RESEARCH • COMPUTER SCIENCE

Wind power experiences a tremendous development of its installed capacities in the world. Though, the integration of wind generation causes difficulties in the management of power systems. In the context of deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3 day ahead eases the integration of wind generation. Wind power forecasting plays a very important role to improve the economical and technical integration of a large share of wind energy into an existing electricity grid. The wind power forecasting accuracy is directly related to the need for balancing energy, system reliability of the electricity supply system and hence the cost of wind power integration. Wind power forecasts are provided in the form of point predictions, which correspond to most likely power production for a given horizon. Wind is perfect to deal with energy security and environment concerns, but its variability and average predictability may cause operational problem, which need more actions and better services. The very-short–term forecasting approach consists of statistical models that are based on the time series approach and includes such models as the Kalman Filters, ARMA, ARX, and Box-Jenkins forecasting methods. These types of models only take as inputs past values from the forecaste