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

Autor: Patrick Glauner, Jorge Augusto Meira, Petko Valtchev, Radu State, Franck Bettinger
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: International Journal of Computational Intelligence Systems, Vol 10, Iss 1 (2017)
Druh dokumentu: article
ISSN: 25872435
1875-6883
DOI: 10.2991/ijcis.2017.10.1.51
Popis: 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.
Databáze: Directory of Open Access Journals
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