INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

Soumaya Elkantassi; Evangelia Kalligiannaki; Raul Tempone

doi:10.7712/120217.5377.16899

INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

Soumaya Elkantassi, Evangelia Kalligiannaki, Raul Tempone

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 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 language	English (US)
Title of host publication	Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)
Publisher	ECCOMAS
Pages	381-393
Number of pages	13
ISBN (Print)	9786188284449
DOIs	https://doi.org/10.7712/120217.5377.16899
State	Published - Oct 3 2017

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KAUST Repository Item: Exported on 2020-10-01

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10.7712/120217.5377.16899

https://repository.kaust.edu.sa/bitstream/10754/626375/1/16899.pdf

Cite this

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title = "INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.",

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.",

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INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS. / Elkantassi, Soumaya; Kalligiannaki, Evangelia; Tempone, Raul.
Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017). ECCOMAS, 2017. p. 381-393.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Kalligiannaki, Evangelia

AU - Tempone, Raul

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N2 - 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.

AB - 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.

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BT - Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017)

PB - ECCOMAS

ER -

INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.

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