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dc.contributor.authorWu, Chih-Hungen_US
dc.contributor.authorTzeng, Gwo-Hshiungen_US
dc.contributor.authorLin, Rong-Hoen_US
dc.date.accessioned2014-12-08T15:09:41Z-
dc.date.available2014-12-08T15:09:41Z-
dc.date.issued2009-04-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2008.06.046en_US
dc.identifier.urihttp://hdl.handle.net/11536/7408-
dc.description.abstractThis study developed a novel model, HCA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model Outperforms the previous models. Specifically, the new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectSupport vector regression (SVR)en_US
dc.subjectHybrid genetic algorithm (HGA)en_US
dc.subjectParameter optimizationen_US
dc.subjectKernel function optimizationen_US
dc.subjectElectrical load forecastingen_US
dc.subjectForecasting accuracyen_US
dc.titleA Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regressionen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2008.06.046en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume36en_US
dc.citation.issue3en_US
dc.citation.spage4725en_US
dc.citation.epage4735en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000263584100069-
dc.citation.woscount55-
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