uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An autocorrelation-based copula model for generating realistic clear-sky index time-series
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)ORCID iD: 0000-0003-4887-9547
2017 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 158, p. 9-19Article in journal (Refereed) Published
Abstract [en]

This study presents a method for using copulas to model the temporal variability of the clear-sky index, which in turn can be used to produce realistic time-series of photovoltaic power generation. The method utilizes the autocorrelation function of a clear-sky index time-series, and based on that a correlation matrix is set up for the dependency between clear-sky indices at Ntime-steps. With the use of this correlation matrix an N-dimensional copula function is configured so that correlated samples for these N time-steps can be obtained. Results from the copula model are compared with the original data set and an uncorrelated model based on zero correlated clear-sky index data in terms of distribution, autocorrelation, step changes and distribution. The copula model is shown to be superior to the uncorrelated model in these aspects. As a validation the model is tested with solar irradiance for two different geographical regions: Norrköping, Sweden and Hawaii, USA. The copula model is also applied to a set of bins of daily mean clear-sky index and the use of bins is shown to improve the results.

Place, publisher, year, edition, pages
2017. Vol. 158, p. 9-19
Keyword [en]
Autocorrelation function, Copula modeling, Probability distribution modeling
National Category
Probability Theory and Statistics Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-330876DOI: 10.1016/j.solener.2017.09.028ISI: 000418974500002OAI: oai:DiVA.org:uu-330876DiVA, id: diva2:1147354
Available from: 2017-10-05 Created: 2017-10-05 Last updated: 2018-02-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Munkhammar, JoakimWidén, Joakim

Search in DiVA

By author/editor
Munkhammar, JoakimWidén, Joakim
By organisation
Solid State Physics
In the same journal
Solar Energy
Probability Theory and StatisticsEngineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 115 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf