IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p4868-d1092176.html
   My bibliography  Save this article

Prevention and Detection of Electricity Theft of Distribution Network

Author

Listed:
  • Sajad Ali

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Min Yongzhi

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Wajid Ali

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

Electricity theft is a costly problem. This paper will be focused on Pakistan and the problem of electricity theft. We will discuss its impacts and how best to fix them through the use of technology. For this purpose, we developed a smart meter, focusing on grid modernization through economic smart meter development. This paper focuses on a study carried out with the help of PESCO. It is one of the most inefficient distribution providers. The study has evaluated commercial, industrial, rural, and urban areas, covering a total area of 15 km 2 . The area includes several power sinks. Previous research has been used to compare the results of this case study; this included studies of other Third World countries, such as Pakistan and South Africa. The design of, clever, innovative, intelligent meters used in this study was better than the basic digital meters and had many features compatible with the E.U., and U.S.A.’s western power market and energy infrastructure. The study also discusses the potential use of neural network-trained models and IoT (internet of things) integration with cloud computing. This can provide an alternate means of data analysis, accurate prediction, and greater user accessibility. The case study is the first ever done using smart meters on such a large scale, and the compiled data has provided insight into energy consumers and their usage. The statistics can be used to isolate the most probable cause of theft and the area or location of occurrence.

Suggested Citation

  • Sajad Ali & Min Yongzhi & Wajid Ali, 2023. "Prevention and Detection of Electricity Theft of Distribution Network," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4868-:d:1092176
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/4868/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/4868/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Faisal Mohammad & Young-Chon Kim, 2020. "Energy load forecasting model based on deep neural networks for smart grids," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 824-834, August.
    2. Anna Cretì & Fulvio Fontini, 2019. "Economics of Electricity. Markets, Competition and Rules," Post-Print hal-02304345, HAL.
    3. Faisal Jamil & Eatzaz Ahmad, 2014. "An Empirical Study of Electricity Theft from Electricity Distribution Companies in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 53(3), pages 239-254.
    4. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    5. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
    6. Adongo, Charles Atanga & Taale, Francis & Bukari, Shaibu & Suleman, Shafic & Amadu, Iddrisu, 2021. "Electricity theft whistleblowing feasibility in commercial accommodation facilities," Energy Policy, Elsevier, vol. 155(C).
    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. Yiran Wang & Shuowei Jin & Ming Cheng, 2023. "A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection," Sustainability, MDPI, vol. 15(13), pages 1-22, June.

    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. Wabukala, Benard M. & Mukisa, Nicholas & Watundu, Susan & Bergland, Olvar & Rudaheranwa, Nichodemus & Adaramola, Muyiwa S., 2023. "Impact of household electricity theft and unaffordability on electricity security: A case of Uganda," Energy Policy, Elsevier, vol. 173(C).
    2. Babar, Zainab & Jamil, Faisal & Haq, Wajiha, 2022. "Consumer's perception towards electricity theft: A case study of Islamabad and Rawalpindi using a path analysis," Energy Policy, Elsevier, vol. 169(C).
    3. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    4. Ahmad, Hafsa & Jamil, Faisal, 2024. "Investigating power outages in Pakistan," Energy Policy, Elsevier, vol. 189(C).
    5. 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.
    6. Gautier, Axel & Nsabimana, René & Walheer, Barnabé, 2023. "Quality performance gaps and minimal electricity losses in East Africa," Utilities Policy, Elsevier, vol. 82(C).
    7. Stracqualursi, Erika & Rosato, Antonello & Di Lorenzo, Gianfranco & Panella, Massimo & Araneo, Rodolfo, 2023. "Systematic review of energy theft practices and autonomous detection through artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    8. Nadeem, Ahmad & Arshad, Naveed, 2021. "A data-driven approach to reduce electricity theft in developing countries," Utilities Policy, Elsevier, vol. 73(C).
    9. Hugo Brise o & Omar Rojas, 2020. "Factors Associated with Electricity Losses: A Panel Data Perspective," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 281-286.
    10. 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).
    11. Yakubu, Osman & Babu C., Narendra & Adjei, Osei, 2018. "Electricity theft: Analysis of the underlying contributory factors in Ghana," Energy Policy, Elsevier, vol. 123(C), pages 611-618.
    12. Jamil, Faisal, 2018. "Electricity theft among residential consumers in Rawalpindi and Islamabad," Energy Policy, Elsevier, vol. 123(C), pages 147-154.
    13. Hugo Brise o & Omar Rojas, 2020. "Factors Associated with Electricity Theft in Mexico," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 250-254.
    14. Arkorful, Vincent Ekow, 2022. "Unravelling electricity theft whistleblowing antecedents using the theory of planned behavior and norm activation model," Energy Policy, Elsevier, vol. 160(C).
    15. Adongo, Charles Atanga & Taale, Francis & Bukari, Shaibu & Suleman, Shafic & Amadu, Iddrisu, 2021. "Electricity theft whistleblowing feasibility in commercial accommodation facilities," Energy Policy, Elsevier, vol. 155(C).
    16. Hugo Brise o & Jessica Rubiano & Rodolfo Garc a & Omar Rojas, 2021. "Factors Associated with Electricity Losses in Colombia," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 465-470.
    17. Fernando de Souza Savian & Julio Cezar Mairesse Siluk & Tai s Bisognin Garlet & Felipe Moraes do Nascimento & Jose Renes Pinheiro & Zita Vale, 2022. "Non-technical Losses in Brazil: Overview, Challenges, and Directions for Identification and Mitigation," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 93-107, May.
    18. Imam, M. & Jamasb, T. & Llorca, M. & Llorca, M., 2018. "Power Sector Reform and Corruption: Evidence from Electricity Industry in Sub-Saharan Africa," Cambridge Working Papers in Economics 1801, Faculty of Economics, University of Cambridge.
    19. Marco Toledo-Orozco & Carlos Arias-Marin & Carlos Álvarez-Bel & Diego Morales-Jadan & Javier Rodríguez-García & Eddy Bravo-Padilla, 2021. "Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities," Energies, MDPI, vol. 14(4), pages 1-23, February.
    20. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.

    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:jsusta:v:15:y:2023:i:6:p:4868-:d:1092176. 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.