Probabilistic forecast reconciliation with applications to wind power and electric load

Jooyoung Jeon, Anastasios Panagiotelis, Fotios Petropoulos

Research output: Contribution to journalArticlepeer-review

46 Citations (SciVal)
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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.
Original languageEnglish
Pages (from-to)364-379
Number of pages16
JournalEuropean Journal of Operational Research
Issue number2
Early online date22 May 2019
Publication statusPublished - 1 Dec 2019


  • Aggregation
  • Cross-validation
  • Forecasting
  • 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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