Detection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models

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dc.contributor.authorOtuoze, A. O.
dc.contributor.authorMustafa, M. W
dc.contributor.authorSultana, U.
dc.contributor.authorAbiodun, E. A.
dc.contributor.authorJimada-Ojuolape, B.
dc.contributor.authorIbrahim, O.
dc.contributor.authorAvazi-OmeizaI, O.
dc.contributor.authorAbdullateef , A. I.
dc.date.accessioned2026-05-11T10:04:43Z
dc.date.available2026-05-11T10:04:43Z
dc.date.issued2024
dc.description.abstractThe successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.
dc.identifier.citationOtuoze, A. O., Mustafa, M. W., Sultana, U., Abiodun, E. A., Jimada-Ojuolape, B., Ibrahim, O., Omeiza, I. O. A. & Abdullateef, A. I. (2024). Detection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models. Nigerian Journal of Technological Development, 21 (1), 112-130. Published by Faculty of Engineering and Technology University of Ilorin.
dc.identifier.issn2437-2110
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/18267
dc.publisherFaculty of Engineering and Technology, University of Ilorin
dc.subjectAdvanced metering infrastructure
dc.subjectAnomaly detection
dc.subjectConfirmation model
dc.subjectElectricity theft detection
dc.subjectFuzzy inference system
dc.subjectLong short-term memory
dc.titleDetection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models

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