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dc.contributor.authorYang, Kaolee
dc.contributor.authorAziz, Mohammad
dc.date.accessioned2019-09-03T19:07:56Z
dc.date.available2019-09-03T19:07:56Z
dc.date.issued2018-05
dc.identifier.urihttp://digital.library.wisc.edu/1793/79304
dc.descriptionColor poster with text, charts, and graphs.en_US
dc.description.abstractThe fast depletion of non-renewable energy has challenged researchers to look for clean and fuel-efficient sources. Among the bioenergies, wind energy has shown to be clean, fuel-efficient, and cost-effective. Thus, researchers are actively seeking for ways to describe wind speed distribution. The most commonly used distribution is Weibull. However, typical wind speed show skewness and bimodality therefore we focused on flexible skew distributions. We demonstrated the accuracy of each model with application to three datasets and a Monte Carlo simulation.en_US
dc.description.sponsorshipRonald E. McNair Postbaccalaureate Program; University of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectWind speed distribution functionen_US
dc.subjectMonte Carlo simulationen_US
dc.subjectPostersen_US
dc.titleModeling Wind Speed Distributions Using Skewed Probability Functions : A Monte Carlo Simulation with Applications to Real Wind Speed Dataen_US
dc.typePresentationen_US


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    Posters of collaborative student/faculty research presented at Student Research Day

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