INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

Handle URI:
http://hdl.handle.net/10754/626375
Title:
INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.
Authors:
Elkantassi, Soumaya ( 0000-0003-2610-3676 ) ; Kalligiannaki, Evangelia; Tempone, Raul ( 0000-0003-1967-4446 )
Abstract:
Reliable 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.
KAUST Department:
Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Elkantassi 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.
Publisher:
Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece
Journal:
Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)
Issue Date:
3-Oct-2017
DOI:
10.7712/120217.5377.16899
Type:
Conference Paper
Additional Links:
https://www.eccomasproceedia.org/conferences/thematic-conferences/uncecomp-2017/5377
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorElkantassi, Soumayaen
dc.contributor.authorKalligiannaki, Evangeliaen
dc.contributor.authorTempone, Raulen
dc.date.accessioned2017-12-14T12:34:04Z-
dc.date.available2017-12-14T12:34:04Z-
dc.date.issued2017-10-03en
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.en
dc.identifier.doi10.7712/120217.5377.16899en
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.en
dc.publisherInstitute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greeceen
dc.relation.urlhttps://www.eccomasproceedia.org/conferences/thematic-conferences/uncecomp-2017/5377en
dc.rightsArchived with thanks to Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)en
dc.subjectIndirect inferenceen
dc.subjectwind poweren
dc.subjectprobabilistic forecastingen
dc.subjectmodel selectionen
dc.subjectsensitivityen
dc.titleINFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.en
dc.typeConference Paperen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)en
dc.eprint.versionPublisher's Version/PDFen
kaust.authorElkantassi, Soumayaen
kaust.authorKalligiannaki, Evangeliaen
kaust.authorTempone, Raulen
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