Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1407
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLing, SH-
dc.creatorLeung, FHF-
dc.creatorLam, HK-
dc.creatorTam, PKS-
dc.date.accessioned2014-12-11T08:26:24Z-
dc.date.available2014-12-11T08:26:24Z-
dc.identifier.isbn0-7803-7278-6-
dc.identifier.urihttp://hdl.handle.net/10397/1407-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2002 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.subjectComputer simulationen_US
dc.subjectElectric load forecastingen_US
dc.subjectError analysisen_US
dc.subjectGenetic algorithmsen_US
dc.subjectIntelligent agentsen_US
dc.subjectNeural networksen_US
dc.subjectOptimizationen_US
dc.titleShort-term daily load forecasting in an intelligent home with GA-based neural networken_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformationAuthor name used in this publication: P. K. S. Tamen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractDaily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIJCNN'02 : proceedings of the 2002 International Joint Conference on Neural Networks : May 12-17, 2002, Honolulu, Hawaii, p. 997-1001-
dcterms.issued2002-
dc.identifier.isiWOS:000177402800178-
dc.identifier.scopus2-s2.0-0036073032-
dc.identifier.rosgroupidr08567-
dc.description.ros2001-2002 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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