Patrick Glauner, J. Meira, P. Valtchev, R. State, F. Bettinger
The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey
International Journal of Computational Intelligence Systems, vol. 10, no. 1, pp. 760-775
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.
Beitrag (Sammelband oder Tagungsband)
Patrick Glauner, A. Boechat, L. Dolberg, R. State, F. Bettinger, Y. Rangoni, D. Duarte
Large-scale detection of non-technical losses in imbalanced data sets
Proceedings of the 2016 Seventh IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2016) [September 6-9, 2016; Minneapolis, MN, USA]
Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.