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Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

Author

Listed:
  • José R. Andrade

    (INESC Technology and Science (INESC TEC), Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • Jorge Filipe

    (INESC Technology and Science (INESC TEC), Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • Marisa Reis

    (INESC Technology and Science (INESC TEC), Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • Ricardo J. Bessa

    (INESC Technology and Science (INESC TEC), Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal)

Abstract

Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

Suggested Citation

  • José R. Andrade & Jorge Filipe & Marisa Reis & Ricardo J. Bessa, 2017. "Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model," Sustainability, MDPI, vol. 9(11), pages 1-29, October.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:1990-:d:117115
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    References listed on IDEAS

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