Tackling Energy Theft in Smart Grids through Data-driven Analysis

Jindal, Anish and Schaeffer-Filho, Alberto and Marnerides, Angelos and Smith, Paul and Mauthe, Andreas and Granville, Lisandro (2020) Tackling Energy Theft in Smart Grids through Data-driven Analysis. In: 2020 International Conference on Computing, Networking and Communications, ICNC 2020 :. 2020 International Conference on Computing, Networking and Communications, ICNC 2020 . IEEE, USA, pp. 410-414. ISBN 9781728149059

Text (1570584803 (2))
1570584803_2_.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (1MB)

Abstract

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 datadriven 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.