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Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Author

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  • Rehan Akram

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Nasir Ayub

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
    School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad 44000, Pakistan
    These authors contributed equally to this work.)

  • Imran Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
    These authors contributed equally to this work.)

  • Fahad R. Albogamy

    (Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 26571, Saudi Arabia
    These authors contributed equally to this work.)

  • Gul Rukh

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
    These authors contributed equally to this work.)

  • Sheraz Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Shiraz

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
    Department of Computer Science, Allama Iqbal Open University, Islamabad 44000, Pakistan)

  • Kashif Rizwan

    (Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan)

Abstract

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

Suggested Citation

  • Rehan Akram & Nasir Ayub & Imran Khan & Fahad R. Albogamy & Gul Rukh & Sheraz Khan & Muhammad Shiraz & Kashif Rizwan, 2021. "Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid," Energies, MDPI, vol. 14(23), pages 1-17, December.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8029-:d:692815
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    References listed on IDEAS

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    1. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft," Energy Policy, Elsevier, vol. 39(2), pages 1007-1015, February.
    2. John Creedy, 2016. "Interpreting inequality measures and changes in inequality," New Zealand Economic Papers, Taylor & Francis Journals, vol. 50(2), pages 177-192, August.
    3. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
    4. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
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    Cited by:

    1. Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    2. Taha Selim Ustun, 2022. "Power Systems Imitate Nature for Improved Performance Use of Nature-Inspired Optimization Techniques," Energies, MDPI, vol. 15(17), pages 1-2, August.
    3. Nasir Ayub & Usman Ali & Kainat Mustafa & Syed Muhammad Mohsin & Sheraz Aslam, 2022. "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid," Forecasting, MDPI, vol. 4(4), pages 1-13, November.

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