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A practical feature-engineering framework for electricity theft detection in smart grids

Author

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  • Razavi, Rouzbeh
  • Gharipour, Amin
  • Fleury, Martin
  • Akpan, Ikpe Justice

Abstract

Despite many potential advantages, Advanced Metering Infrastructures have introduced new ways to falsify meter readings and commit electricity theft. This study contributes a new model-agnostic, feature-engineering framework for theft detection in smart grids. The framework introduces a combination of Finite Mixture Model clustering for customer segmentation and a Genetic Programming algorithm for identifying new features suitable for prediction. Utilizing demand data from more than 4000 households, a Gradient Boosting Machine algorithm is applied within the framework, significantly outperforming the results of prior machine-learning, theft-detection methods. This study further examines some important practical aspects of deploying theft detection including: the detection delay; the required size of historical demand data; the accuracy in detecting thefts of various types and intensity; detecting irregular and unseen attacks; and the computational complexity of the detection algorithm.

Suggested Citation

  • Razavi, Rouzbeh & Gharipour, Amin & Fleury, Martin & Akpan, Ikpe Justice, 2019. "A practical feature-engineering framework for electricity theft detection in smart grids," Applied Energy, Elsevier, vol. 238(C), pages 481-494.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:481-494
    DOI: 10.1016/j.apenergy.2019.01.076
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    Cited by:

    1. Farah Mohammad & Kashif Saleem & Jalal Al-Muhtadi, 2023. "Ensemble-Learning-Based Decision Support System for Energy-Theft Detection in Smart-Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
    2. Nadeem, Ahmad & Arshad, Naveed, 2021. "A data-driven approach to reduce electricity theft in developing countries," Utilities Policy, Elsevier, vol. 73(C).
    3. Pamir & Nadeem Javaid & Saher Javaid & Muhammad Asif & Muhammad Umar Javed & Adamu Sani Yahaya & Sheraz Aslam, 2022. "Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit," Energies, MDPI, vol. 15(8), pages 1-20, April.
    4. 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.
    5. Rubén González Rodríguez & Jamer Jiménez Mares & Christian G. Quintero M., 2020. "Computational Intelligent Approaches for Non-Technical Losses Management of Electricity," Energies, MDPI, vol. 13(9), pages 1-25, May.
    6. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    7. Benish Kabir & Umar Qasim & Nadeem Javaid & Abdulaziz Aldegheishem & Nabil Alrajeh & Emad A. Mohammed, 2022. "Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    8. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2021. "Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid," Energy, Elsevier, vol. 220(C).
    10. Yang, Kaixiang & Chen, Wuxing & Bi, Jichao & Wang, Mengzhi & Luo, Fengji, 2023. "Multi-view broad learning system for electricity theft detection," Applied Energy, Elsevier, vol. 352(C).
    11. Otuoze, Abdulrahaman Okino & Mustafa, Mohd Wazir & Abdulrahman, Abdulhakeem Temitope & Mohammed, Olatunji Obalowu & Salisu, Sani, 2020. "Penalization of electricity thefts in smart utility networks by a cost estimation-based forced corrective measure," Energy Policy, Elsevier, vol. 143(C).
    12. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
    13. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    14. Rui Xia & Yunpeng Gao & Yanqing Zhu & Dexi Gu & Jiangzhao Wang, 2022. "An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis," Energies, MDPI, vol. 15(19), pages 1-25, October.

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