Two-stage pattern recognition of load curves for classification of electricity customers

Tsekouras, GJ; Hatziargyriou, ND; Dialynas, EN

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Hatziargyriou, ND	en
dc.contributor.author	Dialynas, EN	en
dc.date.accessioned	2014-03-01T01:27:31Z
dc.date.available	2014-03-01T01:27:31Z
dc.date.issued	2007	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18488
dc.subject	Adaptive vector quantization	en
dc.subject	Chronological load patterns	en
dc.subject	Clustering	en
dc.subject	Customer classes	en
dc.subject	Fuzzy k-means	en
dc.subject	Hierarchical clustering	en
dc.subject	K-means	en
dc.subject	Pattern recognition	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Electric loads	en
dc.subject.other	Electric power systems	en
dc.subject.other	Vector quantization	en
dc.subject.other	Chronological load patterns	en
dc.subject.other	Classification of customers	en
dc.subject.other	Hierarchical clustering	en
dc.subject.other	Load forecasting	en
dc.subject.other	Pattern recognition	en
dc.title	Two-stage pattern recognition of load curves for classification of electricity customers	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2007.901287	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2007.901287	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	This paper describes a two-stage methodology that was developed for the classification of electricity customers. It is based on pattern recognition methods, such as k-means, Kohonen adaptive vector quantization, fuzzy k-means, and hierarchical clustering, which are theoretically described and properly adapted. In the first stage, typical chronological load curves of various customers are estimated using pattern recognition methods, and their results are compared using six adequacy measures. In the second stage, classification of customers is performed by the same methods and measures, together with the representative load patterns of customers being obtained from the first stage. The results of the first stage can be used for load forecasting of customers and determination of tariffs. The results of the second stage provide valuable information for electricity suppliers in competitive energy markets. The developed methodology is applied on a set of medium voltage customers of the Greek power system, and the obtained results are presented and discussed. © 2007 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Power Systems	en
dc.identifier.doi	10.1109/TPWRS.2007.901287	en
dc.identifier.isi	ISI:000248352100027	en
dc.identifier.volume	22	en
dc.identifier.issue	3	en
dc.identifier.spage	1120	en
dc.identifier.epage	1128	en