Electrical load forecasting formulation by a fast neural network

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:29:01Z
dc.date.available2014-05-20T13:29:01Z
dc.date.created2014-05-20T13:29:01Z
dc.date.issued2003-03-01
dc.identifierEngineering Intelligent Systems For Electrical Engineering and Communications. Market Harboroug: C R L Publishing Ltd, v. 11, n. 1, p. 51-57, 2003.
dc.identifier0969-1170
dc.identifierhttp://hdl.handle.net/11449/9730
dc.identifierWOS:000183124000006
dc.identifier7166279400544764
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.
dc.languageeng
dc.publisherC R L Publishing Ltd
dc.relationEngineering Intelligent Systems For Electrical Engineering and Communications
dc.relation0,140
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectload forecasting
dc.subjectshort term
dc.subjectneural networks
dc.subjectbackpropagation
dc.subjectfuzzy logic
dc.titleElectrical load forecasting formulation by a fast neural network
dc.typeArtículos de revistas


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