Electricity-theft detection for smart grid security using smart meter data: A deep-CNN based approach

Electricity theft has a considerable negative effect on energy suppliers and power infrastructure, leading to non-technical losses and business losses. Power quality deteriorates and overall profitability falls as a result of energy theft. By fusing information and energy flow, smart grids may assist solve the issue of power theft. The examination of smart grid data aids in the detection of power theft. However, the earlier techniques were not very good in detecting energy theft. In this work, we suggested an electricity theft detection approach using smart meter consumption data in order to handle the aforementioned issues and assist and assess energy supply businesses to lower the obstacles of limited energy, unexpected power usage, and bad power management. In specifically, the Deep CNN model effectively completes two tasks: it differentiates between energy that is not periodic and that is, while keeping the general features of data on power consumption. The trial's results show that the deep CNN model outperforms prior ones and has the best level of accuracy for detecting energy theft. (c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Electricity theft has a significant negative impact on energy suppliers and power infrastructure, resulting in non-technical losses and business losses. Smart grids can help address the issue of power theft by integrating information and energy flow, and the analysis of smart grid data aids in the detection of power theft. This study proposes an electricity theft detection approach using smart meter consumption data to mitigate the aforementioned challenges and assist in assessing energy supply businesses in managing limited energy, unexpected power usage, and poor power management.