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 and electric load in Boston. For these applications, optimal decisions related to grid operations and bidding strategies are based on coherent probabilistic forecasts of energy power. Empirical evidence is also presented showing that probabilistic forecast reconciliation improves the accuracy of the probabilistic forecasts.
- Renewable energy generation
- Temporal hierarchies
ASJC Scopus subject areas
- Computer Science(all)
- Modelling and Simulation
- Management Science and Operations Research
- Information Systems and Management
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- Management - Professor
- Information, Decisions & Operations - Chair in Management Science; Director of Studies MSc in Operations, Logistics & Supply Chain Management
- Smart Warehousing and Logistics Systems - Member
Person: Research & Teaching, Researcher