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Energies 2014, 7(9), 5523-5547; doi:10.3390/en7095523

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

1
Department of Applied Mathematics, Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark
2
ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark
3
Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325, 2800 Kgs. Lyngby, Denmark
*
Author to whom correspondence should be addressed.
Received: 1 May 2014 / Revised: 30 July 2014 / Accepted: 15 August 2014 / Published: 25 August 2014
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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. View Full-Text
Keywords: stochastic processes; electricity prices; density forecasting; quantile regression; non-stationarity stochastic processes; electricity prices; density forecasting; quantile regression; non-stationarity
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Jónsson, T.; Pinson, P.; Madsen, H.; Nielsen, H.A. Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression. Energies 2014, 7, 5523-5547.

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