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Parametrisation of domestic load profiles

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  • Riddell, A. G.
  • Manson, K.

Abstract

As part of a study to determine the factors influencing the power usage patterns of domestic consumers, electrical loads have been logged at half- and quarter-hourly intervals for several months. This enables the production of hundreds of daily load profiles, the shapes of which can be analysed with respect to various attributes of the consumers. However, in order to carry out such an analysis, it is necessary to find a numerical representation of a load shape. It is found that the most suitable means of doing so is to calculate Fourier series approximations to the profiles, and use the coefficients of the series to represent the profile shapes.

Suggested Citation

  • Riddell, A. G. & Manson, K., 1996. "Parametrisation of domestic load profiles," Applied Energy, Elsevier, vol. 54(3), pages 199-210, July.
    • Handle: RePEc:eee:appene:v:54:y:1996:i:3:p:199-210
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    Cited by:

    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Akito Ozawa & Ryota Furusato & Yoshikuni Yoshida, 2017. "Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan," Sustainability, MDPI, vol. 9(4), pages 1-23, March.
    3. 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.
    4. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
    5. 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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