On the Use of Information Theoretic Mean Shift for Electricity Load Patterns Clustering

dc.contributor.author	Jean Sumaili	en
dc.contributor.author	Gianfranco Chicco	en
dc.contributor.author	Hrvoje Keko	en
dc.contributor.author	Vladimiro Miranda	en
dc.date.accessioned	2017-11-16T13:18:28Z
dc.date.available	2017-11-16T13:18:28Z
dc.date.issued	2011	en
dc.description.abstract	This paper analyzes the application of the Information Theoretic (IT) Mean Shift algorithm for modes finding in order to provide the classification of Electricity Customer Load Patterns. The impact of the algorithm parameters is discussed and then clustering indices are used in order to make a comparison with the classical methods available. Results show a good capability of the modes found in capturing the data structure, aggregating similar load patterns and identifying the uncommon patterns (outliers).	en
dc.identifier.uri	http://repositorio.inesctec.pt/handle/123456789/2173
dc.language	eng	en
dc.relation	5164	en
dc.relation	208	en
dc.relation	4811	en
dc.rights	info:eu-repo/semantics/openAccess	en
dc.title	On the Use of Information Theoretic Mean Shift for Electricity Load Patterns Clustering	en
dc.type	conferenceObject	en
dc.type	Publication	en