The Detection of Non-Technical Losses and Electricity Theft by Smart Meter Data and Artificial Intelligence In the Context Of Electric Distribution Utilities: A Comprehensive Review

Abstract

Across the world, electric distribution utilities are facing two major challenges i.e., non-technical losses and electricity theft. These losses occur extremely high. Consequently, electric distribution networks’ performance deteriorates drastically. Many traditional methods are in practice to detect and minimize these losses, but these are not so effective and also very time-consuming. Hence, keen researchers are looking forward to recent technology, i.e., Artificial Intelligence, because NTL detection by Artificial Intelligence is superior to the traditional techniques in terms of performance such as accuracy. By the application of AI technique on dataset generated by smart meters, electricity theft and NTL are filtered out. This paper describes the causes of NTL followed by an impact on economy. Further, we have thoroughly studied various exercises of technical surveys. Thereafter, based on different AI techniques and essential parameters, a comparison with the existing works has been analyzed. Various simulation tools and compatible environments have been explained. Moreover, multiple challenges occur during AI-based detection of NTL, and their possible solutions are also being discussed.