An Efficient Power Theft Detection Using Mean-Shift Clustering and Deep Learning in Smart Grid

Abstract

Energy theft constitutes a major concern for the utility operators in these modern smart homes. The task of detecting and reducing the energy losses has been highly challenging due to insufficient inspection techniques. Energy distribution is comprised of Technical and Non-Technical Losses (NTL). Energy theft generates a major share of Non-Technical Losses which also prompts budgetary misfortunes for the service organizations. The data in the modern smart meters are transmitted in wireless mode. Therefore the smart homes are vulnerable to energy theft. Many new technologies have been adopted to resolve the issue of energy theft in Advance Metering Infrastructure (AMI) in Smart Grids. Consumption pattern must be derived to identify illegal energy consumers. Computational method has been derived to analyze and identify energy consumption patterns based on data mining techniques. Machine learning technique improves the got client energy utilization readings and guides them on contrasting irregularities in use. Deep Learning method as Convolution Neural Network is implemented on the activity of order methods on client energy utilization and illegal use of electricity and the amount of consumption by energy theft.