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An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities

Author

Listed:
  • Muhammad Salman Saeed

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
    Multan Electric Power Company, Multan 60000, Pakistan)

  • Mohd Wazir Mustafa

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia)

  • Usman Ullah Sheikh

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia)

  • Touqeer Ahmed Jumani

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia
    Mehran University of Engineering and Technology SZAB Campus, Khairpur Mirs 66020, Pakistan)

  • Ilyas Khan

    (Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam)

  • Samer Atawneh

    (College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Nawaf N. Hamadneh

    (Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

Abstract

Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers’ consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson’s chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms.

Suggested Citation

  • Muhammad Salman Saeed & Mohd Wazir Mustafa & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Ilyas Khan & Samer Atawneh & Nawaf N. Hamadneh, 2020. "An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities," Energies, MDPI, vol. 13(12), pages 1-19, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3242-:d:375089
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    References listed on IDEAS

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    1. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    2. Kessides, Ioannis N., 2013. "Chaos in power: Pakistan's electricity crisis," Energy Policy, Elsevier, vol. 55(C), pages 271-285.
    3. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Smart meters for power grid: Challenges, issues, advantages and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2736-2742, August.
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    Cited by:

    1. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    2. Vanessa Gindri Vieira & Daniel Pinheiro Bernardon & Vinícius André Uberti & Rodrigo Marques de Figueiredo & Lucas Melo de Chiara & Juliano Andrade Silva, 2023. "Detection of Non-Technical Losses in Irrigant Consumers through Artificial Intelligence: A Pilot Study," Energies, MDPI, vol. 16(19), pages 1-17, September.
    3. Alaa M. Elsayad & Ahmed M. Nassef & Mujahed Al-Dhaifallah & Khaled A. Elsayad, 2020. "Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
    4. Muhammad Salman Saeed & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Nawa A. Alshammari & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Saifulnizam Bin Abd Khalid & Ilyas Khan, 2020. "Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review," Energies, MDPI, vol. 13(18), pages 1-25, September.

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