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Probabilistic forecast reconciliation with applications to wind power and electric load.

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Title: Probabilistic forecast reconciliation with applications to wind power and electric load.
Authors: Jeon, Jooyoung1,2 (AUTHOR) j.jeon@bath.ac.uk, Panagiotelis, Anastasios3 (AUTHOR) Anastasios.Panagiotelis@monash.edu, Petropoulos, Fotios1 (AUTHOR) f.petropoulos@bath.ac.uk
Source: European Journal of Operational Research. Dec2019, Vol. 279 Issue 2, p364-379. 16p.
Subjects: Electrical load, Load forecasting (Electric power systems), Wind forecasting, Wind power, Electric windings, Reconciliation, Bidding strategies
Geographic Terms: Crete (Greece), Boston (Mass.)
Abstract: • Temporal Hierarchy is used to improve density forecasts. • Sampling methods and reconciliation methods are presented and compared. • Cross-validation reconciliation is proposed for hierarchical forecasting. • Methods are evaluated with forecasting case studies on wind power and electric load. 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Temporal Hierarchy is used to improve density forecasts. • Sampling methods and reconciliation methods are presented and compared. • Cross-validation reconciliation is proposed for hierarchical forecasting. • Methods are evaluated with forecasting case studies on wind power and electric load. 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. [ABSTRACT FROM AUTHOR]
ISSN:03772217
DOI:10.1016/j.ejor.2019.05.020