Title :
Wind speed forecast for wind farms based on ARMA-ARCH model
Author :
Gao, Shan ; He, Yu ; Chen, Hao
Author_Institution :
Dept. of Electr. Eng., Southeast Univ., Nanjing, China
Abstract :
Short term wind forecasting is a very important work to the operation of wind farms and power systems. In this paper, ARCH (Autoregressive Conditional Heteroscedasticity) effects of wind data series are analyzed with Eviews software. Firstly, an ARMA (Autoregressive Moving Average) model of wind speed time series is built. Secondly, ARCH (Autoregressive Conditional Heteroscedasticity) effect of the residual of ARMA model is tested by Lagrange Multiplier, and the corresponding ARMA-ARCH model is set up. Lastly, forecasting performances of ARMA-ARCH model are compared with ARMA model. Validation of ARMA-ARCH model is proved. And the results show that ARMA-ARCH model possesses higher accuracy.
Keywords :
autoregressive moving average processes; wind power plants; Eviews software; Lagrange multiplier; autoregressive conditional heteroscedasticity; autoregressive moving average; wind data series; wind farms; wind speed forecasting; Autoregressive processes; Data analysis; Lagrangian functions; Power system analysis computing; Power system modeling; Predictive models; Testing; Wind farms; Wind forecasting; Wind speed; ARCH effect; ARCH-ARCH model; ARMA model; volatility cluster; wind speed forecasting;
Conference_Titel :
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4934-7
DOI :
10.1109/SUPERGEN.2009.5348142
