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.
Highlights
Motivated by the requirements for accurate risk management, forecasting the density functions for electricity prices and loads is attracting an increased amount of research into new methodologies.Substantial overviews on the related literature are given in references [1,2]
Analyzed Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices, Chan and Grant [5] compared energy price dynamics with GARCH and stochastic volatility models, Jiang et al [6] forecasted day-ahead electricity prices based on a hybrid model applying particle swarm optimization and core mapping with fuzzy logic and model selection, Uniejewski et al [7] show how variance stabilizing transformations can improve electricity spot price forecasting and Hagfors et al [8] used quantile regressions to forecast UK electricity prices
The German/Austrian intraday power exchange is operated by EPEX Spot SE and the power market area comprises 5 delivery zones managed by 5 Transmission System Operators (TSOs), one of which is the Austrian Power Grid (APG)
Summary
Motivated by the requirements for accurate risk management, forecasting the density functions for electricity prices and loads is attracting an increased amount of research into new methodologies. More commonly used in practice is the value-at-risk backtesting procedure, whereby for example the 5%, 95% quantile predictions are expected to cover 5% and 95% of the outcomes ex post (see the coverage tests and references [20,21]) While such tests of calibration are useful for comparing the specification of different densities, the overall question of the usefulness of the full density representation, compared to expected values, remains under-researched in the context of short-term electricity decision-making. The value of forecasting with densities, compared to mean-values, is shown, through back-testing real-time trading strategies on the Austrian balancing zone of the German/Austrian electricity market, Secondly, a new density modeling technique, previously only applied to prices, is extended successfully to forecasting the imbalance volumes at 15 min resolution, and outperforms a more conventional benchmark.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
