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dc.contributor.authorElkantassi, Soumaya
dc.contributor.authorKalligiannaki, Evangelia
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-12-14T12:34:04Z
dc.date.available2017-12-14T12:34:04Z
dc.date.issued2017-10-03
dc.identifier.citationElkantassi S, Kalligiannaki E, Tempone R (2017) INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS. Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017) . Available: http://dx.doi.org/10.7712/120217.5377.16899.
dc.identifier.doi10.7712/120217.5377.16899
dc.identifier.urihttp://hdl.handle.net/10754/626375
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.publisherECCOMAS
dc.relation.urlhttps://www.eccomasproceedia.org/conferences/thematic-conferences/uncecomp-2017/5377
dc.rightsArchived with thanks to Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)
dc.subjectIndirect inference
dc.subjectwind power
dc.subjectprobabilistic forecasting
dc.subjectmodel selection
dc.subjectsensitivity
dc.titleINFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.
dc.typeConference Paper
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalProceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)
dc.eprint.versionPublisher's Version/PDF
kaust.personElkantassi, Soumaya
kaust.personKalligiannaki, Evangelia
kaust.personTempone, Raul
refterms.dateFOA2018-06-13T12:45:19Z
dc.date.published-online2017-10-03
dc.date.published-print2017


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