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

José R. Andrade, Jorge Filipe, Marisa Reis and Ricardo J. Bessa
Additional contact information
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
Marisa Reis: INESC Technology and Science (INESC TEC), Campus da FEUP, 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

Sustainability, 2017, vol. 9, issue 11, 1-29

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.

Keywords: electricity market; price forecasting; uncertainty; statistical learning; intraday; feature engineering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

Downloads: (external link)
https://www.mdpi.com/2071-1050/9/11/1990/pdf (application/pdf)
https://www.mdpi.com/2071-1050/9/11/1990/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:9:y:2017:i:11:p:1990-:d:117115

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().