The Algorithm for Intellectual Support for Management Decision-Making in the Process of Operational Detection of Non-Technical Electricity Losses in Electric Networks

Morgoev, Irbek Dzhabrailovich

doi:10.53015/18159958_2022_18_3_33

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this two-way flow of communication inevitably leads to problems related to cyber security, such as data leakage and theft/alteration of consumer electricity data [10][11][12]. In other words, the AMI can be hacked and reprogrammed with the intention of committing fraud related to electricity theft [13].…”

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

View full text Add to dashboard Cite

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.

show abstract