A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers

Tsekouras, GJ; Kotoulas, PB; Tsirekis, CD; Dialynas, EN; Hatziargyriou, ND

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Kotoulas, PB	en
dc.contributor.author	Tsirekis, CD	en
dc.contributor.author	Dialynas, EN	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:27:47Z
dc.date.available	2014-03-01T01:27:47Z
dc.date.issued	2008	en
dc.identifier.issn	0378-7796	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18568
dc.subject	Clustering algorithms	en
dc.subject	Fuzzy k-means	en
dc.subject	Hierarchical clustering	en
dc.subject	k-Means	en
dc.subject	Load profiles	en
dc.subject	Pattern recognition	en
dc.subject	Self-organizing map	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Clustering algorithms	en
dc.subject.other	Electric loads	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Fuzzy k-means	en
dc.subject.other	Hierarchical clustering	en
dc.subject.other	k-Means	en
dc.subject.other	Load profiles	en
dc.subject.other	Self-organizing map	en
dc.subject.other	Pattern recognition	en
dc.title	A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.epsr.2008.01.010	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.epsr.2008.01.010	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	This paper describes a pattern recognition methodology for the classification of the daily chronological load curves of each large electricity customer, in order to estimate his typical days and his respective representative daily load profiles. It is based on pattern recognition methods, such as k-means, self-organized maps (SOM), fuzzy k-means and hierarchical clustering, which are theoretically described and properly adapted. The parameters of each clustering method are properly selected by an optimization process, which is separately applied for each one of six adequacy measures. The results can be used for the short-term and mid-term load forecasting of each consumer, for the choice of the proper tariffs and the feasibility studies of demand side management programs. This methodology is analytically applied for one medium voltage industrial customer and synoptically for a set of medium voltage customers of the Greek power system. The results of the clustering methods are presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Electric Power Systems Research	en
dc.identifier.doi	10.1016/j.epsr.2008.01.010	en
dc.identifier.isi	ISI:000257619800003	en
dc.identifier.volume	78	en
dc.identifier.issue	9	en
dc.identifier.spage	1494	en
dc.identifier.epage	1510	en