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Short-term forecasting of electricity prices: Do we need a different model for each hour?

Author

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  • Adam Misiorek

Abstract

This empirical paper is a continuation of our earlier work on time series forecasting of day-ahead electricity prices. Given the controversy in the literature whether to use one large model across all hours or 24 separate models, we study if the model structure (and not only the coefficients) should change for different periods of the day. We find that leaving out the statistically insignificant factors leads to, on average, better point forecasts.

Suggested Citation

  • Adam Misiorek, 2008. "Short-term forecasting of electricity prices: Do we need a different model for each hour?," HSC Research Reports HSC/08/01, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Handle: RePEc:wuu:wpaper:hsc0801
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    File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_08_01.pdf
    File Function: Original draft, 2008
    Download Restriction: no

    File URL: http://www.met-online.nl/pdf/MET16-2-2.pdf
    File Function: Final printed version, 2008
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    Citations

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    Cited by:

    1. repec:eee:rensus:v:81:y:2018:i:p1:p:1548-1568 is not listed on IDEAS
    2. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-22, August.
    3. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.

    More about this item

    Keywords

    Electricity price forecasting; Autoregression (AR) model; Threshold Autoregression (TAR) model;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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