Reconciliation of probabilistic forecasts with an application to wind power

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.

References in corpus (1)

• Wind Energy: Forecasting Challenges for Its Operational Management

Cited by in corpus (7)

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• Point and probabilistic forecast reconciliation for general linearly constrained multiple time series
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• Probabilistic Reconciliation of Count Time Series
• Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures