Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps
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AuthorValero Verdú, Sergio; Ortiz García, Mario; Senabre Blanes, Carolina; Gabaldón Marín, Antonio; García Franco, Francisco J.
Knowledge AreaIngeniería e Infraestructura del TransporteIngeniería e Infraestructura del Transporte
SponsorsThis work was supported by European Union Sixth Frame work Program under Project EU-DEEP SES6-CT-2003-503516.Paper no.TPWRS-00633-2005
PublisherIEEE Power & Energy Society
Bibliographic 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 0885-8950
KeywordsGestión de la demanda
Segmentación de clientes
Patrones de carga
Libre organización de los mapas
Electrical customer segmentation
Different 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 ...
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