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

Volume

2

fYear

2010

fDate

27-29 March 2010

Firstpage

73

Lastpage

77

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5487187

Filename

5487187