Hybrid Machine Learning Framework for Electricity Theft Detection and Consumption Anomaly Analysis in Nagpur Distribution Networks

Abstract

Electricity theft and electricity consumption anomalies are two of the biggest problems faced by today's distribution network systems, leading to huge losses and inefficiencies. Traditional methods of theft detection are mainly based on manual inspection and are costly and ineffective in detecting more sophisticated consumption patterns. With the emergence of smart meters, huge amount of electricity consumption data can now be analyzed with machine learning methods to detect suspicious patterns. This paper aims to design a Hybrid Machine Learning Framework for Detection of Electricity Theft and Consumption Anomaly Analysis in Nagpur Distribution Networks. The proposed system will use hybrid methods of supervised and unsupervised learning algorithms to detect both types of known and unknown electricity theft patterns. Deviation analysis using a rule-based method is first used to detect any significant deviation in the consumption pattern.