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Conference Paper: Load forecasting by fuzzy neural network in Box-Jenkins models

TitleLoad forecasting by fuzzy neural network in Box-Jenkins models
Authors
Issue Date1998
PublisherIEEE.
Citation
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Diego, CA, 11-14 October 1998, v. 2, p. 1738-1743 How to Cite?
AbstractIn this paper, the use of FNN to identify appropriate Box-Jenkins models for the electricity load forecasting in Hong Kong is presented. FNN is found to be suitable to identify the Box-Jenkin model. By such model, we can forecast the load accurately.
Persistent Identifierhttp://hdl.handle.net/10722/152523
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorTang, AWK-
dc.contributor.authorWong, MH-
dc.contributor.authorWong, YK-
dc.contributor.authorChung, TS-
dc.date.accessioned2012-07-03T08:57:39Z-
dc.date.available2012-07-03T08:57:39Z-
dc.date.issued1998-
dc.identifier.citationProceedings of the IEEE International Conference on Systems, Man and Cybernetics, San Diego, CA, 11-14 October 1998, v. 2, p. 1738-1743-
dc.identifier.isbn0-7803-4778-1-
dc.identifier.issn08843627-
dc.identifier.urihttp://hdl.handle.net/10722/152523-
dc.description.abstractIn this paper, the use of FNN to identify appropriate Box-Jenkins models for the electricity load forecasting in Hong Kong is presented. FNN is found to be suitable to identify the Box-Jenkin model. By such model, we can forecast the load accurately.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE International Conference on Systems, Man and Cybernetics-
dc.rightsCopyright © IEEE.-
dc.rights©1998 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.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleLoad forecasting by fuzzy neural network in Box-Jenkins modelsen_US
dc.typeConference_Paperen_US
dc.identifier.emailTang, AWK: wktang@eee.hku.hk-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICSMC.1998.728145-
dc.identifier.scopuseid_2-s2.0-0032318494-
dc.identifier.volume2-
dc.identifier.spage1738-
dc.identifier.epage1743-
dc.publisher.placeUnited States-

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