Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network

dc.contributor.authorAbreu, Thays [UNESP]
dc.contributor.authorAmorim, Aline J. [UNESP]
dc.contributor.authorSantos-Junior, Carlos R.
dc.contributor.authorLotufo, Anna D.P. [UNESP]
dc.contributor.authorMinussi, Carlos R. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionScience and Technology of São Paulo
dc.date.accessioned2018-12-11T16:54:25Z
dc.date.available2018-12-11T16:54:25Z
dc.date.issued2018-10-01
dc.description.abstractThis work proposes a predictor system (multinodal forecasting) considering several points of an electrical network, such as substations, transformers, and feeders, based on an adaptive resonance theory (ART) neural network family. It is a problem similar to global forecasting, with the main difference being the strategy to align the input and output of the data with several parallel neural modules. Considering that multinodal prediction is more complex compared to global prediction, the multinodal prediction will use a fuzzy-ARTMAP neural network and a global load participation factor. The advantages of this approach are as follows: (1) the processing time is equivalent to the processing required for global forecasting (i.e., the additional time processing is quite low); and (2) Fuzzy-ARTMAP neural networks converge significantly faster than backpropagation neural networks (improved benchmark in precision). The preference for neural networks of the ART family is due to the characteristic stability and plasticity that these architectures have to provide results in a fast and precise way. To test the proposed forecast system, the results are presented for nine substations from the database of an electrical company.en
dc.description.affiliationElectrical Engineering Department UNESP – São Paulo State University, Av. Brasil 56–P.O. Box 31
dc.description.affiliationIFSP - Federal Institute of Education Science and Technology of São Paulo Campus Hortolândia
dc.description.affiliationUnespElectrical Engineering Department UNESP – São Paulo State University, Av. Brasil 56–P.O. Box 31
dc.format.extent307-316
dc.identifierhttp://dx.doi.org/10.1016/j.asoc.2018.06.039
dc.identifier.citationApplied Soft Computing Journal, v. 71, p. 307-316.
dc.identifier.doi10.1016/j.asoc.2018.06.039
dc.identifier.file2-s2.0-85049831467.pdf
dc.identifier.issn1568-4946
dc.identifier.scopus2-s2.0-85049831467
dc.identifier.urihttp://hdl.handle.net/11449/171211
dc.language.isoeng
dc.relation.ispartofApplied Soft Computing Journal
dc.relation.ispartofsjr1,199
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAdaptive resonance theory
dc.subjectArtificial neural networks
dc.subjectElectrical system distribution
dc.subjectLoad forecasting
dc.titleMultinodal load forecasting for distribution systems using a fuzzy-artmap neural networken
dc.typeArtigo
unesp.author.lattes6022112355517660[4]
unesp.author.lattes7166279400544764[5]
unesp.author.orcid0000-0002-0192-2651[4]
unesp.author.orcid0000-0001-6428-4506[5]

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