Machine Learning based Smart Electricity Monitoring & Fault Detection for Smart City 4.0 Ecosystem

Abstract

Growing electricity needs among the vast majority of the population seconded by a voluminous increase in electrical appliances have led to a huge surge in electric power demands. With thediminishing unit price of electric meters and increase of loading, it has been observed that a certain amount of electric meters generate faulty readings after exhaustive usage. This results in erroneous meter readings thereby affecting the billings. We propose a fault detecting learning algorithm that is trained by early meter readings and compares the actual meter reading (AMR) with the predicted meter reading (PMR). The decision matrix generates an alarm if |PMR-AMR|>T; where T equals the threshold limit. T itself is decided by the learning algorithm depending upon the meter variance. Moreover, our system also detects if there is any power theft as such an action would result in a sudden rise in AMR. The learning algorithm deploys six binary classifiers which reflect an accuracy of 98.24% for the detection module and an error rate of 1.26% for the prediction module.