A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market
Derek W. Bunn (),
Angelica Gianfreda () and
Stefan Kermer ()
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Derek W. Bunn: Management Science and Operations, London Business School, London NW1 4SA, UK
Stefan Kermer: Institute of Energy Systems and Electrical Drives-Energy Economics Group, Technical University of Vienna, Vienna 1040, Austria
Energies, 2018, vol. 11, issue 10, 1-13
This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which are estimated as linear functions of lagged imbalance and forecast errors for load, wind and solar production. The evaluation of this density predictor is compared to an expected value obtained from OLS regression model, using the same regressors, through an out-of-sample backtest of a flexible generator seeking to optimize its imbalance positions on the intraday market. This research contributes to forecasting methodology and imbalance prediction, and most significantly it provides a case study in the evaluation of density forecasts through decision-making performance. The main finding is that the use of the density forecasts substantially increased trading profitability and reduced risk compared to the more conventional use of mean value regressions.
Keywords: electricity; forecasting; imbalances; density forecasts; trading (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:10:p:2658-:d:173889
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