期刊
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
卷 10, 期 1, 页码 760-775出版社
SPRINGERNATURE
DOI: 10.2991/ijcis.2017.10.1.51
关键词
Covariate shift; electricity theft; expert systems; machine learning; non-technical losses; stochastic processes
资金
- Luxembourg National Research Fund
Detection of non-technical losses (NIL) 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 NIL detection and suggests how they could be addressed in the future.
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