Clustering Bus Load Curves

dc.creatorSalgado R.M.
dc.creatorOhishi T.
dc.creatorBallini R.
dc.date2004
dc.date2015-06-26T14:25:34Z
dc.date2015-11-26T14:15:17Z
dc.date2015-06-26T14:25:34Z
dc.date2015-11-26T14:15:17Z
dc.date.accessioned2018-03-28T21:16:12Z
dc.date.available2018-03-28T21:16:12Z
dc.identifier078038718X
dc.identifier2004 Ieee Pes Power Systems Conference And Exposition. , v. 3, n. , p. 1251 - 1256, 2004.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-15944412089&partnerID=40&md5=915cbc57ecb000c5413bf99bd2ce19e9
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/94790
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/94790
dc.identifier2-s2.0-15944412089
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1242761
dc.descriptionThis paper proposes the clustering of a set of busses through a fuzzy c-means clustering approach. The utilization of fuzzy techniques in a clustering problem aims the attainment of a partition fuzzy in the data set, allowing degrees of relationship between different elements of the set, this way, an element can belong to more than one group with different membership value. In this paper the clustering algorithm aims at exploring data characteristics and determining groups composed by busses with similar bus daily load. Results show the efficiency of the clustering method, where the data was classified into distinct groups such as: commercial, residential and industrial consumption profiles. © 2004 IEEE.
dc.description3
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dc.description1251
dc.description1256
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dc.languageen
dc.publisher
dc.relation2004 IEEE PES Power Systems Conference and Exposition
dc.rightsfechado
dc.sourceScopus
dc.titleClustering Bus Load Curves
dc.typeActas de congresos


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