Electrical load forecasting formulation by a fast neural network

Página do item simplificado
dc.contributor.authorLopes, Mara Lúcia M. [UNESP]
dc.contributor.authorMinussi, Carlos R. [UNESP]
dc.contributor.authorLotufo, Anna Diva P. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T19:55:54Z
dc.date.available2022-04-28T19:55:54Z
dc.date.issued2003-03-01
dc.description.abstractThe objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.en
dc.description.affiliationDepartamento de Engenharia Eletrica Universidade Estadual Paulista UNESP, Ilha Solteria, SP
dc.description.affiliationUnespDepartamento de Engenharia Eletrica Universidade Estadual Paulista UNESP, Ilha Solteria, SP
dc.format.extent51-57
dc.identifier.citationInternational Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, v. 11, n. 1, p. 51-57, 2003.
dc.identifier.issn1472-8915
dc.identifier.scopus2-s2.0-0038038897
dc.identifier.urihttp://hdl.handle.net/11449/224313
dc.language.isoeng
dc.relation.ispartofInternational Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
dc.sourceScopus
dc.subjectBackpropagation
dc.subjectFuzzy logic
dc.subjectLoad forecasting
dc.subjectNeural networks
dc.subjectShort term
dc.titleElectrical load forecasting formulation by a fast neural networken
dc.typeArtigo

Arquivos

Coleções

Artigos - Engenharia Elétrica - FEIS