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Energies 2018, 11(10), 2658; https://doi.org/10.3390/en11102658

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

1
Management Science and Operations, London Business School, London NW1 4SA, UK
2
Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano 39100, Italy
3
Institute of Energy Systems and Electrical Drives-Energy Economics Group, Technical University of Vienna, Vienna 1040, Austria
*
Author to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 16 September 2018 / Accepted: 29 September 2018 / Published: 5 October 2018
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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Abstract

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. View Full-Text
Keywords: electricity; forecasting; imbalances; density forecasts; trading electricity; forecasting; imbalances; density forecasts; trading
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Bunn, D.W.; Gianfreda, A.; Kermer, S. A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market. Energies 2018, 11, 2658.

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