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

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 depend...

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Bibliographic Details
Published inEnergies (Basel) Vol. 7; no. 9; pp. 5523 - 5547
Main Authors Jónsson, Tryggvi, Pinson, Pierre, Madsen, Henrik, Nielsen, Henrik
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2014
Subjects
Alternative energy sources
density forecasting
Electricity
electricity prices
Forecasting
non-stationarity
quantile regression
Stochastic models
stochastic processes
Denmark
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en7095523