Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1409
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLing, SH-
dc.creatorLam, HK-
dc.creatorLeung, FHF-
dc.creatorTam, PKS-
dc.date.accessioned2014-12-11T08:26:23Z-
dc.date.available2014-12-11T08:26:23Z-
dc.identifier.isbn0-7803-7293-X-
dc.identifier.urihttp://hdl.handle.net/10397/1409-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2001 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.subjectDigital arithmeticen_US
dc.subjectElectric appliancesen_US
dc.subjectElectric load forecastingen_US
dc.subjectElectric power transmission networksen_US
dc.subjectGenetic algorithmsen_US
dc.subjectMembership functionsen_US
dc.subjectNeural networksen_US
dc.titleA neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent homeen_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.abstractIn this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for a short-term daily load forecasting in an intelligent home will be given.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationThe 10th IEEE International Conference on Fuzzy Systems : meeting the grand challenge : machines that serve people : The University of Melbourne, Australia, December, 2001, Sunday 2nd to Wednesday 5th, p. 1456-1459-
dcterms.issued2001-
dc.identifier.isiWOS:000178178300360-
dc.identifier.scopus2-s2.0-0036343506-
dc.relation.ispartofbookThe 10th IEEE International Conference on Fuzzy Systems : meeting the grand challenge : machines that serve people : The University of Melbourne, Australia, December, 2001, Sunday 2nd to Wednesday 5th-
dc.relation.conferenceIEEE International Conference on Fuzzy Systems [FUZZ]-
dc.identifier.rosgroupidr07073-
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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