Power Theft and Energy Fraud Detection

The illegal usage of electricity has been a major source of worry. Fraudulent electricity usage on a large scale may result in an unequal demand-supply imbalance. This study created a power theft detection approach in response to the extensive deployment of smart meters and improved metering infrastructure. Smart grids can also solve electricity theft problem by connecting information flows with energy flows. Data analysis on smart grid data is crucial in detecting power theft due to the unusual electricity usage pattern of energy thieves. Since they mostly rely on 1D energy consumption data and ignore electricity demand, current power theft detection systems have poor detection accuracy. We first present power theft detection, a method based on the Wide Convolutional Neural Network (CNN) and Deep Convolutional Neural Network models in this study, to address the mentioned problems. There are two sections to the Wide Convolutional Neural Network and Deep Convolutional Neural Network concepts. The wide Convolutional Neural Network component comes first, followed by the deep Convolutional Neural Network component. The Deep Convolutional Neural Network part describes periodic power use and non-periodic electricity theft. Data on 1D electricity usage characteristics recorded by the Wide component. As a result, the Wide and Deep Convolutional Neural Network model can accurately detect energy theft. Numerous investigations using primary data obtained demonstrate that the Wide and Deep Convolutional Neural Network model outperforms competing strategies.