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dc.contributor.advisorTempone, Raul
dc.contributor.advisorKalligiannaki, Evangelia
dc.contributor.authorElkantassi, Soumaya
dc.date.accessioned2017-05-10T07:23:19Z
dc.date.available2017-05-10T07:23:19Z
dc.date.issued2017-04
dc.identifier.doi10.25781/KAUST-8V2P2
dc.identifier.urihttp://hdl.handle.net/10754/623461
dc.description.abstractReliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.
dc.language.isoen
dc.subjectIndirect inference
dc.subjectwind power
dc.subjectprobabilistic forecasting
dc.subjectmodel selection
dc.subjectsensitivity
dc.titleProbabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberHuser, Raphaël
dc.contributor.committeememberScavino, Marco
thesis.degree.disciplineApplied Mathematics and Computational Science
thesis.degree.nameMaster of Science
refterms.dateFOA2018-05-02T00:00:00Z


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