  Recent studies concerning the point electricity price forecasting have shown
evidence that the hourly German Intraday Continuous Market is weak-form
efficient. Therefore, we take a novel, advanced approach to the problem. A
probabilistic forecasting of the hourly intraday electricity prices is
performed by simulating trajectories in every trading window to receive a
realistic ensemble to allow for more efficient intraday trading and redispatch.
A generalized additive model is fitted to the price differences with the
assumption that they follow a zero-inflated distribution, precisely a mixture
of the Dirac and the Student's t-distributions. Moreover, the mixing term is
estimated using a high-dimensional logistic regression with lasso penalty. We
model the expected value and volatility of the series using i.a. autoregressive
and no-trade effects or load, wind and solar generation forecasts and
accounting for the non-linearities in e.g. time to maturity. Both the in-sample
characteristics and forecasting performance are analysed using a rolling window
forecasting study. Multiple versions of the model are compared to several
benchmark models and evaluated using probabilistic forecasting measures and
significance tests. The study aims to forecast the price distribution in the
German Intraday Continuous Market in the last 3 hours of trading, but the
approach allows for application to other continuous markets, especially in
Europe. The results prove superiority of the mixture model over the benchmarks
gaining the most from the modelling of the volatility. They also indicate that
the introduction of XBID reduced the market volatility.
