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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Tackling Energy Theft in Smart Grids through Data-driven Analysis
AU - Jindal, Anish
AU - Schaeffer-Filho, Alberto
AU - Marnerides, Angelos
AU - Smith, Paul
AU - Mauthe, Andreas
AU - Granville, Lisandro
N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2020/3/30
Y1 - 2020/3/30
N2 - 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.
AB - 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.
U2 - 10.1109/ICNC47757.2020.9049793
DO - 10.1109/ICNC47757.2020.9049793
M3 - Conference contribution/Paper
T3 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
SP - 410
EP - 414
BT - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
PB - IEEE
T2 - IEEE ICNC 2020 : IEEE International Conference on Computing, Networking & Communications 2020
Y2 - 17 February 2020
ER -