Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

Abstract

Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study’s contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions.

Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review

“…In Table 1, a comparison of previous survey papers on data-driven ETD is presented. While existing review papers focus on specific aspects of data-driven ETD, such as methodologies [16][17][18][19][20], smart grid components [21], consumer privacy [22], and the scale of energy usage [23], there has not been an in-depth analysis of data-driven ETD that emphasizes the limitations of energy theft datasets. This survey addresses these limitations and categorizes AI modeling based on the types of energy theft datasets.…”