Utilizing Machine Learning for Detecting Electricity Theft in Smart Grids

Abstract

Smart grids, which provide several advantages, such as improved energy efficiency, less power outages, and higher security, are growing in popularity as a result of the rising need for electricity. But one of the biggest problems with smart grids is power theft, which costs utility companies a lot of money. Therefore, electric power distribution firms are quite concerned about theft of power. The purpose of this study is to offer an effective technique based on artificial neural networks (ANNs) for identifying Smart grid electricity theft. After training on a dataset of acceptable consumption patterns, the ANN model will be evaluated on information about energy theft events. The design will be tested using test data in order to assess the effectiveness of the suggested strategy. The outcomes that we anticipated from our suggested ANN-based method for Smart grid detection of electricity theft are favourable. 99% Training Accuracy and 99% Validation Accuracy were attained by our method. The performance measures that will be employed include F1-score, recall, accuracy, and precision. Additionally, we created the proposed system that makes use of the Flask Web framework to make it easier to use and provide a better user interface for outcome prediction. The research will likely produce an efficient method for employing ANN to identify energy theft in smart grids, which utility companies can utilise to increase revenue collection and fortify the security of the smart grid. This research may potentially be expanded to other fields, such intrusion detection in computer networks and fraud detection in financial systems, that entail anomaly identification in large-scale datasets