The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
It tackles the issue of economic losses and grid instability for electricity providers, but it is incremental as it synthesizes existing work without introducing new methods.
This survey paper addresses the problem of detecting non-technical losses (NTL) in electricity distribution, such as theft and billing errors, which can account for up to 40% of distributed electricity in some countries, by reviewing AI-based methods, algorithms, features, and datasets used in current research.
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.