MACHINE LEARNING FOR ANOMALY DETECTION IN SMART GRID ENERGY CONSUMPTION: A ONE-CLASS SVM APPROACH

Authors

  • Agbo, E.R

    The Federal University of Technology, Akure, Nigeria

  • Olajide, I.A

    The Federal University of Technology, Akure, Nigeria

  • Itodo, E.S

    The Federal University of Technology, Akure, Nigeria

  • Faleye, O.P

    Afe Babalola University, Ado-Ekiti, Nigeria

DOI:
https://doi.org/10.51459/futajeet.2025.19.1.481
Keywords:
Energy monitoring, Energy meter, Energy theft detection, One-Class Support Vector Machine
Abstract

Energy monitoring holds significant implications for sustainability, cost-efficiency, and energy security in Energy usage. In this paper, the One-Class Support Vector Machine model (OCSVM) was employed to monitor energy usage. The system collected real-time data on voltage, current, power, and other energy parameters from a residential apartment over one month. Advanced data analytics provided useful information into consumption patterns. The OCSVM model was trained to identify anomalies indicative of potential energy/electricity theft. The implemented system effectively acquired real-time electrical data, enabling analysis of peak usage times, recurring trends, and parameter correlations. The trained OCSVM model exhibited a precision of 0.9525, recall of 0.9441, and F1 score of 0.948 in detecting energy consumption anomalies, thereby demonstrating its effectiveness in energy theft detection.

Author Biographies
  1. Agbo, E.R, The Federal University of Technology, Akure, Nigeria

    Department of Electrical and Electronics Engineering, 

  2. Olajide, I.A, The Federal University of Technology, Akure, Nigeria

    Department of Electrical and Electronics Engineering

  3. Itodo, E.S, The Federal University of Technology, Akure, Nigeria

    Department of Electrical and Electronics Engineering

  4. Faleye, O.P, Afe Babalola University, Ado-Ekiti, Nigeria

    Department of Electrical, Electronics and Computer Engineering

     

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Published
2025-05-30
Issue
Vol. 19 No. 1 (2025): FUTA Journal of Engineering and Engineering Technology
Section
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How to Cite

MACHINE LEARNING FOR ANOMALY DETECTION IN SMART GRID ENERGY CONSUMPTION: A ONE-CLASS SVM APPROACH. (2025). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 19(1), 208-215. https://doi.org/10.51459/futajeet.2025.19.1.481

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