Detection and Confirmation of Electricity Thefts in Advanced Metering Infrastructure by Long Short-Term Memory and Fuzzy Inference System Models


  • A. O. Otuoze Department of Electrical and Electronics Engineering, University of Ilorin, Ilorin, Nigeria.
  • M. W. Mustafa Department of Electrical Power Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • U. Sultana Department of Electrical Engineering, NED University of Engineering and Technology, Karachi, Pakistan.
  • E. A. Abiodun Department of Viticulture and Enology, Fresno State University, 2415 E. San Ramon, MS AS79, Fresno, California. 93740, United States of America.
  • B. Jimada-Ojuolape Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, Kwara State University, Malete, Nigeria
  • O. Ibrahim Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria.
  • I. O. Avazi-Omeiza Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria.
  • A. I. Abdullateef Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria.


Advanced Metering Infrastructure, Anomaly Detection, Confirmation Model, Electricity Theft Detection, Fuzzy Inference System, Long Short-Term Memory, Advanced metering infrastructure, Anomaly detection, Confirmation model, Electricity theft detection, Fuzzy inference system, Long short-term memory


The 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.


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