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Modeling Peak Electricity Demand: A Semiparametric Approach Using Weather-Driven Cross Temperature Response Functions

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Abstract

We propose a novel method to model daily peak electricity demand using temperature and additional hourly and daily weather covariates, such as humidity and wind speed. Rather than enter into the temperature response function additively, the additional covariates may flexibly impact the demand response to temperature. Such flexibility allows differential responses to the actual temperature based on the heat index and wind chill factor, for example. Most notably, we find that ignoring humidity substantially underestimates the effect of high temperatures, while ignoring the effect of cloud cover overestimates the effect of low temperatures. Time of day also matters: a demand response to the same temperature may be different at different times of day. Moreover, accounting for weather-related covariates improves the model's explanation of the peak daily demand.

Suggested Citation

  • J. Isaac Miller & Kyungsik Nam, 2021. "Modeling Peak Electricity Demand: A Semiparametric Approach Using Weather-Driven Cross Temperature Response Functions," Working Papers 2112, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:2112
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    1. Sigauke, Caston & Verster, Andréhette & Chikobvu, Delson, 2013. "Extreme daily increases in peak electricity demand: Tail-quantile estimation," Energy Policy, Elsevier, vol. 53(C), pages 90-96.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643.
    3. Yee Yan, Yuk, 1998. "Climate and residential electricity consumption in Hong Kong," Energy, Elsevier, vol. 23(1), pages 17-20.
    4. 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.
    5. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    6. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(5), pages 664-703, October.
    7. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2016. "Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 232-243.
    8. Paniagua-Tineo, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Ortiz-García, E.G. & Cony, M.A. & Hernández-Martín, E., 2011. "Prediction of daily maximum temperature using a support vector regression algorithm," Renewable Energy, Elsevier, vol. 36(11), pages 3054-3060.
    9. 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.
    10. 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.
    11. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    12. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    13. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    14. Rallapalli, Srinivasa Rao & Ghosh, Sajal, 2012. "Forecasting monthly peak demand of electricity in India—A critique," Energy Policy, Elsevier, vol. 45(C), pages 516-520.
    15. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    16. Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.
    17. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-162, February.
    18. 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.
    19. 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.
    20. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    21. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    22. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    23. 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.
    24. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    25. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    26. 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.
    27. Stephen Chan & Saralees Nadarajah, 2015. "Extreme value analysis of electricity demand in the UK," Applied Economics Letters, Taylor & Francis Journals, vol. 22(15), pages 1246-1251, October.
    28. 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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    More about this item

    Keywords

    peak electricity demand; temperature response function; cross-temperature response function;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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