Article (Scientific journals)
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Glauner, Patrick; Meira, Jorge Augusto; Valtchev, Petko et al.
2017In International Journal of Computational Intelligence Systems, 10 (1), p. 760-775
Peer reviewed
 

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Keywords :
Covariate shift; Electricity theft; Expert systems; Machine learning; Non-technical losses; Stochastic processes
Abstract :
[en] Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.
Disciplines :
Computer science
Author, co-author :
Glauner, Patrick ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Meira, Jorge Augusto ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Valtchev, Petko ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
State, Radu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Bettinger, Franck;  CHOICE Technologies Holding Sàrl
External co-authors :
no
Language :
English
Title :
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Publication date :
2017
Journal title :
International Journal of Computational Intelligence Systems
Volume :
10
Issue :
1
Pages :
760-775
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
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since 07 March 2017

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