MS-Thesis Defense: Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models By Soumaya ElKantassi, PhD candidate of Professor Raul Tempone (KAUST)

12 April, 2017

  • Date: ​Thursday, April 12th, 2017, 2:30 pm
  • Location: Building 3 - Level 5 - 5209


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.