{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:23:14Z","timestamp":1777706594339,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>\n                    Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity\n                    <jats:italic>O<\/jats:italic>\n                    \u00a0(\n                    <jats:italic>k<\/jats:italic>\n                    \u00a0*\u00a0\n                    <jats:italic>N<\/jats:italic>\n                    <jats:sup>3<\/jats:sup>\n                    ) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors.\n                  <\/jats:p>","DOI":"10.3233\/jifs-236239","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T10:33:18Z","timestamp":1712658798000},"page":"109-124","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting"],"prefix":"10.1177","volume":"49","author":[{"given":"Yuerong","family":"Li","sequence":"first","affiliation":[{"name":"School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China"},{"name":"Key Laboratory of Engineering Mathematics and Advanced Computing, Nanchang Institute of Technology, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China"},{"name":"Key Laboratory of Engineering Mathematics and Advanced Computing, Nanchang Institute of Technology, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxing","family":"Che","sequence":"additional","affiliation":[{"name":"School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China"},{"name":"Key Laboratory of Engineering Mathematics and Advanced Computing, Nanchang Institute of Technology, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"e_1_3_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2014.08.008"},{"issue":"5","key":"e_1_3_1_3_1","first-page":"583593","article-title":"Extended ARMA models for estimating price developments on day-ahead electricity markets","volume":"77","author":"Derk Swider J.","year":"2007","unstructured":"Derk SwiderJ.WeberC.Extended ARMA models for estimating price developments on day-ahead electricity markets, Electric Power Systems Research77(5-6) (2007), 583593.","journal-title":"Electric Power Systems Research"},{"key":"e_1_3_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2008.06.003"},{"key":"e_1_3_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2011.03.078"},{"key":"e_1_3_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2021.105110"},{"key":"e_1_3_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2013.11.031"},{"key":"e_1_3_1_8_1","first-page":"291305","article-title":"Electricity price forecasting by a hybrid model, combining wavelet transform","volume":"190","author":"Yang Z.","year":"2017","unstructured":"YangZ.CeL.LianL., Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and Kernel-Based Extreme Learning Machine Methods. 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