GrAb : A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection

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 a...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-05, Vol.22 (11), p.4048
Main Authors: Tanwar, Sudeep, Kumari, Aparna, Vekaria, Darshan, Raboaca, Maria Simona, Alqahtani, Fayez, Tolba, Amr, Neagu, Bogdan-Constantin, Sharma, Ravi
Format: Article
Language:English
Subjects:
Algorithms
Classification
Datasets
Deep learning
demand response management
Electric power grids
Electricity
Electricity distribution
Energy consumption
energy consumption prediction
Energy industry
Energy management
energy theft
Limited liability companies
LSTM
Regression analysis
Robbery
smart grid
Support vector machines
Theft
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Summary: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 , which uses a data-driven analytics approach. 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 ( ) identifies energy theft more accurately compared to the state-of-the-art approaches.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22114048