INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

Soumaya Elkantassi, Evangelia Kalligiannaki, Raul Tempone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationProceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)
PublisherECCOMAS
Pages381-393
Number of pages13
ISBN (Print)9786188284449
DOIs
StatePublished - Oct 3 2017

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