Jónsson, Tryggvi1; Pinson, Pierre2; Madsen, Henrik1; Nielsen, Henrik Aalborg5
1 Department of Applied Mathematics and Computer Science, Technical University of Denmark2 Department of Electrical Engineering, Technical University of Denmark3 Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark4 Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark5 ENFOR A/S
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.
Energies, 2014, Vol 7, Issue 9, p. 5523-5547
Stochastic processes; Electricity prices; Density forecasting; Quantile regression; Non-stationarity