Tackling energy theft in smart grids through data-driven analysis

Jindal, A., Marnerides, A. and Mauthe, A. (2020) Tackling energy theft in smart grids through data-driven analysis. In: International Conference on Computing, Networking and Communications (ICNC 2020)), Big Island, HI, USA, 17-20 February 2020, pp. 410-414. ISBN 9781728149059 (doi: 10.1109/ICNC47757.2020.9049793)

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The increasing use of information and communication technology (ICT) in electricity grid infrastructures facilitates improved energy generation, transmission, and distribution. However, smart grids are still in their infancy with a disparate regional role out. Due to the involved costs utility providers are only embedding ICT in selected parts of the grid, thereby creating only partial smart grid infrastructures. We argue that using the data provided by these partial smart grid deployments can still be beneficial in solving various issues such as energy theft detection. In this paper, we focus on various data-driven techniques to detect energy theft in power networks. These data-driven detection techniques (at the smart meter as well as the aggregated level) can indicate various forms of energy theft (e.g. through clandestine connections or meter tampering). This paper also presents two case studies to show the effectiveness of these approaches.

Item Type:Conference Proceedings
Additional Information:This work was supported by ProSeG - Information Security, Protection and Resilience in Smart Grids, a research project funded by MCTI/CNPq/CT-ENERG (Grant # 404958/2013-3). This work has also received funding from the EU’s Horizon 2020 research and innovation programme for “EASY-RES” project under grant agreement No 764090
Glasgow Author(s) Enlighten ID:Marnerides, Dr Angelos
Authors: Jindal, A., Marnerides, A., and Mauthe, A.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:2020 International Conference on Computing, Networking and Communications (ICNC)
Published Online:30 March 2020
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