Comprehensive Analysis of Cost-Effective Data-Driven Approaches for Electricity Theft Detection

Electricity theft poses a significant challenge to utilities and power grids worldwide, leading to substantial financial losses, increased operational costs, and overloading of electrical networks. This not only compromises the efficiency and reliability of power supply systems but also increases the risk of grid failures and safety hazards for maintenance personnel. Despite various efforts to mitigate electricity theft, including manual detection methods, hardware-based detection systems, and data-driven approaches, the issue remains prevalent and difficult to fully eliminate. The existing data-driven techniques, particularly traditional methods such as Support Vector Machines, Random Forests, Long Short-Term Memory networks, and Decision Trees, have shown limitations in terms of accuracy, precision, sensitivity, recall, and specificity, making them less effective for real-time detection and prevention of electricity theft.This paper aims to address these challenges by exploring advanced data-driven methods, specifically deep learning techniques, to enhance the accuracy and reliability of electricity theft detection systems. By applying Convolutional Neural Networks, Wide and Deep Convolutional Neural Networks, and Time Series Forecasting alongside machine learning approaches, the research aims to overcome the limitations of traditional models. These advanced methods provide improved performance in detecting theft, offering better precision and sensitivity in comparison to fundamental approaches. The study presents a detailed comparison of different models, highlighting the advantages of deep learning and time series-based forecasting in improving the overall detection process, with the goal of contributing to more robust and efficient electricity theft detection mechanisms for modern power grids.