IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v72y2017icp573-589.html
   My bibliography  Save this article

Non-technical loss analysis and prevention using smart meters

Author

Listed:
  • Ahmad, Tanveer

Abstract

In the countries such as Pakistan, for analyzing the losses and techniques in the power distribution and for mitigating, are the two active areas of research which is spread globally for increasing the accessibility of power irrespective of installing future generation equipment. As, the Technical Loss and the Non-Technical Loss are accounted by the total energy losses. They both are also referred as TL and NTLs. In terms of the non-technical losses there are major financial losses for the utility companies present in the countries that are in the developing stage. NTLs is the major cause for the additional losses and also it includes the part of damaging the network that includes the infrastructure and network reliability reduction. This paper is subjected to investigating the non-technical losses in terms of the power distribution systems. In addition to that, the consumer energy consumption information is used for analyzing the NTLs from Rawalpindi region from the different feeder source. The data of Low Voltage (LV) of the distribution network are focused more that consists of commercial, industrial, residential and agricultural consumers by the use of KWh interval data which is captured over a month using the smart meter infrastructure. The discussion of this review paper determines analysis and prevention techniques of NTLs to safeguard from the illegal use of the electricity in the distribution of electrical power system.

Suggested Citation

  • Ahmad, Tanveer, 2017. "Non-technical loss analysis and prevention using smart meters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 573-589.
    • Handle: RePEc:eee:rensus:v:72:y:2017:i:c:p:573-589
    DOI: 10.1016/j.rser.2017.01.100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032117300990
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2017.01.100?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Neaimeh, Myriam & Wardle, Robin & Jenkins, Andrew M. & Yi, Jialiang & Hill, Graeme & Lyons, Padraig F. & Hübner, Yvonne & Blythe, Phil T. & Taylor, Phil C., 2015. "A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts," Applied Energy, Elsevier, vol. 157(C), pages 688-698.
    2. 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.
    3. Neenan, Bernard & Hemphill, Ross C., 2008. "Societal Benefits of Smart Metering Investments," The Electricity Journal, Elsevier, vol. 21(8), pages 32-45, October.
    4. Daskalaki, S. & Kopanas, I. & Goudara, M. & Avouris, N., 2003. "Data mining for decision support on customer insolvency in telecommunications business," European Journal of Operational Research, Elsevier, vol. 145(2), pages 239-255, March.
    5. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
    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. 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).
    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. Zeeshan Aslam & Nadeem Javaid & Ashfaq Ahmad & Abrar Ahmed & Sardar Muhammad Gulfam, 2020. "A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-24, October.
    4. Simões, Paulo Fernando Mahaz & Souza, Reinaldo Castro & Calili, Rodrigo Flora & Pessanha, José Francisco Moreira, 2020. "Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    5. Rubén González Rodríguez & Jamer Jiménez Mares & Christian G. Quintero M., 2020. "Computational Intelligent Approaches for Non-Technical Losses Management of Electricity," Energies, MDPI, vol. 13(9), pages 1-25, May.
    6. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    7. 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).
    8. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
    9. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    10. Villar-Rodriguez, Esther & Del Ser, Javier & Oregi, Izaskun & Bilbao, Miren Nekane & Gil-Lopez, Sergio, 2017. "Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis," Energy, Elsevier, vol. 137(C), pages 118-128.
    11. 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).
    12. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    13. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
    14. 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).
    15. 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).
    16. 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.
    17. Akter, Sonia & Mathew, Nikhitha Mary & Fila, Marian Edward, 2023. "The impact of an improvement in the quality and reliability of rural residential electricity supply on clean cooking fuel adoption: Evidence from six energy poor Indian states," World Development, Elsevier, vol. 172(C).
    18. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.

    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. Costa-Campi, Maria Teresa & Daví-Arderius, Daniel & Trujillo-Baute, Elisa, 2018. "The economic impact of electricity losses," Energy Economics, Elsevier, vol. 75(C), pages 309-322.
    2. Rains, Emily & Abraham, Ronald J., 2018. "Rethinking barriers to electrification: Does government collection failure stunt public service provision?," Energy Policy, Elsevier, vol. 114(C), pages 288-300.
    3. Never, Babette, 2015. "Social norms, trust and control of power theft in Uganda: Does bulk metering work for MSEs?," Energy Policy, Elsevier, vol. 82(C), pages 197-206.
    4. Jamil, Faisal, 2013. "On the electricity shortage, price and electricity theft nexus," Energy Policy, Elsevier, vol. 54(C), pages 267-272.
    5. Furszyfer Del Rio, Dylan D. & Sovacool, Benjamin K., 2023. "Of cooks, crooks and slum-dwellers: Exploring the lived experience of energy and mobility poverty in Mexico's informal settlements," World Development, Elsevier, vol. 161(C).
    6. 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.
    7. Pless, Jacquelyn & Fell, Harrison, 2017. "Bribes, bureaucracies, and blackouts: Towards understanding how corruption at the firm level impacts electricity reliability," Resource and Energy Economics, Elsevier, vol. 47(C), pages 36-55.
    8. El Hage, Fabio S. & Rufín, Carlos, 2016. "Context analysis for a new regulatory model for electric utilities in Brazil," Energy Policy, Elsevier, vol. 97(C), pages 145-154.
    9. Viegas, Joaquim L. & Esteves, Paulo R. & Melício, R. & Mendes, V.M.F. & Vieira, Susana M., 2017. "Solutions for detection of non-technical losses in the electricity grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1256-1268.
    10. 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).
    11. Bhatt, Brijesh & Singh, Anoop, 2020. "Stakeholders’ role in distribution loss reduction technology adoption in the Indian electricity sector: An actor-oriented approach," Energy Policy, Elsevier, vol. 137(C).
    12. 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).
    13. Gideon Otchere-Appiah & Shingo Takahashi & Mavis Serwaa Yeboah & Yuichiro Yoshida, 2021. "The Impact of Smart Prepaid Metering on Non-Technical Losses in Ghana," Energies, MDPI, vol. 14(7), pages 1-16, March.
    14. Duncan Chaplin & Delia Welsh & Arif Mamun & Nick Ingwersen & Kristine Bos & Erin Crossett & Poonam Ravindranath & Dara Bernstein & William Derbyshire, "undated". "Ghana Power Compact: Evaluation Design Report," Mathematica Policy Research Reports 8c1896c6f9af45f08347287c1, Mathematica Policy Research.
    15. Darragh Carr & Murray Thomson, 2022. "Non-Technical Electricity Losses," Energies, MDPI, vol. 15(6), pages 1-14, March.
    16. Parikh, Priti & Chaturvedi, Sankalp & George, Gerard, 2012. "Empowering change: The effects of energy provision on individual aspirations in slum communities," Energy Policy, Elsevier, vol. 50(C), pages 477-485.
    17. 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.
    18. 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.
    19. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    20. 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).

    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:eee:rensus:v:72:y:2017:i:c:p:573-589. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.