Title :
Wind speed forecasting via ensemble Kalman Filter
Author :
Wei, Zhang ; Weimin, Wang
Author_Institution :
Shenzhen Institutes of Adv. Technol., Chinese Acad. of Sci., Shenzhen, China
Abstract :
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.
Keywords :
Kalman filters; autoregressive moving average processes; power generation planning; power system security; wind power; ARMA; EnKF; Kalman Filter; auto-regressive and moving average model; electricity system planning; electricity system security; wind energy; wind speed forecasting; wind speed prediction; Data security; Equations; Error analysis; Power system planning; Power system security; Predictive models; Technology forecasting; Time series analysis; Wind forecasting; Wind speed; ARMA; EnKF; time series model; wind speed forecasting;
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
DOI :
10.1109/ICACC.2010.5487187
