Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption pr...

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Bibliographic Details
Published inEnergies (Basel) Vol. 17; no. 7; p. 1729
Main Authors Souza, Murilo A., Gouveia, Hugo T. V., Ferreira, Aida A., de Lima Neta, Regina Maria, Nóbrega Neto, Otoni, da Silva Lira, Milde Maria, Torres, Geraldo L., de Aquino, Ronaldo R. B.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2024
Subjects
Artificial intelligence
Consumers
Datasets
distribution systems
Electric power
Electric utilities
Electricity
Electricity distribution
Employees
Energy consumption
Fraud
Marketing research
Neural networks
non-technical loss
smart grids
Smart meters
Support vector machines
Time series
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Summary:Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17071729