INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.
Type
Conference PaperKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2017-10-03Online Publication Date
2017-10-03Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/626375
Metadata
Show full item recordAbstract
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.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
ECCOMASAdditional Links
https://www.eccomasproceedia.org/conferences/thematic-conferences/uncecomp-2017/5377ae974a485f413a2113503eed53cd6c53
10.7712/120217.5377.16899