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. 2022 May 26;22(11):4048.
doi: 10.3390/s22114048.

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

Sudeep Tanwar  1 , Aparna Kumari  2 , Darshan Vekaria  1 , Maria Simona Raboaca  3 , Fayez Alqahtani  4 , Amr Tolba  5 , Bogdan-Constantin Neagu  6 , Ravi Sharma  7
Affiliations

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

Sudeep Tanwar et al. Sensors (Basel). .

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.

Keywords: LSTM; deep learning; demand response management; energy consumption prediction; energy theft; smart grid.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Smart Grid Market Growth [6].
Figure 2
Figure 2
Energy Growth Forecasting Using Deep Learning.
Figure 3
Figure 3
GrAb System Model.
Figure 4
Figure 4
GrAb system architecture.
Figure 5
Figure 5
RMSE values for devices using GrAb.
Figure 6
Figure 6
Comparison of the proposed GrAb model and state-of-art approaches. (a) Grab—RMSE Comparison. (b) Grab—MAPE Comparison.
Figure 7
Figure 7
Distribution of hours for deviation.
Figure 8
Figure 8
Energy loss prediction for AC.
Figure 9
Figure 9
Energy loss prediction for lights.
Figure 10
Figure 10
Energy Loss prediction for Basic Facilities.
Figure 11
Figure 11
Energy loss prediction for miscellaneous devices.
See this image and copyright information in PMC

References

    1. Kumari A., Tanwar S., Tyagi S., Kumar N., Obaidat M.S., Rodrigues J.J. Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions. IEEE Wirel. Commun. 2019;26:47–53. doi: 10.1109/MWC.2019.1800356. - DOI
    1. Belitz H.J., Winter S., Müller C., Langhammer N., Kays R., Wietfeld C., Rehtanz C. Technical and economic analysis of future smart grid applications in the E-DeMa project; Proceedings of the 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe); Berlin, Germany. 14–17 October 2012; pp. 1–8.
    1. Gagwari D., Bhatia R. A brief analysis of recent developments in smart grid; Proceedings of the 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES); Kozhikode, India. 19–21 February 2015; pp. 1–5.
    1. L1. [(accessed on 30 December 2019)]. Available online: http://deeplearning.net/tutorial/lstm.html.
    1. Espinoza J.L., Samaniego E., Jara-Alvear J., Ochoa T.D. Smart grids: A multi-scale framework of analysis; Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America); Quito, Ecuador. 20–22 September 2017; pp. 1–6.

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