IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v41y2020i3p161-182.html

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. 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.
    2. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    3. Sailor, David J, 2001. "Relating residential and commercial sector electricity loads to climate—evaluating state level sensitivities and vulnerabilities," Energy, Elsevier, vol. 26(7), pages 645-657.
    4. 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.
    5. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    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. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    3. Harish, Santosh & Singh, Nishmeet & Tongia, Rahul, 2020. "Impact of temperature on electricity demand: Evidence from Delhi and Indian states," Energy Policy, Elsevier, vol. 140(C).
    4. 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.
    5. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
    6. Palacios-Garcia, E.J. & Moreno-Munoz, A. & Santiago, I. & Flores-Arias, J.M. & Bellido-Outeirino, F.J. & Moreno-Garcia, I.M., 2018. "A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector," Energy, Elsevier, vol. 144(C), pages 1080-1091.
    7. Chen Zhang & Hua Liao & Zhifu Mi, 2019. "Climate impacts: temperature and electricity consumption," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1259-1275, December.
    8. Evgeniy Ozhegov & Evgeniya Popova, 2017. "Demand for electricity and weather conditions: Nonparametric analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 46, pages 55-73.
    9. Gupta, Eshita, 2012. "Global warming and electricity demand in the rapidly growing city of Delhi: A semi-parametric variable coefficient approach," Energy Economics, Elsevier, vol. 34(5), pages 1407-1421.
    10. Hekkenberg, M. & Benders, R.M.J. & Moll, H.C. & Schoot Uiterkamp, A.J.M., 2009. "Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands," Energy Policy, Elsevier, vol. 37(4), pages 1542-1551, April.
    11. Hekkenberg, M. & Moll, H.C. & Uiterkamp, A.J.M. Schoot, 2009. "Dynamic temperature dependence patterns in future energy demand models in the context of climate change," Energy, Elsevier, vol. 34(11), pages 1797-1806.
    12. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    13. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    14. Marilyn A. Brown & Matt Cox & Ben Staver & Paul Baer, 2016. "Modeling climate-driven changes in U.S. buildings energy demand," Climatic Change, Springer, vol. 134(1), pages 29-44, January.
    15. 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.
    16. Ahmad, Tanveer & Zhang, Hongcai, 2020. "Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts," Energy, Elsevier, vol. 209(C).
    17. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    18. Waite, Michael & Cohen, Elliot & Torbey, Henri & Piccirilli, Michael & Tian, Yu & Modi, Vijay, 2017. "Global trends in urban electricity demands for cooling and heating," Energy, Elsevier, vol. 127(C), pages 786-802.
    19. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    20. Mauro Bernardi & Francesco Lisi, 2020. "Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case," Energies, MDPI, vol. 13(23), pages 1-34, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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.