Deep Neural Networks are Utilized in Energy Management Systems for identifying Theft of Electricity

The installation of connected systems is essential for the identification of renewable energy thieves because it generate enormous volumes of the information, include information about customer behavior, that could potentially be identify electricity theft using machine learning and deep learning techniques. This project describes a method for detecting theft that employs extensive information in the time and frequency domains in a deep neural network-based classification approach. Through data interpolation and synthetic data generation procedures, we solve dataset flaws such as missing data and class imbalance issues. We evaluate and analyze the contribution of features from both the temporal and frequency domains, execute experiments in combined and reduced feature space using principal component analysis, and lastly add a minimal redundancy-based strategy to maximizing significance for determining what’s most pertinent features. We increase the detection performance of power theft by optimizing hyper parameters with a Bayesian optimizer and using an adaptive moment estimation optimizer to run tests with varying values of critical parameters to identify the ideal settings that produce the greatest accuracy. Since we train the model with over and under sampling datasets, it provides the equal representation while training the model.