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The power of weather

Author

Listed:
  • Christian Huurman
  • Francesco Ravazzolo
  • Chen Zhou

Abstract

This paper examines the predictive power of weather for electricity prices in day ahead markets in real time. We find that next-day weather forecasts improve the forecast accuracy of Scandinavian day-ahead electricity prices substantially in terms of point forecasts, suggesting that weather forecasts can price the weather premium. This improvement strengthens the confidence in the forecasting model, which results in high center-mass predictive densities. In density forecast, such a predictive density may not accommodate forecasting uncertainty well. Our density forecast analysis confirms this intuition by showing that incorporating weather forecasts in density forecasting does not deliver better density forecast performances.

Suggested Citation

  • Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2010. "The power of weather," DNB Working Papers 236, Netherlands Central Bank, Research Department.
  • Handle: RePEc:dnb:dnbwpp:236
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Monticini, Andrea & Ravazzolo, Francesco, 2014. "Forecasting the intraday market price of money," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 304-315.
    2. Paulo Soares Esteves & António Rua, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    3. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    4. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," CQE Working Papers 6117, Center for Quantitative Economics (CQE), University of Muenster.
    5. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    6. 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.
    7. repec:eee:rensus:v:81:y:2018:i:p1:p:1548-1568 is not listed on IDEAS
    8. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.
    9. Ergemen, Yunus Emre & Haldrup, Niels & Rodríguez-Caballero, Carlos Vladimir, 2016. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," Energy Economics, Elsevier, vol. 60(C), pages 79-96.
    10. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Papers 1801.01093, arXiv.org.
    11. repec:ctc:serie1:def10 is not listed on IDEAS
    12. 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.
    13. repec:eee:energy:v:139:y:2017:i:c:p:594-605 is not listed on IDEAS

    More about this item

    Keywords

    Electricity prices; weather forecasts; point and density forecasts; GARCH models.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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