IDEAS home Printed from https://ideas.repec.org/p/umc/wpaper/1512.html
   My bibliography  Save this paper

A New Approach to Modeling the Effects of Temperature Fluctuations on Monthly Electricity Demand

Author

Listed:
  • Yoosoon Chang

    (Department of Economics, Indiana University)

  • Chang Sik Kim

    () (Department of Economics, Sungkyunkwan University, Seoul 110-745, Korea)

  • J. Isaac Miller

    () (Department of Economics, University of Missouri)

  • Joon Y. Park

    (Department of Economics, Indiana University and Sungkyunkwan University)

  • Sungkeun Park

    ( Korea Institute for Industrial Economics and Trade)

Abstract

This paper proposes a novel approach to measure and analyze the effect of temperature on electricity demand. This temperature effect is specified as a function of the density of temperatures observed at a high frequency with a functional coefficient, which we call the temperature response function. This approach contrasts with the usual approach to model the temperature effect as a function of heating and cooling degree days. We further investigate how non-climate variables, which include the price of electricity relative to that of substitutable energy and latent variables such as preferences and technology that we proxy by a linear time trend, affect the demand response to temperature changes. Our approach and methodology are demonstrated using Korean electricity demand data for residential and commercial sectors.

Suggested Citation

  • Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2015. "A New Approach to Modeling the Effects of Temperature Fluctuations on Monthly Electricity Demand," Working Papers 2015-12, Department of Economics, University of Missouri.
  • Handle: RePEc:umc:wpaper:1512
    as

    Download full text from publisher

    File URL: https://economics.missouri.edu/working-papers/2015/wp1512_miller.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    2. Jones, Clifton T, 1994. "Accounting for technical progress in aggregate energy demand," Energy Economics, Elsevier, vol. 16(4), pages 245-252, October.
    3. Beenstock, Michael & Goldin, Ephraim & Nabot, Dan, 1999. "The demand for electricity in Israel," Energy Economics, Elsevier, vol. 21(2), pages 168-183, April.
    4. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
    5. Sailor, David J. & Muñoz, J.Ricardo, 1997. "Sensitivity of electricity and natural gas consumption to climate in the U.S.A.—Methodology and results for eight states," Energy, Elsevier, vol. 22(10), pages 987-998.
    6. Filippini, Massimo, 1995. "Swiss residential demand for electricity by time-of-use," Resource and Energy Economics, Elsevier, vol. 17(3), pages 281-290, November.
    7. Fan, Shu & Hyndman, Rob J., 2011. "The price elasticity of electricity demand in South Australia," Energy Policy, Elsevier, vol. 39(6), pages 3709-3719, June.
    8. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    9. Henley, Andrew & Peirson, John, 1998. "Residential energy demand and the interaction of price and temperature: British experimental evidence," Energy Economics, Elsevier, vol. 20(2), pages 157-171, April.
    10. Engle, R. F. & Granger, C. W. J. & Hallman, J. J., 1989. "Merging short-and long-run forecasts : An application of seasonal cointegration to monthly electricity sales forecasting," Journal of Econometrics, Elsevier, vol. 40(1), pages 45-62, January.
    11. Moral-Carcedo, Julian & Vicens-Otero, Jose, 2005. "Modelling the non-linear response of Spanish electricity demand to temperature variations," Energy Economics, Elsevier, vol. 27(3), pages 477-494, May.
    12. Chang, Yoosoon & Martinez-Chombo, Eduardo, 2003. "Electricity Demand Analysis Using Cointegration and Error-Correction Models with Time Varying Parameters: The Mexican Case," Working Papers 2003-08, Rice University, Department of Economics.
    13. Halicioglu, Ferda, 2007. "Residential electricity demand dynamics in Turkey," Energy Economics, Elsevier, vol. 29(2), pages 199-210, March.
    14. Silk, Julian I. & Joutz, Frederick L., 1997. "Short and long-run elasticities in US residential electricity demand: a co-integration approach," Energy Economics, Elsevier, vol. 19(4), pages 493-513, October.
    15. Cheolbeom Park, 2011. "How does changing age distribution impact stock prices? a nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 886-887, August.
    16. Serletis, Apostolos & Shahmoradi, Asghar, 2008. "Semi-nonparametric estimates of interfuel substitution in U.S. energy demand," Energy Economics, Elsevier, vol. 30(5), pages 2123-2133, September.
    17. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    18. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355, October.
    19. Park, Sung Y. & Zhao, Guochang, 2010. "An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach," Energy Economics, Elsevier, vol. 32(1), pages 110-120, January.
    20. Train, Kenneth & Ignelzi, Patrice & Engle, Robert & Granger, Clive & Ramanathan, Ramu, 1984. "The billing cycle and weather variables in models of electricity sales," Energy, Elsevier, vol. 9(11), pages 1041-1047.
    21. Watts, Geof & Quiggin, John C., 1984. "A Note on the Use of a Logarithmic Time Trend," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 0(Number 02), pages 1-9, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:enepol:v:118:y:2018:i:c:p:257-269 is not listed on IDEAS
    2. repec:eee:appene:v:209:y:2018:i:c:p:167-179 is not listed on IDEAS
    3. repec:eee:energy:v:127:y:2017:i:c:p:534-543 is not listed on IDEAS

    More about this item

    Keywords

    electricity demand; temperature effect; temperature response function; cross temperature response function; electricity demand in Korea;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:umc:wpaper:1512. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Valerie Kulp). General contact details of provider: http://edirc.repec.org/data/edumous.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.