Publicação: Electrical consumers data clustering through optimum-path forest
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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.
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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
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Inglês
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2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011.
