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A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies

Author

Listed:
  • Sufian A. Badawi

    (Department of Computing, School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Djamel Guessoum

    (Electrical Engineering Department, Ecole de Technologie Superieure, Montreal, QC H3C 1K3, Canada)

  • Isam Elbadawi

    (Industrial Engineering Department, College of Engineering, University of Hail, Ha’il 81481, Saudi Arabia)

  • Ameera Albadawi

    (Department of Analytics in the Digital Era, College of Business and Economics, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

Abstract

Several approaches have been proposed to detect any malicious manipulation caused by electricity fraudsters. Some of the significant approaches are Machine Learning algorithms and data-based methods that have shown advantages compared to the traditional methods, and they are becoming predominant in recent years. In this study, a novel method is introduced to detect the fraudulent NTL loss in the smart grids in a two-stage detection process. In the first stage, the time-series readings are enriched by adding a new set of extracted features from the detection of sudden Jump patterns in the electricity consumption and the Autoregressive Integrated moving average (ARIMA). In the second stage, the distributed random forest (DRF) generates the learned model. The proposed model is applied to the public SGCC dataset, and the approach results have reported 98% accuracy and F1-score. Such results outperform the other recently reported state-of-the-art methods for NTL detection that are applied to the same SGCC dataset.

Suggested Citation

  • Sufian A. Badawi & Djamel Guessoum & Isam Elbadawi & Ameera Albadawi, 2022. "A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies," Mathematics, MDPI, vol. 10(11), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1878-:d:828024
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    References listed on IDEAS

    as
    1. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    2. Muhammad Salman Saeed & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Nawa A. Alshammari & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Saifulnizam Bin Abd Khalid & Ilyas Khan, 2020. "Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review," Energies, MDPI, vol. 13(18), pages 1-25, September.
    3. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
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