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

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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 http://hdl.handle.net/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

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