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Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions

Author

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  • Eduardo Correia

    (Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22453-900, Brazil)

  • Rodrigo Calili

    (Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22453-900, Brazil)

  • José Francisco Pessanha

    (Institute of Mathematics and Statistics, Rio de Janeiro State University, Rio de Janeiro 20550-000, Brazil)

  • Maria Fatima Almeida

    (Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22453-900, Brazil)

Abstract

Non-technical losses (NTLs) are one of the main problems that electricity distribution utilities face in developing regions such as Latin America, the Caribbean, sub-Saharan Africa, and South Asia. Particularly in Brazil, based on the socioeconomic and market variables concerning all the distribution utilities, the National Electric Energy Agency (ANEEL) has formulated several specifications of econometric models for panel data with random effects, all aimed at determining an index that reflects the difficulty of combating NTLs according to the intrinsic characteristics of each distribution area. Nevertheless, given the exhaustive search for combinations of explanatory variables and the complexity inherent to defining regulatory NTL targets, this process still requires the evaluation of many models through hypothesis and goodness-of-fit tests. In this regard, this article proposes an automatic model-selection technique for panel data regressions to better assist the Agency in establishing NTL regulatory targets for the distribution of utilities in this country. The proposed technique was applied to panel data containing annual observations from 62 Brazilian electricity distribution utilities from 2007 to 2017, thus generating 1,097,789 models associated with the regression types in the panel data. The main results are three selected models that showed more adherence to the actual capacity of Brazilian distribution utilities to reduce their NTLs.

Suggested Citation

  • Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2519-:d:1089877
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    as
    1. Flavio Martins & Maria Fatima Almeida & Rodrigo Calili & Agatha Oliveira, 2020. "Design Thinking Applied to Smart Home Projects: A User-Centric and Sustainable Perspective," Sustainability, MDPI, vol. 12(23), pages 1-27, December.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    4. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107609464, November.
    5. Hsiao, Cheng & Appelbe, Trent W. & Dineen, Christopher R., 1993. "A general framework for panel data models with an application to Canadian customer-dialed long distance telephone service," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 63-86, September.
    6. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    7. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    8. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    9. Calcagno, Vincent & de Mazancourt, Claire, 2010. "glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i12).
    10. 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.
    11. Loann D. Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," AMSE Working Papers 1852, Aix-Marseille School of Economics, France.
    12. Remy Tehero & Emmanuel Brou Aka & Murat Cokgezen, 2020. "Drivers of the Quality of Electricity Supply," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 183-195.
    13. Muhammad Luqman & Mirajul Haq & Iftikhar Ahmad, 2021. "Power Outages and Technical Efficiency of Manufacturing Firms: Evidence from Selected South Asian Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 133-140.
    14. 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.
    15. Zanardo, Rafael Petri & Siluk, Julio Cezar Mairesse & de Souza Savian, Fernando & Schneider, Paulo Smith, 2018. "Energy audit model based on a performance evaluation system," Energy, Elsevier, vol. 154(C), pages 544-552.
    16. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    17. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    18. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    19. Leticia dos Santos Benso Maciel & Benedito Donizeti Bonatto & Hector Arango & Lucas Gustavo Arango, 2020. "Evaluating Public Policies for Fair Social Tariffs of Electricity in Brazil by Using an Economic Market Model," Energies, MDPI, vol. 13(18), pages 1-20, September.
    20. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    21. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    22. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    23. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
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    1. Garcia, Alexandre Schwinden & Alves, Frederick Fagundes & Pimentel Filgueiras, João Marcello, 2024. "Tariff flags and electricity consumption response in Brazil," Utilities Policy, Elsevier, vol. 88(C).

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