Please use this identifier to cite or link to this item:
http://sgc.anlis.gob.ar/handle/123456789/2553
Title: | GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection | Authors: | Tanwar, Sudeep Kumari, Aparna Vekaria, Darshan Raboaca, Maria Simona Alqahtani, Fayez Tolba, Amr Neagu, Bogdan-Constantin Sharma, Ravi |
Keywords: | Memoria a Corto Plazo;Aprendizaje Profundo;Predicción;Consumo de Energía | Issue Date: | 26-May-2022 | Journal: | Sensors (Basel, Switzerland) | Series/Report no.: | Sensors (Basel);22(11)2022:4048 | Abstract: | 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. |
Description: | Fil: Tanwar, Sudeep. Department of Computer Science and Engineering, Institute of Technology, Nirma University; India Fil: Kumari, Aparna. Institute of Computer Technology, Ganpat University; India Fil: Vekaria, Darshan. Department of Computer Science and Engineering, Institute of Technology, Nirma University; India Fil: Raboaca, Maria Simona. National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm; Romania Fil: Alqahtani, Fayez. Software Engineering Department, College of Computer and Information Sciences, King Saud University; Saudi Arabia Fil: Tolba, Amr. Computer Science Department, Community College, King Saud University; Saudi Arabia Fil: Neagu, Bogdan-Constantin. Department of Power Engineering, "Gheorghe Asachi" Technical University of Iasi; Romania Fil: Sharma, Ravi. Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies; India |
URI: | http://sgc.anlis.gob.ar/handle/123456789/2553 | DOI: | 10.3390/s22114048 |
Appears in Collections: | Artículos |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.