Comparative Research on the Techniques of Electricity Fraud Detection Using Different Machine Learning Techniques

The vulnerability of power theft has hampered the electricity industry for decades. It obstructs social progress by having varying degrees of impact on home, commercial, and industrial customers. Sneak thieves have caught up with contemporary metering systems, putting electricity suppliers in trouble financially. This comparative analysis is the first step in the presentation of principles. Theft of electricity has serious consequences for the power grid's proper operation as well as the economic benefits of power corporations and commercial power service providers. An effective anti-power-theft algorithm is required for tracking power usage statistics in order to detect electricity power theft. In this literature review, we differentiate the Support Vector Machine (SVM) algorithm with other techniques for detecting abnormal usage among consumers (i.e., electricity fraudsters) in time-series data on power consumption. The results show some combinations can reach significantly better values than others, comparing both the balancing techniques for a same machine learning method itself as well as comparing these combinations between themselves.