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Volume 9 Issue 4
Apr.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(4): 578-600
M. Ahmed, A. Khan, M. Ahmed, M. Tahir, G. Jeon, G. Fortino, and F. Piccialli, “Energy theft detection in smart grids: Taxonomy, comparative analysis, challenges, and future research directions,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 578–600, Apr. 2022. doi: 10.1109/JAS.2022.105404
Citation: M. Ahmed, A. Khan, M. Ahmed, M. Tahir, G. Jeon, G. Fortino, and F. Piccialli, “Energy theft detection in smart grids: Taxonomy, comparative analysis, challenges, and future research directions,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 578–600, Apr. 2022. doi: 10.1109/JAS.2022.105404
shu

Energy Theft Detection in Smart Grids: Taxonomy, Comparative Analysis, Challenges, and Future Research Directions

doi: 10.1109/JAS.2022.105404
  • 1.

    Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan

  • 2.

    Department of Computer Science, School of Computing, Engineering and Digital Technologies, Teesside University, Tees Valley TS1 3BX, United Kingdom

  • 3.

    Innovative Value Institute, Maynooth University, Maynooth W23 F2K8, Ireland

  • 4.

    Department of Computer Science, Bahria University, Lahore 54782, Pakistan

  • 5.

    School of Electronic Engineering, Xidian University, Xi’an 710071, Chin

  • 6.

    Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea

  • 7.

    Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, CS 87036, Italy

  • 8.

    Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Napoli 80138, Italy

Funds:  This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement (801522), Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology (13/RC/2106_P2)
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    Abstract
  • Electricity theft is one of the major issues in developing countries which is affecting their economy badly. Especially with the introduction of emerging technologies, this issue became more complicated. Though many new energy theft detection (ETD) techniques have been proposed by utilising different data mining (DM) techniques, state & network (S&N) based techniques, and game theory (GT) techniques. Here, a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations. Three levels of taxonomy are presented to classify state-of-the-art ETD techniques. Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature. The challenges of different ETD techniques and their mitigation are suggested for future work. It is observed that the literature on ETD lacks knowledge management techniques that can be more effective, not only for ETD but also for theft tracking. This can help in the prevention of energy theft, in the future, as well as for ETD.

     

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    Highlights

    • Electricity theft is one of the major issues in developing countries which is affecting their economy badly.
    • Though many new energy theft detection (ETD) techniques have been proposed by utilizing different data mining (DM) techniques, state & network (S&N) based techniques, and game theory (GT) techniques.
    • A detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.
    • Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.
    • Different types and ways of energy theft and their consequences are studied and summarized and different parameters to benchmark the performance of proposed techniques are extracted from literature.

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