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The influence of temperature on spike probability in day-ahead power prices

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  • Huisman, Ronald

Abstract

It is well known that day-ahead prices in power markets exhibit spikes and time-varying volatility. Spikes and extremely high volatility are the results of (short-term) frictions in demand and/or supply conditions. It is known that information on load or the reserve margin help to forecast spikes. However, these variables are not (timely) available for every market participant and this paper suggests to use temperature as a proxy. Interpreting the results from several switching-regimes models, the paper shows that the probability of spike occurrence increases when temperature deviates substantially from mean temperature levels.

Suggested Citation

  • Huisman, Ronald, 2008. "The influence of temperature on spike probability in day-ahead power prices," Energy Economics, Elsevier, vol. 30(5), pages 2697-2704, September.
    • Handle: RePEc:eee:eneeco:v:30:y:2008:i:5:p:2697-2704
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    References listed on IDEAS

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    1. Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
    2. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    3. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2008. "The power of weather. Some empirical evidence on predicting day-ahead power prices through weather forecasts," Working Paper 2008/08, Norges Bank.
    4. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    5. Kosater, Peter, 2006. "On the impact of weather on German hourly power prices," Discussion Papers in Econometrics and Statistics 1/06, University of Cologne, Institute of Econometrics and Statistics.
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