| Autor: |
Tanwar S; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India., Kumari A; Institute of Computer Technology, Ganpat University, Ahmedabad 384012, India., Vekaria D; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India., Raboaca MS; National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm. Valcea, Uz-Inei Street, No. 4, Raureni, P.O. Box 7, 240050 Rm. Valcea, Romania., Alqahtani F; Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia., Tolba A; Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia., Neagu BC; Department of Power Engineering, 'Gheorghe Asachi' Technical University of Iasi, 700050 Iasi, Romania., Sharma R; Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun 248007, India. |
| Abstrakt: |
Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb , which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme ( GrAb ) identifies energy theft more accurately compared to the state-of-the-art approaches. |
