  New methods are proposed for adjusting probabilistic forecasts to ensure
coherence with the aggregation constraints inherent in temporal hierarchies.
The different approaches nested within this framework include methods that
exploit information at all levels of the hierarchy as well as a novel method
based on cross-validation. The methods are evaluated using real data from two
wind farms in Crete, an application where it is imperative for optimal
decisions related to grid operations and bidding strategies to be based on
coherent probabilistic forecasts of wind power. Empirical evidence is also
presented showing that probabilistic forecast reconciliation improves the
accuracy of both point forecasts and probabilistic forecasts.
