Electricity Theft Detection using Machine Learning

Ivan Petrlik, Pedro Lezama, Ciro Rodriguez, Ricardo Inquilla, Julissa Elizabeth Reyna-González, Roberto Esparza

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)


This research work dealt with the indiscriminate theft of electric power, reported as a non-technical loss, affecting electric distribution companies and customers, triggering serious consequences including fires and blackouts. The research focused on recommending the best prediction model using Machine Learning in electrical energy theft. The source of the information on the electricity consumption of 42372 consumers was a dataset published in the State Grid Corporation of China. The method used was data imputation, data balancing (oversampling and under sampling), and feature extraction to improve energy theft detection. Five Machine Learning models were tested. As a result, the accuracy indicator of the SVM model was 81%, K-Nearest Neighbors 79%, Random Forest 80%, Logistic Regression 69%, and Naive Bayes 68%. It is concluded that the best performance, with an accuracy of 81%, is obtained by using the SVM model.

Idioma originalInglés
Páginas (desde-hasta)420-425
Número de páginas6
PublicaciónInternational Journal of Advanced Computer Science and Applications
EstadoPublicada - 2022


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