Electricity Theft Detection System Using Cloud Computing and Deep Learning Techniques

Abstract

Electricity theft is a widespread problem with significant economic implications. In this paper, we present an efficient system to detect electricity theft using cloud computing and deep learning techniques, specifically convolutional neural networks (CNNs) due to its ability to effectively extract features from electrical data. To address the challenge of imbalanced data, we tested various data preprocessing techniques. The adaptive synthetic (ADASYN) technique was selected to handle data imbalance. The evaluation of our CNN model demonstrates its effectiveness in accurately detecting incidents of theft, where our model achieved an accuracy of 97.22%, with a precision of 97% and a recall of 99.9%. The system was tested under real-life conditions and proved to be effective. Furthermore, cloud computing techniques have greatly facilitated the dissemination of CNN models by providing storage for data and a computational space for executing the model, as well as presenting the results to the authorities for electricity theft detection. The use of cloud servers has greatly simplified the distribution and utilization of CNN models in the context of electricity theft detection.