Reconciliation of probabilistic forecasts with an application to wind power

TL;DR

This paper introduces new methods for adjusting probabilistic wind power forecasts to ensure they are consistent across hierarchical levels, improving forecast accuracy for grid management.

Contribution

It proposes novel reconciliation methods, including a cross-validation based approach, to enhance the coherence and accuracy of probabilistic wind power forecasts.

Findings

Reconciliation improves forecast accuracy at all hierarchy levels.

Methods outperform existing approaches in empirical tests.

Enhanced forecasts support better grid operation decisions.

Abstract

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.