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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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    8. 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.
    9. 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).
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    13. 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.
    14. 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.
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