Non-technical losses in electricity distribution: a bibliometric analysis

Savian, Fernando de Souza; Siluk, Julio Cezar Mairesse; Garlet, Taís Bisognin; Nascimento, Felipe Moraes do; Pinheiro, J.R.

doi:10.1109/tla.2021.9447584

Cited by 4 publications

(1 citation statement)

References 68 publications

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“…In underdeveloped and developing countries, the situation tends to worsen. The main emerging countries record annual losses of USD 58.7 billion [6]. In Brazil, in 2022, NTLs represented 14.6% of all losses in the low-voltage power system.…”

Section: Introductionmentioning

confidence: 99%

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

Souza,

Gouveia,

Ferreira

et al. 2024

Energies

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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.

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Section: Introductionmentioning

confidence: 99%