","year":"2005","unstructured":"Goethals I, Pelckmans K, Suykens JAK, De Moor B (2005) Identification of mimo hammerstein models using least squares support vector machines. Automatica 41(7):1263\u20131272","journal-title":"Automatica"},{"issue":"3","key":"1730_CR13","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.ejor.2009.01.062","volume":"199","author":"H Hahn","year":"2009","unstructured":"Hahn H, Meyer-Nieberg S, Pickl S (2009) Electric load forecasting methods: Tools for decision making. Eur J Oper Res 199(3):902\u2013907","journal-title":"Eur J Oper Res"},{"key":"1730_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108877","volume":"122","author":"A Haque","year":"2022","unstructured":"Haque A, Rahman S (2022) Short-term electrical load forecasting through heuristic configuration of regularized deep neural network. Appl Soft Comput 122:108877","journal-title":"Appl Soft Comput"},{"issue":"1","key":"1730_CR15","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/59.910780","volume":"16","author":"HS Hippert","year":"2001","unstructured":"Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: A review and evaluation. IEEE Trans Power Syst 16(1):44\u201355","journal-title":"IEEE Trans Power Syst"},{"key":"1730_CR16","unstructured":"Hong Tao (2010) Short term electric load forecasting. North Carolina State University"},{"issue":"2","key":"1730_CR17","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.ijforecast.2013.07.001","volume":"30","author":"T Hong","year":"2014","unstructured":"Hong T, Pinson P, Fan S (2014) Global energy forecasting competition 2012. Int J Forecast 30(2):357\u2013363","journal-title":"Int J Forecast"},{"key":"1730_CR18","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10700-013-9166-9","volume":"13","author":"T Hong","year":"2014","unstructured":"Hong T, Wang P (2014) Fuzzy interaction regression for short term load forecasting. Fuzzy Optim Decis Making 13:91\u2013103","journal-title":"Fuzzy Optim Decis Making"},{"issue":"1","key":"1730_CR19","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TSG.2013.2274373","volume":"5","author":"T Hong","year":"2013","unstructured":"Hong T, Wilson J, Xie J (2013) Long term probabilistic load forecasting and normalization with hourly information. IEEE Transactions on Smart Grid 5(1):456\u2013462","journal-title":"IEEE Transactions on Smart Grid"},{"key":"1730_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00521-012-0969-3","volume":"22","author":"JNK Liu","year":"2013","unstructured":"Liu JNK, Yanxing H (2013) Application of feature-weighted support vector regression using grey correlation degree to stock price forecasting. Neural Comput Appl 22:143\u2013152","journal-title":"Neural Comput Appl"},{"key":"1730_CR21","unstructured":"Liu Sifeng, Lin Yi (2010) Grey Systems: Theory and Applications. Springer Verlag"},{"issue":"06","key":"1730_CR22","doi-asserted-by":"publisher","first-page":"1650046","DOI":"10.1142\/S0217595916500469","volume":"33","author":"J Luo","year":"2016","unstructured":"Luo J, Fang S-C, Deng Z, Guo X (2016) Soft quadratic surface support vector machine for binary classification. Asia-Pacific J Oper Res 33(06):1650046","journal-title":"Asia-Pacific J Oper Res"},{"issue":"5","key":"1730_CR23","doi-asserted-by":"publisher","first-page":"5397","DOI":"10.1109\/TSG.2018.2881562","volume":"10","author":"J Luo","year":"2018","unstructured":"Luo J, Hong T, Fang S-C (2018) Robust regression models for load forecasting. IEEE Transactions on Smart Grid 10(5):5397\u20135404","journal-title":"IEEE Transactions on Smart Grid"},{"key":"1730_CR24","doi-asserted-by":"crossref","unstructured":"Luo Jian, Hong Tao, Gao Zheming, Fang Shu-Cherng (2022) A robust support vector regression model for electric load forecasting. International Journal of Forecasting","DOI":"10.1016\/j.ijforecast.2022.04.001"},{"issue":"2","key":"1730_CR25","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s40565-017-0351-7","volume":"6","author":"J Luo","year":"2018","unstructured":"Luo J, Hong T, Yue M (2018) Real-time anomaly detection for very short-term load forecasting. Journal of Modern Power Systems and Clean Energy 6(2):235\u2013243","journal-title":"Journal of Modern Power Systems and Clean Energy"},{"key":"1730_CR26","doi-asserted-by":"crossref","unstructured":"Maldonado S, Gonzalez A, Crone S (2019) Automatic time series analysis for electric load forecasting via support vector regression. Appl Soft Comput 83:105616","DOI":"10.1016\/j.asoc.2019.105616"},{"issue":"9","key":"1730_CR27","doi-asserted-by":"publisher","first-page":"5567","DOI":"10.1007\/s00500-023-07860-3","volume":"27","author":"H Moosaei","year":"2023","unstructured":"Moosaei H, Mousavi A, Hlad\u00edk M, Gao Z (2023) Sparse l1-norm quadratic surface support vector machine with universum data. Soft Comput 27(9):5567\u20135586","journal-title":"Soft Comput"},{"key":"1730_CR28","doi-asserted-by":"crossref","unstructured":"Mousavi A, Gao Z, Han L, Lim A (2022) Quadratic surface support vector machine with L1 norm regularization. Journal of Industrial & Management Optimization 18(3):1835\u20131861","DOI":"10.3934\/jimo.2021046"},{"key":"1730_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115424","volume":"184","author":"I Ni\u00f1o-Adan","year":"2021","unstructured":"Ni\u00f1o-Adan I, Manjarres D, Landa-Torres I, Portillo E (2021) Feature weighting methods: A review. Expert Syst Appl 184:115424","journal-title":"Expert Syst Appl"},{"issue":"5","key":"1730_CR30","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Sch\u00f6lkopf","year":"2000","unstructured":"Sch\u00f6lkopf B (2000) Alex J Smola, Robert C Williamson, and Peter L Bartlett. New support vector algorithms. Neural Computation 12(5):1207\u20131245","journal-title":"New support vector algorithms. Neural Computation"},{"key":"1730_CR31","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.energy.2019.02.141","volume":"174","author":"P Singh","year":"2019","unstructured":"Singh P, Dwivedi P, Kant V (2019) A hybrid method based on neural network and improved environmental adaptation method using controlled gaussian mutation with real parameter for short-term load forecasting. Energy 174:460\u2013477","journal-title":"Energy"},{"key":"1730_CR32","doi-asserted-by":"crossref","unstructured":"Tziolis Georgios, Spanias Chrysovalantis, Theodoride Maria, Theocharides Spyros, Lopez-Lorente Javier, Livera Andreas, Makrides George, Georghiou George\u00a0E (2023) Short-term electric net load forecasting for solar-integrated distribution systems based on bayesian neural networks and statistical post-processing. Energy, page 127018","DOI":"10.1016\/j.energy.2023.127018"},{"key":"1730_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105548","volume":"82","author":"R Wang","year":"2019","unstructured":"Wang R, Wang J, Yunzhen X (2019) A novel combined model based on hybrid optimization algorithm for electrical load forecasting. Appl Soft Comput 82:105548","journal-title":"Appl Soft Comput"},{"key":"1730_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.119969","volume":"223","author":"W Jinran","year":"2021","unstructured":"Jinran W, Wang Y-G, Tian Y-C, Burrage K, Cao T (2021) Support vector regression with asymmetric loss for optimal electric load forecasting. Energy 223:119969","journal-title":"Energy"},{"key":"1730_CR35","doi-asserted-by":"crossref","unstructured":"Wu Mingyang, Yang Zhixia (2024) Kernel-free reduced quadratic surface support vector machine with 0-1 loss function and l p-norm regularization. Annals of Data Science, pages 1\u201332","DOI":"10.1007\/s40745-024-00573-w"},{"issue":"2","key":"1730_CR36","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1109\/TWC.2020.3029029","volume":"20","author":"W Zi-Yang","year":"2021","unstructured":"Zi-Yang W, Ismail M, Serpedin E, Wang J (2021) Efficient prediction of link outage in mobile optical wireless communications. IEEE Trans Wireless Commun 20(2):882\u2013896","journal-title":"IEEE Trans Wireless Commun"},{"key":"1730_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109928","volume":"257","author":"X Yan","year":"2022","unstructured":"Yan X, Zhu H (2022) A novel robust support vector machine classifier with feature mapping. Knowl-Based Syst 257:109928","journal-title":"Knowl-Based Syst"},{"key":"1730_CR38","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.energy.2016.07.092","volume":"113","author":"YL Yang","year":"2016","unstructured":"Yang YL, Che JX, Li YY, Zhao YJ, Zhu SL (2016) An incremental electric load forecasting model based on support vector regression. Energy 113:796\u2013808","journal-title":"Energy"},{"key":"1730_CR39","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.ins.2022.02.012","volume":"594","author":"J Ye","year":"2022","unstructured":"Ye J, Yang Z, Ma M, Wang Y, Yang X (2022) $$\\epsilon $$-kernel-free soft quadratic surface support vector regression. Inf Sci 594:177\u2013199","journal-title":"Inf Sci"},{"key":"1730_CR40","unstructured":"Zhou Zhi-Hua (2021) Machine Learning. Springer Singapore"},{"issue":"3","key":"1730_CR41","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.ijforecast.2016.01.001","volume":"32","author":"F Ziel","year":"2016","unstructured":"Ziel F, Liu B (2016) Lasso estimation for GEFCom2014 probabilistic electric load forecasting. Int J Forecast 32(3):1029\u20131037","journal-title":"Int J Forecast"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01730-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01730-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01730-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:22:22Z","timestamp":1738268542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01730-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1730"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01730-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"9 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"123"}}