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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 1512, Department of Economics, University of Missouri.
  • Handle: RePEc:umc:wpaper:1512
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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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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).
    11. Stéphane AURAY & Vincent CAPONI, 2020. "A Vector Autoregressive Model of Forecast Electricity Consumption in France," Working Papers 2020-06, Center for Research in Economics and Statistics.
    12. 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.
    13. 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.
    14. 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.

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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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