IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1907-d1068657.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1907/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1907/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nadeem, Ahmad & Arshad, Naveed, 2021. "A data-driven approach to reduce electricity theft in developing countries," Utilities Policy, Elsevier, vol. 73(C).
    2. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    3. Gianfranco Di Lorenzo & Erika Stracqualursi & Rodolfo Araneo, 2022. "The Journey Towards the Energy Transition: Perspectives from the International Conference on Environment and Electrical Engineering (EEEIC)," Energies, MDPI, vol. 15(18), pages 1-5, September.
    4. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    5. Sufian A. Badawi & Djamel Guessoum & Isam Elbadawi & Ameera Albadawi, 2022. "A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies," Mathematics, MDPI, vol. 10(11), pages 1-16, May.
    6. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    7. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
    8. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    9. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    10. Gao, Bixuan & Kong, Xiangyu & Li, Shangze & Chen, Yi & Zhang, Xiyuan & Liu, Ziyu & Lv, Weijia, 2024. "Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach," Applied Energy, Elsevier, vol. 353(PB).
    11. Moez Krichen & Yasir Basheer & Saeed Mian Qaisar & Asad Waqar, 2023. "A Survey on Energy Storage: Techniques and Challenges," Energies, MDPI, vol. 16(5), pages 1-29, February.
    12. 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.
    13. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    14. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
    15. Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Ma, Zhenjun, 2023. "A new data mining strategy for performance evaluation of a shared energy recovery system integrated with data centres and district heating networks," Energy, Elsevier, vol. 285(C).
    16. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    17. Faheem Haroon & Muhammad Aamir & Asad Waqar & Saeed Mian Qaisar & Syed Umaid Ali & Abdulaziz Turki Almaktoom, 2022. "A Composite Exponential Reaching Law Based SMC with Rotating Sliding Surface Selection Mechanism for Two Level Three Phase VSI in Vehicle to Load Applications," Energies, MDPI, vol. 16(1), pages 1-20, December.
    18. Daniel Leite & José Pessanha & Paulo Simões & Rodrigo Calili & Reinaldo Souza, 2020. "A Stochastic Frontier Model for Definition of Non-Technical Loss Targets," Energies, MDPI, vol. 13(12), pages 1-20, June.
    19. Hoon Lee & Jin-Wook Kang & Bong-Yeon Choi & Kyung-Min Kang & Mi-Na Kim & Chang-Gyun An & Junsin Yi & Chung-Yuen Won, 2021. "Energy Management System of DC Microgrid in Grid-Connected and Stand-Alone Modes: Control, Operation and Experimental Validation," Energies, MDPI, vol. 14(3), pages 1-26, January.
    20. Martin Besfamille & Nicolás Figueroa & León Guzmán, 2023. "Ramsey pricing revisited: Natural monopoly regulation with evaders," Documentos de Trabajo 576, Instituto de Economia. Pontificia Universidad Católica de Chile..

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1907-:d:1068657. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.