Cox–Ingersoll–Ross model for wind speed modeling and forecasting

We propose a dynamic model for the squared norm of the wind speed which is a Markov diffusion process. It presents several advantages. Since the transition probability densities are in closed form, it can be calibrated with the maximum likelihood method. It presents nice modeling features both in terms of marginal probability density function and temporal correlation. We have tested the model with real wind speed data set provided by the National Renewable Energy Laboratory. The model fits very well with the data. Besides, we obtained a very good performance in forecasting wind speed at short term. This is an interesting perspective for operational use in industry.