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A new approach to modeling the effects of temperature fluctuations on monthly electricity demand

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  • Chang, Yoosoon
  • Kim, Chang Sik
  • Miller, J. Isaac
  • Park, Joon Y.
  • Park, Sungkeun

Abstract

We propose a novel approach to measure and analyze the short-run effect of temperature on monthly sectoral electricity demand. This effect is specified as a function of the density of temperatures observed at a high frequency with a functional coefficient, in contrast to conventional methods using a function of monthly heating and cooling degree days. Our approach also allows non-climate variables to influence the short-run demand response to temperature changes. Our methodology is demonstrated using Korean electricity demand data for residential and commercial sectors. In the residential sector, we do not find evidence that the non-climate variables affect the demand response to temperature. In contrast, we show conclusive evidence that the non-climate variables influence the demand response in the commercial sector. In particular, commercial consumers are less responsive to cold temperatures when controlling for the electricity price relative to city gas. They are more responsive to the price when temperatures are cold. The estimated effect of the time trend suggests that seasonality of commercial demand has increased in the winter but decreased in the summer.

Suggested Citation

  • Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
  • Handle: RePEc:eee:eneeco:v:60:y:2016:i:c:p:206-216
    DOI: 10.1016/j.eneco.2016.09.016
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    3. Hocheol Jeon, 2019. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data," Energies, MDPI, Open Access Journal, vol. 12(23), pages 1-15, November.
    4. 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).
    5. 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.
    6. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, Open Access Journal, vol. 13(20), pages 1-17, October.
    7. Gautam, Tej K. & Paudel, Krishna P., 2018. "Estimating sectoral demands for electricity using the pooled mean group method," Applied Energy, Elsevier, vol. 231(C), pages 54-67.
    8. Manner, Hans & Alavi Fard, Farzad & Pourkhanali, Armin & Tafakori, Laleh, 2019. "Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae," Energy Economics, Elsevier, vol. 78(C), pages 143-164.
    9. 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.
    10. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego F., 2018. "Effect of stopping hydroelectric power generation on the dynamics of electricity prices: An event study approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 456-467.
    11. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, Open Access Journal, 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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