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dc.contributor.authorValero Verdú, Sergio 
dc.contributor.authorOrtiz García, Mario 
dc.contributor.authorSenabre Blanes, Carolina 
dc.contributor.authorGabaldón Marín, Antonio 
dc.contributor.authorGarcía Franco, Francisco J. 
dc.date.accessioned2018-09-13T08:56:12Z
dc.date.available2018-09-13T08:56:12Z
dc.date.issued2006-11
dc.identifier.citationVALERO VERDÚ, Sergio, ORTIZ GARCÍA, Mario, SENABRE, Carolina, GABALDÓN MARÍN, Antonio, GARCÍA FRANCO, Francisco J. Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Power & Energy Magazine, 21 (4): 1672-1682, Noviembre 2006. ISSN 0885-8950es_ES
dc.identifier.issn0885-8950
dc.description.abstractDifferent methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns.es_ES
dc.description.sponsorshipThis work was supported by European Union Sixth Frame work Program under Project EU-DEEP SES6-CT-2003-503516.Paper no.TPWRS-00633-2005es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherIEEE Power & Energy Societyes_ES
dc.rightsCopyright © 2006 IEEEes
dc.titleClassification, filtering, and identification of electrical customer load patterns through the use of self-organizing mapses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.otherIngeniería e Infraestructura del Transportees_ES
dc.subject.otherIngeniería e Infraestructura del Transportees_ES
dc.subjectGestión de la demandaes_ES
dc.subjectExtracciónde datoses_ES
dc.subjectSegmentación de clienteses_ES
dc.subjectPatrones de cargaes_ES
dc.subjectLibre organización de los mapases_ES
dc.subjectData mininges_ES
dc.subjectDemand managementes_ES
dc.subjectElectrical customer segmentationes_ES
dc.subjectLoad patternses_ES
dc.subjectSelf-organizing mapses_ES
dc.identifier.urihttp://hdl.handle.net/10317/7212
dc.identifier.doi10.1109/TPWRS.2006.881133
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES


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