Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models by Soumaya ElKantassi

Abstract

Reliable forecasting of wind power generation is crucial to optimal control of costs in the 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.

Brief Biography

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's 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.