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
A spatiotemporal Markov-chain mixture distribution model of the clear-sky index
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0003-4887-9547
2019 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 179, p. 398-409Article in journal (Refereed) Published
Abstract [en]

This study presents a spatiotemporal Markov-chain mixture distribution model of the clear-sky index for an arbitrary number of locations, and is particularly suited for simulations of small-scale spatial networks with a span of 10 km or less. The model is statistical, but in practice data-driven and based on clear-sky index input from an arbitrary number of locations to generate synthetic time-series for the same locations. When trained on clear-sky index data based on the NREL Hawaii network radiometer solar irradiance data, dispersed within 1 km x 1.2 km, the model is shown to have high goodness-of-fit compared with test data from the network in terms of probability distributions, autocorrelations, location pair-correlations and k-lag correlations between locations. It is also shown to perform comparably to state of the art temporal, spatial and spatiotemporal clear-sky index generators. All measures of model goodness-of-fit are shown to improve with increased number of bins, up to a certain limit of N > 4, where the performance improvements reaches a plateau. The results are also shown to be insensitive with respect to choice of training and test data sets as well as number of output time-steps.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 179, p. 398-409
Keywords [en]
Clear-sky index, spatiotemporal variability, Markov-chain modeling, Mixture distribution modeling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-378739DOI: 10.1016/j.solener.2018.12.064ISI: 000458942300039OAI: oai:DiVA.org:uu-378739DiVA, id: diva2:1295206
Funder
Swedish Energy AgencyAvailable from: 2019-03-11 Created: 2019-03-11 Last updated: 2019-03-11Bibliographically 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 Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 41 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