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A statistical model for natural gas standardized load profiles

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  • Marek Brabec
  • Ondřej Konár
  • Marek Malý
  • Emil Pelikán
  • Jiří Vondráček

Abstract

Summary. We present a statistical model for construction and application of standardized load profiles. Standardized load profile curves give a typical natural gas consumption pattern throughout a year in various time resolutions. Our semiparametric regression model uses three types of information, constant characteristics of an individual customer, individual historical consumption and time varying explanatory variables, both to describe typical trend and to correct for departure of current conditions from normal. The model's multiplicative structure allows for convenient separation of individual‐specific and common time varying parts. Although corrections are parametric, no substantial information about the typical consumption trend is available, so it is modelled non‐parametrically. Corrections for temperature effects are non‐linear; hence we deal with them through an obvious extension of the generalized additive model framework.

Suggested Citation

  • Marek Brabec & Ondřej Konár & Marek Malý & Emil Pelikán & Jiří Vondráček, 2009. "A statistical model for natural gas standardized load profiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 123-139, February.
    • Handle: RePEc:bla:jorssc:v:58:y:2009:i:1:p:123-139
    DOI: 10.1111/j.1467-9876.2008.00636.x
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    2. Riddell, A. G. & Manson, K., 1996. "Parametrisation of domestic load profiles," Applied Energy, Elsevier, vol. 54(3), pages 199-210, July.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
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    Cited by:

    1. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    2. Marta P. Fernandes & Joaquim L. Viegas & Susana M. Vieira & João M. C. Sousa, 2017. "Segmentation of Residential Gas Consumers Using Clustering Analysis," Energies, MDPI, vol. 10(12), pages 1-26, December.
    3. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    4. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    5. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
    6. Sarah E. Heaps & Malcolm Farrow & Kevin J. Wilson, 2020. "Identifying the effect of public holidays on daily demand for gas," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 471-492, February.
    7. J. M. Azaïs & S. Bercu & J. C. Fort & A. Lagnoux & P. Lé, 2010. "Simultaneous confidence bands in curve prediction applied to load curves," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 889-904, November.
    8. M. Brabec & O. Kon�r & M. Malý & I. Kasanický & E. Pelik�n, 2015. "Statistical models for disaggregation and reaggregation of natural gas consumption data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 921-937, May.

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