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

Author

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  • 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 1512, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:1512
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    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. 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.
    3. Jones, Clifton T, 1994. "Accounting for technical progress in aggregate energy demand," Energy Economics, Elsevier, vol. 16(4), pages 245-252, October.
    4. Halicioglu, Ferda, 2007. "Residential electricity demand dynamics in Turkey," Energy Economics, Elsevier, vol. 29(2), pages 199-210, March.
    5. Beenstock, Michael & Goldin, Ephraim & Nabot, Dan, 1999. "The demand for electricity in Israel," Energy Economics, Elsevier, vol. 21(2), pages 168-183, April.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Filippini, Massimo, 1995. "Swiss residential demand for electricity by time-of-use," Resource and Energy Economics, Elsevier, vol. 17(3), pages 281-290, November.
    12. 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.
    13. 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.
    14. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    15. 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.
    16. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    17. 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.
    18. 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.
    19. 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.
    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. 52(02), pages 1-9, August.
    22. 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.
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    Cited by:

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    3. Tian, Chuyin & Huang, Guohe & Piwowar, Joseph M. & Yeh, Shin-Cheng & Lu, Chen & Duan, Ruixin & Ren, Jiayan, 2022. "Stochastic RCM-driven cooling and heating energy demand analysis for residential building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    4. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
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    6. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    7. Hocheol Jeon, 2019. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data," Energies, MDPI, vol. 12(23), pages 1-15, November.
    8. 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).
    9. Alimohammadisagvand, Behrang & Jokisalo, Juha & Sirén, Kai, 2018. "Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building," Applied Energy, Elsevier, vol. 209(C), pages 167-179.
    10. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    11. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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    14. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
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    More about this item

    Keywords

    electricity demand; temperature effect; temperature response function; cross temperature response function; electricity demand in Korea;
    All these keywords.

    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

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