Electrical consumers data clustering through optimum-path forest - Ramos, Caio C. O.; Souza, André N.; Nakamura, Rodrigo Y. M. [UNESP]; Papa, João Paulo [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2011)
Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.
Keywords: Clustering; Non-technical Losses; Optimum-Path Forest; Pattern Recognition; Clustering techniques; Data clustering; Data sets; Electric power company; Non-technical loss; Specific profile; Clustering algorithms; Crime; Data processing; Electric utilities; Industry; Intelligent systems; Pattern recognition; Power transmission; Forestry; Algorithms; Artificial Intelligence; Data Processing; Electric Power Transmission; Electricity; Losses