ELECTRICITY THEFT DETECTION USING MACHINE LEARNING

Utility companies are quite concerned about electricity theft. Individual user power usage is one of the vast amounts of data that the smart grid (SG) system generates. Machine learning and deep learning methods can precisely identify electricity theft customers using this data. We develop a convolutional neural network (CNN) model for automatically detecting electricity theft. This study takes into account experimentation to determine the sequential model’s (SM) ideal configuration for categorising and identifying electricity theft. The two layers with the best performance each have 64 nodes in the second layer and 128 nodes in the first layer. The precision was up to 0.92. This makes it possible to create high-performance electricity signal classifiers for a variety of applications.