Electrical consumers data clustering through Optimum-Path Forest

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...

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Bibliographic Details
Main Authors: Ramos, C. C. O., Souza, A. N., Nakamura, R. Y. M., Papa, J. P.
Format: Conference Proceeding
Language:English
Subjects:
Clustering
Clustering algorithms
Companies
Conferences
Context
Feature extraction
Non-technical Losses
Optimum-Path Forest
Pattern Recognition
Power systems
Support vector machines
Online Access:Request full text
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Summary: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.
DOI:10.1109/ISAP.2011.6082217