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Ensemble-Learning-Based Decision Support System for Energy-Theft Detection in Smart-Grid Environment

Author

Listed:
  • Farah Mohammad

    (Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 12372, Saudi Arabia)

  • Kashif Saleem

    (College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11461, Saudi Arabia)

  • Jalal Al-Muhtadi

    (Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 12372, Saudi Arabia
    College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11461, Saudi Arabia)

Abstract

Theft of electricity poses a significant risk to the public and is the most costly non-technical loss for an electrical supplier. In addition to affecting the quality of the energy supply and the strain on the power grid, fraudulent electricity use drives up prices for honest customers and creates a ripple effect on the economy. Using data-analysis tools, smart grids may drastically reduce this waste. Smart-grid technology produces much information, including consumers’ unique electricity-use patterns. By analyzing this information, machine-learning and deep-learning methods may successfully pinpoint those who engage in energy theft. This study presents an ensemble-learning-based system for detecting energy theft using a hybrid approach. The proposed approach uses a machine-learning-based ensemble model based on a majority voting strategy. This work aims to develop a smart-grid information-security decision support system. This study employed a theft-detection dataset to facilitate automatic theft recognition in a smart-grid environment (TDD2022). The dataset consists of six separate electricity thefts. The experiments are performed in four different scenarios. The proposed machine-learning-based ensemble model obtained significant results in all scenarios. The proposed ensemble model obtained the highest accuracy of 88%, 87.24%, 94.75%, and 94.70% with seven classes including the consumer type, seven classes excluding the consumer type, six classes including the consumer type, and six classes excluding the consumer type. The suggested ensemble model outperforms the existing techniques in terms of accuracy when the proposed methodology is compared to state-of-the-art approaches.

Suggested Citation

  • Farah Mohammad & Kashif Saleem & Jalal Al-Muhtadi, 2023. "Ensemble-Learning-Based Decision Support System for Energy-Theft Detection in Smart-Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1907-:d:1068657
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    References listed on IDEAS

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    1. Yurtseven, Çağlar, 2015. "The causes of electricity theft: An econometric analysis of the case of Turkey," Utilities Policy, Elsevier, vol. 37(C), pages 70-78.
    2. 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.
    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. Saeed Mian Qaisar, 2020. "Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge," Energies, MDPI, vol. 13(21), pages 1-20, October.
    5. 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.
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    Cited by:

    1. Qinyu Huang & Zhenli Tang & Xiaofeng Weng & Min He & Fang Liu & Mingfa Yang & Tao Jin, 2024. "A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods," Energies, MDPI, vol. 17(2), pages 1-18, January.

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