Authors:
Matías Di Martino
;
Federico Decia
;
Juan Molinelli
and
Alicia Fernández
Affiliation:
Facultad de Ingeniería Universidad de la República Montevideo, Uruguay
Keyword(s):
Electricity theft, Support vector machine, Optimum path forest, Unbalance class problem, Combining classifier, UTE.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Data Engineering
;
Health Engineering and Technology Applications
;
Information Retrieval
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Ranking
;
Signal Processing
;
Software Engineering
Abstract:
Improving nontechnical loss detection is a huge challenge for electric companies. The great number of clients and the diversity of the different types of fraud makes this a very complex task. In this paper we present a fraud detection strategy based on class imbalance research. An automatic detection tool combining classification strategies is proposed. Individual classifiers such as One Class SVM, Cost Sensitive SVM (CS-SVM), Optimum Path Forest (OPF) and C4.5 Tree, and combination functions are designed taken special care in the data’s class imbalance nature. Analysis over consumers historical kWh load profile data from Uruguayan Electric Company (UTE) shows that using combination and balancing techniques improves automatic detection performance.