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Tackling Energy Theft in Smart Grids through Data-driven Analysis

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Tackling Energy Theft in Smart Grids through Data-driven Analysis. / Jindal, Anish; Schaeffer-Filho, Alberto ; Marnerides, Angelos et al.

2020 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2020. p. 410-414.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Jindal, A, Schaeffer-Filho, A, Marnerides, A, Smith, P, Mauthe, A & Granville, L 2020, Tackling Energy Theft in Smart Grids through Data-driven Analysis. in 2020 International Conference on Computing, Networking and Communications (ICNC). IEEE, pp. 410-414, IEEE ICNC 2020 : IEEE International Conference on Computing, Networking & Communications 2020, Big Island, Hawaii, Hawaii, United States, 17/02/20. https://doi.org/10.1109/ICNC47757.2020.9049793

APA

Jindal, A., Schaeffer-Filho, A., Marnerides, A., Smith, P., Mauthe, A., & Granville, L. (2020). Tackling Energy Theft in Smart Grids through Data-driven Analysis. In 2020 International Conference on Computing, Networking and Communications (ICNC) (pp. 410-414). IEEE. https://doi.org/10.1109/ICNC47757.2020.9049793

Vancouver

Jindal A, Schaeffer-Filho A, Marnerides A, Smith P, Mauthe A, Granville L. Tackling Energy Theft in Smart Grids through Data-driven Analysis. In 2020 International Conference on Computing, Networking and Communications (ICNC). IEEE. 2020. p. 410-414 doi: 10.1109/ICNC47757.2020.9049793

Author

Jindal, Anish ; Schaeffer-Filho, Alberto ; Marnerides, Angelos et al. / Tackling Energy Theft in Smart Grids through Data-driven Analysis. 2020 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2020. pp. 410-414

Bibtex

@inproceedings{fb298deebcf5428dbb56afbd05111c65,
title = "Tackling Energy Theft in Smart Grids through Data-driven Analysis",
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.",
author = "Anish Jindal and Alberto Schaeffer-Filho and Angelos Marnerides and Paul Smith and Andreas Mauthe and Lisandro Granville",
note = "{\textcopyright}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. ; IEEE ICNC 2020 : IEEE International Conference on Computing, Networking & Communications 2020 ; Conference date: 17-02-2020",
year = "2020",
month = mar,
day = "30",
doi = "10.1109/ICNC47757.2020.9049793",
language = "English",
pages = "410--414",
booktitle = "2020 International Conference on Computing, Networking and Communications (ICNC)",
publisher = "IEEE",

}

RIS

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

SP - 410

EP - 414

BT - 2020 International Conference on Computing, Networking and Communications (ICNC)

PB - IEEE

T2 - IEEE ICNC 2020 : IEEE International Conference on Computing, Networking & Communications 2020

Y2 - 17 February 2020

ER -