On the use of information theoretic mean shift for electricity load patterns clustering

Author

Sumaili, Jean ; Keko, Hrvoje ; Miranda, V. ; Chicco, Gianfranco

Author_Institution

Power Syst. Unit, INESC Porto-Inst. de Eng. de Sist. e Comput. do Porto, Porto, Portugal

fYear

2011

fDate

19-23 June 2011

Firstpage

1

Lastpage

6

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).

Keywords

energy consumption; information theory; pattern classification; pattern clustering; power markets; clustering indices; data structure; electricity customer load pattern classification; electricity load pattern clustering; information theoretic mean shift algorithm; Clustering algorithms; Cost function; Entropy; Indexes; Kernel; Minimization; Partitioning algorithms; clustering; information theoretic learning; load patterns; mean shift; modes finding; outliers;

fLanguage

English

Publisher

ieee

Conference_Titel

PowerTech, 2011 IEEE Trondheim

Conference_Location

Trondheim

Print_ISBN

978-1-4244-8419-5

Electronic_ISBN

978-1-4244-8417-1

Type

conf

DOI

10.1109/PTC.2011.6019390

Filename

6019390