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dc.contributor.authorTagle, Felipe
dc.contributor.authorCastruccio, Stefano
dc.contributor.authorCrippa, Paola
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2017-12-28T07:32:11Z
dc.date.available2017-12-28T07:32:11Z
dc.date.issued2017-03-13
dc.identifier.urihttp://hdl.handle.net/10754/626467
dc.description.abstractFacing increasing domestic energy consumption from population growth and industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden its energy mix by expanding investment in renewable energy sources, including wind energy. A preliminary task in the development of wind energy infrastructure is the assessment of wind energy potential, a key aspect of which is the characterization of its spatio-temporal behavior. In this study we examine the impact of internal climate variability on seasonal wind power density fluctuations using 30 simulations from the Large Ensemble Project (LENS) developed at the National Center for Atmospheric Research. Furthermore, a spatio-temporal model for daily wind speed is proposed with neighbor-based cross-temporal dependence, and a multivariate skew-t distribution to capture the spatial patterns of higher order moments. The model can be used to generate synthetic time series over the entire spatial domain that adequately reproduces the internal variability of the LENS dataset.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1703.04312v1
dc.relation.urlhttp://arxiv.org/pdf/1703.04312v1
dc.rightsArchived with thanks to arXiv
dc.titleAssessing Potential Wind Energy Resources in Saudi Arabia with a Skew-t Distribution
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionSchool of Mathematics & Statistics, Newcastle University, Newcastle Upon Tyne, NE1 7RU United Kingdom.
dc.contributor.institutionSchool of Civil Engineering & Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU United Kingdom.
dc.identifier.arxivid1703.04312
kaust.personGenton, Marc G.
kaust.grant.numberOSR-2015-CRG4-2640
refterms.dateFOA2018-06-14T05:31:55Z


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