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.
|
|
|---|