Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity
  Prices
Daniel Nickelsen Gernot Müller
We present a first study of Bayesian forecasting of electricity prices traded
on the German continuous intraday market which fully incorporates parameter
uncertainty. Our target variable is the IDFull price index, forecasts are given
in terms of posterior predictive distributions. For validation we use the
exceedingly volatile electricity prices of 2022, which have hardly been the
subject of forecasting studies before. As a benchmark model, we use all
available intraday transactions at the time of forecast creation to compute a
current value for the IDFull. According to the weak-form efficiency hypothesis,
it would not be possible to significantly improve this benchmark built from
last price information. We do, however, observe statistically significant
improvement in terms of both point measures and probability scores. Finally, we
challenge the declared gold standard of using LASSO for feature selection in
electricity price forecasting by presenting strong statistical evidence that
Orthogonal Matching Pursuit (OMP) leads to better forecasting performance.