Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression

Tryggvi Jónsson, Pierre Pinson, Henrik Madsen and Henrik Aalborg Nielsen
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Tryggvi Jónsson: Department of Applied Mathematics, Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark
Pierre Pinson: Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325, 2800 Kgs. Lyngby, Denmark
Henrik Madsen: Department of Applied Mathematics, Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark
Henrik Aalborg Nielsen: ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark

Energies, 2014, vol. 7, issue 9, 1-25

Abstract: A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%–95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model is compared to that of four benchmark approaches and the well-known the generalist autoregressive conditional heteroskedasticity (GARCH) model over a three-year evaluation period. While all benchmarks are outperformed in terms of forecasting skill overall, the superiority of the semi-parametric model over the GARCH model lies in the former’s ability to generate reliable quantile estimates.

Keywords: stochastic processes; electricity prices; density forecasting; quantile regression; non-stationarity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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