- Date: Thursday, April 12th, 2017, 2:30 pm
- Location: Building 3 - Level 5 - 5209
Abstract
Reliable
forecasting of wind power generation is crucial to optimal control of
costs in generation of electricity. In this work, 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.
Short bio
Ms Soumaya ElKantassi is a master student in Applied Mathematics and
Computational Science and a member of the Stochastic Numerics Group at
KAUST. She obtained her Bachelor degree in Engineering from Tunisia
Polytechnic School majoring in Economic and Scientific Management. Her
research interests are mainly stochastic modeling and uncertainty
quantification. Her current research focuses on statistical inference
methods and stochastic differential equations.
https://stochastic_numerics.kaust.edu.sa/Pages/elkantassi.aspx