Wind speed and direction prediction for wind farms using support vector regression

Predicting wind speed and direction is one of the most important and critic tasks in a wind farm, since wind turbine blades motion and thus energy production is closely related to wind behaviour. Machine learning techniques are often used to predict the non-linear wind evolution. In this context, this paper proposes a short term wind data prediction model based on support vector machines in their regression mode, which have the advantage of being simple, fast and well adapted for the short term. This research tries also to prove how wind direction may influence power generation, and why it is important to predict it. A real data set of wind speed and direction historical values is used, from Sidi Daoud wind farm, north-eastern Tunisia, in order to evaluate the proposed model. This forecasting system predicts wind speed and direction for the short term, from one to 10 hours in advance, using a set of past samples.