DEEP NEURAL NETWORK-BASED ELECTRICITY THEFT DETECTION IN SMART GRID SYSTEMS

Abstract

This research presents a novel method for detecting electrical trickery in smart grids using deep neural networks. Electricity theft has become a major problem for contemporary power distribution systems, resulting in financial losses and a decrease in the dependability of energy delivery. In order to examine a significant amount of smart meter data and find unusual consumption patterns linked to fraudulent behavior, the suggested study makes use of deep learning algorithms. To find complex patterns and distinguish between normal and abnormal electricity consumption behaviors, the software builds a deep neural network using previous energy usage data. Because the technique automatically pulls relevant information and adjusts to evolving larceny schemes, its detection accuracy is higher than that of traditional machine learning and rule-based systems. Experimental results show that the suggested model successfully detects electricity theft with excellent precision, recall, and accuracy. As a result, power providers may improve the security and effectiveness of smart grid systems while also reducing revenue losses.