On April 12th, 2017, Soumaya ElKantassi successfully defended her MS Thesis entitled "Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models".

        Supervisor: Prof. Raul Tempone

        Co-supervisor: Dr. Evangelia Kalligiannaki


        Committee Member: Prof. Marco Scavino

        Committee Member: Prof. Raphael Huser


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.