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A new clustering-based semi-supervised method to restrict the users from anomalous electricity consumption: supporting urbanization

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Abstract

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features’ associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism; clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications.

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Acknowledgements

The authors would like to acknowledge the support of Researchers Supporting Project Number (RSP2024R295), King Saud University, Riyadh, Saudi Arabia.

Funding

This work was supported by King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project number (RSP2024R295).

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Authors and Affiliations

  1. Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, TN, 37996, USA

    Zeeshan Aslam

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

    Nadeem Javaid & Muhammad Umar Javed

  3. International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan

    Nadeem Javaid

  4. Department of Artificial Intelligence, Sejong University, Seoul, 05006, South Korea

    Muhammad Aslam

  5. Department of Urban Planning, College of Architecture and Planning, King Saud University, 11574, Riyadh, Saudi Arabia

    Abdulaziz Aldegheishem

  6. Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, 11633, Riyadh, Saudi Arabia

    Nabil Alrajeh

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  1. Zeeshan Aslam
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  2. Nadeem Javaid
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  4. Muhammad Aslam
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Contributions

Z.A. and M.U.J. wrote the original draft; N.J. and M.A. performed supervision; A.A and N.A. performed conceptualization; Z.A., A.A. and M.A. performed simulations; N.J., M.U.J., N.A. and M.A. performed proofreading; A.A. performed funding acquisition and project management.

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Correspondence to Nadeem Javaid.

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Aslam, Z., Javaid, N., Javed, M.U. et al. A new clustering-based semi-supervised method to restrict the users from anomalous electricity consumption: supporting urbanization. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02362-3

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