A data-driven approach to reduce electricity theft in developing countries. (December 2021) - Nadeem, Ahmad; Arshad, Naveed
Abstract: Theft of electricity is a problem in many developing countries. But AMI is paving the way for data-centric architecture to help in theft detection. However, a smart grid or even AMR is a long shot for many developing countries due to the costs involved in its large-scale deployment. This paper presents a technique to detect outliers among electricity users that further investigates electricity theft using data analytics on monthly usage data available to every utility company. Using this technique, we have reduced the search space for theft identification to as low as 3.4% of the total customer base. Highlights: Electricity theft is a rampant problem in many countries. Unavailability of AMI and smart meters makes finding theft difficult. A lot of utility revenue is eroded due to electricity theft. We have developed a technique to identify outliers using monthly energy data. Our results show a clear correlation between the number of outliers and losses reported at feeders.