A fast electric load forecasting using neural networks

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorLopes, Mara Lúcia M.
dc.creatorMinussi, Carlos R.
dc.creatorLotufo, Anna Diva P.
dc.date2014-05-27T11:19:59Z
dc.date2016-10-25T18:16:42Z
dc.date2014-05-27T11:19:59Z
dc.date2016-10-25T18:16:42Z
dc.date2000-12-01
dc.date.accessioned2017-04-06T00:58:14Z
dc.date.available2017-04-06T00:58:14Z
dc.identifierMidwest Symposium on Circuits and Systems, v. 2, p. 646-649.
dc.identifierhttp://hdl.handle.net/11449/66342
dc.identifierhttp://acervodigital.unesp.br/handle/11449/66342
dc.identifier10.1109/MWSCAS.2000.952840
dc.identifierWOS:000172099300150
dc.identifier2-s2.0-0034463498
dc.identifierhttp://dx.doi.org/10.1109/MWSCAS.2000.952840
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/887925
dc.descriptionThe objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.
dc.languageeng
dc.relationMidwest Symposium on Circuits and Systems
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBackpropagation
dc.subjectFuzzy control
dc.subjectFuzzy sets
dc.subjectGradient methods
dc.subjectKalman filtering
dc.subjectNeural networks
dc.subjectRegression analysis
dc.subjectBinary systems
dc.subjectLinear regression
dc.subjectElectric load forecasting
dc.titleA fast electric load forecasting using neural networks
dc.typeOtro


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