Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, 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 for the period of March 1 to May 31, 2016.
|Date of Award||Apr 2017|
- Computer, Electrical and Mathematical Science and Engineering
|Supervisor||Raul Tempone (Supervisor) & Evangelia Kalligiannaki (Supervisor)|
- Indirect inference
- wind power
- probabilistic forecasting
- model selection