A new classification pattern recognition methodology for power system typical load profiles

Tsekouras, GJ; Kanellos, FD; Kontargyri, VT; Karanasiou, IS; Salis, AD; Mastorakis, NE

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Kanellos, FD	en
dc.contributor.author	Kontargyri, VT	en
dc.contributor.author	Karanasiou, IS	en
dc.contributor.author	Salis, AD	en
dc.contributor.author	Mastorakis, NE	en
dc.date.accessioned	2014-03-01T01:27:43Z
dc.date.available	2014-03-01T01:27:43Z
dc.date.issued	2008	en
dc.identifier.issn	11092734	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18559
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-59249092520&partnerID=40&md5=2446a67bbbddf496ff481b750f8ab496	en
dc.relation.uri	http://www.wseas.us/e-library/transactions/circuits/2008/31-497.pdf	en
dc.subject	Adaptive vector quantization	en
dc.subject	Adequacy measures	en
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-organized maps	en
dc.subject.other	Adaptive algorithms	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Electric network analysis	en
dc.subject.other	Electric power transmission networks	en
dc.subject.other	Fuzzy clustering	en
dc.subject.other	Intelligent robots	en
dc.subject.other	Optical projectors	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Pattern recognition systems	en
dc.subject.other	Planning	en
dc.subject.other	Power transmission	en
dc.subject.other	Resource allocation	en
dc.subject.other	Vector quantization	en
dc.subject.other	Adaptive vector quantization	en
dc.subject.other	Adequacy measures	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-organized maps	en
dc.subject.other	Clustering algorithms	en
dc.title	A new classification pattern recognition methodology for power system typical load profiles	en
heal.type	journalArticle	en
heal.publicationDate	2008	en
heal.abstract	In this paper a new pattern recognition methodology is described for the classification of the daily chronological load curves of power systems, in order to estimate their respective representative daily load profiles, which can be mainly used for load forecasting and feasibility studies of demand side management programs. It is based on pattern recognition methods, such as k-means, adaptive vector quantization, 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 error, function, the mean index adequacy, the clustering dispersion indicator, the similarity matrix indicator, the Davies-Bouldin indicator and the ratio of within cluster sum of squares to between cluster variation. This methodology is applied for the Greek power system, from which is proved that the separation between work days and non-work days for each season is not descriptive enough.	en
heal.journalName	WSEAS Transactions on Circuits and Systems	en
dc.identifier.volume	7	en
dc.identifier.issue	12	en
dc.identifier.spage	1090	en
dc.identifier.epage	1104	en