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dc.contributor.authorTagle, Felipe
dc.contributor.authorGenton, Marc G.
dc.contributor.authorYip, Andrew
dc.contributor.authorMostamandi, Suleiman
dc.contributor.authorStenchikov, Georgiy L.
dc.contributor.authorCastruccio, Stefano
dc.date.accessioned2020-05-07T07:27:30Z
dc.date.available2020-05-07T07:27:30Z
dc.date.issued2020-05-03
dc.date.submitted2019-10-22
dc.identifier.citationTagle, F., Genton, M. G., Yip, A., Mostamandi, S., Stenchikov, G., & Castruccio, S. (2020). A high-resolution bilevel skew- t stochastic generator for assessing Saudi Arabia’s wind energy resources. Environmetrics. doi:10.1002/env.2628
dc.identifier.issn1180-4009
dc.identifier.issn1099-095X
dc.identifier.doi10.1002/env.2628
dc.identifier.urihttp://hdl.handle.net/10754/662754
dc.description.abstractSaudi Arabia has recently established its renewable energy targets as part of its “Vision 2030” proposal, which represents a roadmap for reducing the country's dependence on oil over the next decade. This study provides a foundational assessment of the wind resource in Saudi Arabia that serves as a guide for the development of the outlined wind energy component. The assessment is based on a new high-resolution weather simulation of the region generated with the Weather Research and Forecasting (WRF) model. Furthermore, we propose a spatiotemporal stochastic generator of daily wind speeds that assists in characterizing the uncertainty of the energy estimates. The stochastic generator considers a vector autoregressive structure in time, with innovations from a novel biresolution model based on a skew-t distribution with a low-dimensional latent structure. Estimation of the spatial model parameters is performed using a Monte Carlo expectation-maximization (EM) algorithm, which achieves inference over approximately 184 million points and enables to capture the spatial patterns of the higher order moments that typically characterize high-resolution wind fields. Our results identify regions along the western mountain ranges and central escarpments that are suitable for the deployment of wind energy infrastructure. According to the assessment, between 30 and 70% of the national electricity demand could be met by wind energy.
dc.description.sponsorshipThe research reported in this publication was supported by funding from the KAUST Office of Sponsored Research (OSR)Competitive Research Grants (CRG) under award no. OSR-2015-CRG4-2640.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1002/env.2628
dc.rightsArchived with thanks to Environmetrics
dc.titleA high-resolution bilevel skew- t stochastic generator for assessing Saudi Arabia's wind energy resources
dc.typeArticle
dc.contributor.departmentAtmospheric and Climate Modeling GroupKing Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
dc.identifier.journalEnvironmetrics
dc.rights.embargodate2021-05-04
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Applied and Computational Mathematics and StatisticsUniversity of Notre Dame Notre Dame Indiana USA
kaust.personGenton, Marc G.
kaust.personYip, Andrew
kaust.personMostamandi, Suleiman
kaust.personStenchikov, Georgiy L.
kaust.grant.numberOSR-2015-CRG4-2640
dc.date.accepted2020-03-27
kaust.acknowledged.supportUnitCompetitive Research
kaust.acknowledged.supportUnitKAUST Office of Sponsored Research (OSR)
dc.date.published-online2020-05-03
dc.date.published-print2020-11


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