The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

DOI

10.2991/ijcis.2017.10.1.51How to use a DOI?

Keywords

Covariate shift; electricity theft; expert systems; machine learning; non-technical losses; stochastic processes

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Patrick Glauner
AU  - Jorge Augusto Meira
AU  - Petko Valtchev
AU  - Radu State
AU  - Franck Bettinger
PY  - 2017
DA  - 2017/03/13
TI  - The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey
JO  - International Journal of Computational Intelligence Systems
SP  - 760
EP  - 775
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.51
DO  - 10.2991/ijcis.2017.10.1.51
ID  - Glauner2017
ER  -