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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]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 137265267
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Probabilistic forecast reconciliation with applications to wind power and electric load.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Jeon%2C+Jooyoung%22">Jeon, Jooyoung</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> j.jeon@bath.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Panagiotelis%2C+Anastasios%22">Panagiotelis, Anastasios</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> Anastasios.Panagiotelis@monash.edu</i><br /><searchLink fieldCode="AR" term="%22Petropoulos%2C+Fotios%22">Petropoulos, Fotios</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> f.petropoulos@bath.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Dec2019, Vol. 279 Issue 2, p364-379. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Electrical+load%22">Electrical load</searchLink><br /><searchLink fieldCode="DE" term="%22Load+forecasting+%28Electric+power+systems%29%22">Load forecasting (Electric power systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+windings%22">Electric windings</searchLink><br /><searchLink fieldCode="DE" term="%22Reconciliation%22">Reconciliation</searchLink><br /><searchLink fieldCode="DE" term="%22Bidding+strategies%22">Bidding strategies</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Crete+%28Greece%29%22">Crete (Greece)</searchLink><br /><searchLink fieldCode="DE" term="%22Boston+%28Mass%2E%29%22">Boston (Mass.)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=137265267
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ejor.2019.05.020
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 364
    Subjects:
      – SubjectFull: Electrical load
        Type: general
      – SubjectFull: Load forecasting (Electric power systems)
        Type: general
      – SubjectFull: Wind forecasting
        Type: general
      – SubjectFull: Wind power
        Type: general
      – SubjectFull: Electric windings
        Type: general
      – SubjectFull: Reconciliation
        Type: general
      – SubjectFull: Bidding strategies
        Type: general
      – SubjectFull: Crete (Greece)
        Type: general
      – SubjectFull: Boston (Mass.)
        Type: general
    Titles:
      – TitleFull: Probabilistic forecast reconciliation with applications to wind power and electric load.
        Type: main
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          Name:
            NameFull: Jeon, Jooyoung
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            NameFull: Panagiotelis, Anastasios
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            NameFull: Petropoulos, Fotios
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            – D: 01
              M: 12
              Text: Dec2019
              Type: published
              Y: 2019
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            – TitleFull: European Journal of Operational Research
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