Characterization of Non-Technical Loss in Consumers of the Electric Distribution System

This paper presents the results of an exploratory analysis performed on a set of consumer characteristics of the electric distribution system. The main objective was to identify the most important attributes for recognizing consumers associated with non-technical losses. To achieve this, a hybrid approach was employed, combining heuristic analysis with statistical techniques, including correlation coefficients, mutual information, and scatter plots. As a result of the study, it was possible to reduce the initial set of 385 attributes to just 20, which were also ranked in order of importance. The selected attributes contain relevant information for classifying consumers and will later be used as input in a predictive model. The findings of this paper can help energy companies in understanding which consumer information should be monitored to detect non-technical losses.

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Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3619406
Citations
1
Primary Topic
Electricity Theft Detection Techniques
Type
article
Field-Weighted Citation Impact
0.73
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article

Characterization of Non-Technical Loss in Consumers of the Electric Distribution System

Rafael Mendonça Rocha Barros, Edson Guedes da Costa, Jalberth F. Araújo, Henrique D. Silva
1 citations
IEEE Access
Electricity Theft Detection Techniques
0.73
article

Characterization of Non-Technical Loss in Consumers of the Electric Distribution System

Rafael Mendonça Rocha Barros, Edson Guedes da Costa, Jalberth F. Araújo, Henrique D. Silva
article en
1 citations

Abstract

This paper presents the results of an exploratory analysis performed on a set of consumer characteristics of the electric distribution system. The main objective was to identify the most important attributes for recognizing consumers associated with non-technical losses. To achieve this, a hybrid approach was employed, combining heuristic analysis with statistical techniques, including correlation coefficients, mutual information, and scatter plots. As a result of the study, it was possible to reduce the initial set of 385 attributes to just 20, which were also ranked in order of importance. The selected attributes contain relevant information for classifying consumers and will later be used as input in a predictive model. The findings of this paper can help energy companies in understanding which consumer information should be monitored to detect non-technical losses.

IEEE AccessVol. 13
Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco (BR), Universidade Federal de Campina Grande (BR)
Openalex Percentile: Top 23%
Electricity Theft Detection Techniques
0.73
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Characterization of Non-Technical Loss in Consumers of the Electric Distribution System — Rafael Mendonça Rocha Barros, Edson Guedes da Costa, et al. · IEEE Access (2025) | TGRS Research Map | TGRS