Wind speed forecasting via ensemble Kalman Filter

Wind speed prediction is crucial for electricity system security and planning. In this paper, ensemble Kalman Filter (EnKF) method is employed to predict 10 minutes averaged wind speed. We use Auto-Regressive and Moving Average (ARMA) model as the state function of EnKF, perturb initial wind data to generate ensembles and forecast wind speed data via EnKF. The comparison with in-situ measurements shows that EnKF may be suitable for wind speed prediction and improve grid integration of wind energy.