A new pattern recognition methodology for classification of load profiles for ships electric consumers

Tsekouras, GJ; Hatzilau, IK; Prousalidis, JM

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Hatzilau, IK	en
dc.contributor.author	Prousalidis, JM	en
dc.date.accessioned	2014-03-01T02:45:55Z
dc.date.available	2014-03-01T02:45:55Z
dc.date.issued	2009	en
dc.identifier.issn	1476-1548	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32453
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-77949638881&partnerID=40&md5=566e3bda489253c0d7381e186a63d826	en
dc.subject.other	Between-cluster variation	en
dc.subject.other	Clustering methods	en
dc.subject.other	Electric consumers	en
dc.subject.other	Error function	en
dc.subject.other	Fuzzy k-means	en
dc.subject.other	Hellenic Navy	en
dc.subject.other	Hier-archical clustering	en
dc.subject.other	K-means	en
dc.subject.other	Load curves	en
dc.subject.other	Load profiles	en
dc.subject.other	Optimisations	en
dc.subject.other	Pattern recognition method	en
dc.subject.other	Self-organising maps	en
dc.subject.other	Similarity matrix	en
dc.subject.other	Study case	en
dc.subject.other	Sum of squares	en
dc.subject.other	Vector quantisation	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Ship equipment	en
dc.subject.other	Electric ship equipment	en
dc.title	A new pattern recognition methodology for classification of load profiles for ships electric consumers	en
heal.type	conferenceItem	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	In this paper a new pattern recognition methodology is presented for the classification of the daily chronological load curves of ship electric consumers (equipment) and the determination of the respective typical load curves of each one of them. It is based on pattern recognition methods, such as k-means, adaptive vector quantisation, fuzzy k-means, self-organising maps and hierarchical clustering, which are theoretically described and properly adapted. The parameters of each clustering method are properly selected by an optimisation 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. As a study case, this methodology is applied to a set of consumers of Hellenic Navy MEKO type frigates.	en
heal.publisher	INST MARINE ENGINEERS, SCIENCE & TECHNOLOGY	en
heal.journalName	Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology	en
dc.identifier.isi	ISI:000266094500004	en
dc.identifier.issue	14	en
dc.identifier.spage	45	en
dc.identifier.epage	58	en