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dc.contributor.authorValero Verdú, Sergio 
dc.contributor.authorOrtiz García, Mario 
dc.contributor.authorGarcía Franco, Francisco J. 
dc.contributor.authorEncinas Redondo, Nuria 
dc.contributor.authorGabaldón Marín, Antonio 
dc.contributor.authorMolina García, Ángel 
dc.contributor.authorGómez Lázaro, Emilio 
dc.date.accessioned2009-10-02T06:50:27Z
dc.date.available2009-10-02T06:50:27Z
dc.date.issued2004-10
dc.identifier.citationVALERO VERDÚ, Sergio, ORTÍZ GARCÍA, Mario, ENCINAS, Nuria, GABALDÓN MARÍN, Antonio, MOLINA GARCÍA, Ángel, GÓMEZ LÁZARO, Emilio. Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters. En: Power Systems Conference and Exposition ( 2004: New York ). IEEE PES. Power Systems Conference & Exposition. 10-13 October, 2004. New York: IEEE Power & Energy Society. 2004. vol. 2. 899- 906 p.es
dc.description.abstractThis paper shows the capacity of modern computational techniques such as the self-organizing map (SOM) as a methodology to achieve the classification of the electrical customers in a commercial or geographical area. This approach allows to extract the pattern of customer behavior from historic load demand series. Several ways of data analysis from load curves can be used to get different input data to “feed” the neural network. In this work, we propose two methods to improve customer clustering: the use of frequency-based indices and the use of the hourly load curve. Results of a case study developed on a set of different spanish customers and a comparison between the two approachs proposed here are presented.es
dc.formatapplication/pdf
dc.language.isoenges
dc.publisherIEEE Power & Energy Society (PES)es
dc.rightsCopyright © 2004 IEEEes
dc.titleCharacterization and identification of electrical customers through the use of self-organizing maps and daily load parameterses
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.subjectRedes neuronales artificialeses
dc.subjectSegmentación de clientes eléctricoses
dc.subjectMercado eléctricoes
dc.subjectAnálisis tiempo-frecuenciaes
dc.subjectAgregación de clientees
dc.subjectArtificial neural networkses
dc.subjectElectrical customer segmentationes
dc.subjectElectricity marketes
dc.subjectTime-Frequency analysises
dc.subject.otherIngeniería Eléctricaes
dc.identifier.urihttp://hdl.handle.net/10317/1143
dc.identifier.doi10.1109/PSCE.2004.1397641


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