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Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices

Author

Listed:
  • Eran Raviv

    (Erasmus University Rotterdam)

  • Kees E. Bouwman

    (Erasmus University Rotterdam)

  • Dick van Dijk

    (Erasmus University Rotterdam)

Abstract

This discussion paper led to a publication in 'Energy Economics' , 2015, 50, 227-239. The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices significantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between different hourly prices, but the risk of overfitting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations.

Suggested Citation

  • Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20130068
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Electricity market; Forecasting; Hourly prices; Dimension reduction; Shrinkage; Forecast combinations;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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