Abstract
Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research.
Highlights
Losses of electrical energy in the power grids at the transmission and distribution level include both technical losses (TL) and non-technical losses (NTLs) [1]
The hardware-based solutions mainly focus on installing meters with with specific equipment to enable in identifying any malicious activity by consumers specific equipment to enable
This article presents a detailed review of state-of-the-art methodologies for identifying fraudulent activities in power distribution companies (PDCs) as discussed in three significant repositories: ACM Digital Library, Science Direct, and IEEE explore published since 2000
Summary
Losses of electrical energy in the power grids at the transmission and distribution level include both technical losses (TL) and non-technical losses (NTLs) [1]. The fraudulent behavior of energy customers is usually associated with electricity theft, regularized corruption, and organized crime [4]. Such sort of losses cannot be precisely estimated. The installation of smart meters has appeared to be one of the meaningful and latest solutions to address the NTL detection issue [6]. Their deployment, operational cost, and design involve massive amounts, which are not practical solutions for weak economies [7]
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