Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1658
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dc.contributorDepartment of Electrical Engineering-
dc.creatorYang, Z-
dc.creatorChe, Y-
dc.creatorCheng, KWE-
dc.date.accessioned2014-12-11T08:26:37Z-
dc.date.available2014-12-11T08:26:37Z-
dc.identifier.isbn1-4244-1298-6-
dc.identifier.urihttp://hdl.handle.net/10397/1658-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectLoad forecastingen_US
dc.subjectRBF neural networken_US
dc.subjectReal codingen_US
dc.subjectGenetic algorithmen_US
dc.subjectConvergence rateen_US
dc.titleGenetic algorithm-based RBF neural network load forecasting modelen_US
dc.typeConference Paperen_US
dcterms.abstractTo overcome the limitation of the traditional load forecasting method, a new load forecasting system basing on radial basis Gaussian kernel function (RBF) neural network is proposed in this paper. Genetic algorithm adopting the real coding, crossover probability and mutation probability was applied to optimize the parameters of the neural network, and a faster convergence rate was reached. Theoretical analysis and simulations prove that this load forecasting model is more practical and has more precision than the traditional one.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationPES 2007 : Power Engineering Society General Meeting, 2007, IEEE : 24-28 June, 2007, [p. 1-6]-
dcterms.issued2007-
dc.identifier.scopus2-s2.0-42649098915-
dc.relation.ispartofbookPES 2007 : Power Engineering Society General Meeting, 2007, IEEE : 24-28 June, 2007-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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