IDEAS home Printed from https://ideas.repec.org/p/umc/wpaper/2112.html

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

Author

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
    as

    Download full text from publisher

    File URL: https://drive.google.com/file/d/1ON7jFVXVNYkDavH4968xqsg87oY42xaU/view?usp=sharing
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. is not listed on IDEAS
    2. Kyungsik Nam & Won-Ki Seo, 2025. "Nonlinear Temperature Sensitivity of Residential Electricity Demand: Evidence from a Distributional Regression Approach," Papers 2503.07213, arXiv.org.
    3. Mosquera-López, Stephania & Uribe, Jorge M. & Joaqui-Barandica, Orlando, 2024. "Weather conditions, climate change, and the price of electricity," Energy Economics, Elsevier, vol. 137(C).
    4. Humberto Verdejo & Emiliano Fucks Jara & Tomas Castillo & Cristhian Becker & Diego Vergara & Rafael Sebastian & Guillermo Guzmán & Francisco Tobar & Juan Zolezzi, 2023. "Analysis and Modeling of Residential Energy Consumption Profiles Using Device-Level Data: A Case Study of Homes Located in Santiago de Chile," Sustainability, MDPI, vol. 16(1), pages 1-32, December.
    5. Marco Guerzoni & Luigi Riso & Maria Grazia Zoia, 2025. "Forecasting the Impact of Extreme Weather Events on Electricity Prices in Italy: A GARCH-MIDAS Approach with Enhanced Variable Selection," DISCE - Working Papers del Dipartimento di Politica Economica dipe0043, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).

    More about this item

    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

    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:2112. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chao Gu (email available below). General contact details of provider: https://edirc.repec.org/data/edumous.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.