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
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