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

Energies

Tryggvi Jónsson¹  Pierre Pinson²  Henrik Madsen¹ 

[1] Department of Applied Mathematics, Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark; E-Mails:;Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325, 2800 Kgs. Lyngby, Denmark

关键词: stochastic processes;    electricity prices;    density forecasting;    quantile regression;    non-stationarity;   

DOI  :  10.3390/en7095523

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland