Probabilistic forecast reconciliation with applications to wind power and electric load

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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.
Publisher
ELSEVIER
Issue Date
2019-12
Language
English
Citation

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, v.279, no.2, pp.364 - 379

ISSN
0377-2217
DOI
10.1016/j.ejor.2019.05.020
URI
http://hdl.handle.net/10203/270839
Appears in Collection
RIMS Journal Papers
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