IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v41y2020i3p161-182.html
   My bibliography  Save this article

Intra-day Electricity Demand and Temperature

Author

Listed:
  • James McCulloch
  • Katja Ignatieva

Abstract

The objective of this paper is to explain the relationship between high frequency electricity demand, intra-day temperature variation and time. Using the Generalised Additive Model (GAM) framework we link high frequency (5-minute) aggregate electricity demand in Australia to the time of the day, time of the year and intra-day temperature. We document a strong relationship between high frequency electricity demand and intra-day temperature. We show a superior model fit when using Daylight Saving Time (DST), or clock time, instead of the standard (solar) time. We introduce the time weighted temperature model that captures instantaneous electricity demand sensitivity to temperature as a function of the human daily activity cycle, by assigning different temperature signal weighting based on the DST time. The results on DST and time weighted temperature modelling are novel in the literature and are important innovations in high frequency electricity demand forecasting.

Suggested Citation

  • James McCulloch & Katja Ignatieva, 2020. "Intra-day Electricity Demand and Temperature," The Energy Journal, , vol. 41(3), pages 161-182, May.
  • Handle: RePEc:sae:enejou:v:41:y:2020:i:3:p:161-182
    DOI: 10.5547/01956574.41.3.jmcc
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/01956574.41.3.jmcc
    Download Restriction: no

    File URL: https://libkey.io/10.5547/01956574.41.3.jmcc?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
    ---><---

    References listed on IDEAS

    as
    1. Xiao, Ni & Zarnikau, Jay & Damien, Paul, 2007. "Testing functional forms in energy modeling: An application of the Bayesian approach to U.S. electricity demand," Energy Economics, Elsevier, vol. 29(2), pages 158-166, March.
    Full references (including those not matched with items on IDEAS)

    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. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    2. Karimu, Amin & Brännlund, Runar, 2013. "Functional form and aggregate energy demand elasticities: A nonparametric panel approach for 17 OECD countries," Energy Economics, Elsevier, vol. 36(C), pages 19-27.
    3. Kurt Kratena & Ina Meyer & Michael Wüger, 2009. "Ökonomische, technologische und soziodemographische Einflussfaktoren der Energienachfrage," WIFO Monatsberichte (monthly reports), WIFO, vol. 82(7), pages 525-538, July.
    4. T. M. Fullerton & A. G. Walke, 2013. "Public transportation demand in a border metropolitan economy," Applied Economics, Taylor & Francis Journals, vol. 45(27), pages 3922-3931, September.
    5. Rowland, Christopher S. & Mjelde, James W. & Dharmasena, Senarath, 2017. "Policy implications of considering pre-commitments in U.S. aggregate energy demand system," Energy Policy, Elsevier, vol. 102(C), pages 406-413.
    6. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    7. Woo, C.K. & Liu, Y. & Zarnikau, J. & Shiu, A. & Luo, X. & Kahrl, F., 2018. "Price elasticities of retail energy demands in the United States: New evidence from a panel of monthly data for 2001–2016," Applied Energy, Elsevier, vol. 222(C), pages 460-474.
    8. Thomas M. Fullerton & Felipe I. Galan & Wm. Doyle Smith & Adam G. Walke, 2014. "An Empirical Analysis of Migratory Flows to the United States," Applied Economics and Finance, Redfame publishing, vol. 1(2), pages 11-20, November.
    9. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
    10. Alberini, Anna & Filippini, Massimo, 2011. "Response of residential electricity demand to price: The effect of measurement error," Energy Economics, Elsevier, vol. 33(5), pages 889-895, September.
    11. Angela Köppl & Michael Wüger, 2007. "Determinanten der Energienachfrage der privaten Haushalte unter Berücksichtigung von Lebensstilen," WIFO Studies, WIFO, number 29999.
    12. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    13. Contreras, Sergio & Smith, Wm. Doyle & Fullerton, Thomas M., Jr., 2010. "U.S. commercial electricity consumption," MPRA Paper 34855, University Library of Munich, Germany, revised 22 May 2011.
    14. Ken-ichi Mizobuchi & Hisashi Tanizaki, 2014. "On estimation of almost ideal demand system using moving blocks bootstrap and pairs bootstrap methods," Empirical Economics, Springer, vol. 47(4), pages 1221-1250, December.
    15. Fullerton, Thomas M., Jr. & Ramirez, David A. & Walke, Adam G., 2013. "An Econometric Analysis of Population Change in Arkansas," MPRA Paper 59588, University Library of Munich, Germany, revised 11 Nov 2013.
    16. Woo, C.K. & Shiu, A. & Liu, Y. & Luo, X. & Zarnikau, J., 2018. "Consumption effects of an electricity decarbonization policy: Hong Kong," Energy, Elsevier, vol. 144(C), pages 887-902.
    17. Mizobuchi, Kenichi, 2008. "An empirical study on the rebound effect considering capital costs," Energy Economics, Elsevier, vol. 30(5), pages 2486-2516, September.
    18. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    19. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    20. Fullerton, Thomas M., Jr. & Walke, Adam G. & Villavicencio, Diana, 2015. "An Econometric Approach for Modeling Population Change in Doña Ana County, New Mexico," MPRA Paper 71141, University Library of Munich, Germany, revised 28 Jan 2015.

    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:sae:enejou:v:41:y:2020:i:3:p:161-182. 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: SAGE Publications (email available below). General contact details of provider: .

    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.