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Non-technical loss analysis and prevention using smart meters

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  • 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
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    Cited by:

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    3. 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.
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    5. 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).
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    8. 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.
    9. 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).
    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. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.
    18. 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).
    19. 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.

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