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• W3013890012 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 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." @default.
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• W3013890012 date "2020-02-01" @default.
• W3013890012 modified "2023-10-05" @default.
• W3013890012 title "Tackling Energy Theft in Smart Grids through Data-driven Analysis" @default.
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• W3013890012 doi "https://doi.org/10.1109/icnc47757.2020.9049793" @default.
• W3013890012 hasPublicationYear "2020" @default.
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