Short-term probabilistic forecasting of wind speed using stochastic differential equations

It is widely accepted today that probabilistic forecasts of wind power production constitute valuable information that can allow both wind power producers and power system operators to exploit this form of renewable energy economically, while mitigating the potential adverse effects relating to its variable and uncertain nature. In order to provide reliable wind power forecasts for periods beyond a couple of hours, forecasts of the wind speed are fundamental. In this paper, we propose a modeling framework for wind speed that is based on stochastic differential equations. We show that stochastic differential equations allow us to capture the time dependence structure of wind speed prediction errors naturally (from 1 to 24 h ahead) and, most importantly, to derive point and quantile forecasts, predictive distributions, and time-path trajectories (also referred to as scenarios or ensemble forecasts), all using one single stochastic differential equation model that is characterized by a few parameters.