Wind speed forecasting using autoregressive moving average/generalized autoregressive conditional heteroscedasticity model

• Published in: European transactions on electrical power 2012-07, Vol.22 (5), p.662-673
• Main Authors: Jiang, Wen, Yan, Zheng, Feng, Dong-Han, Hu, Zhi
• Format: Article
• Language: English
• Subjects: ARMA-GARCH; forecast; improved particle swarm optimization; quasi-maximum likelihood estimator; wind speed
• ISSN: 1430-144X; 1546-3109
• DOI: 10.1002/etep.596

SUMMARY In this paper, a hybrid model of autoregressive moving average and generalized autoregressive conditional heteroscedasticity is proposed to forecast wind speed. In this model, the conditional variance of an observation depends linearly on the conditional variance of the previous observations and on the previous prediction errors. This conditional variance can capture the feature that the predictability of meteorological variables is not constant but shows regular variations. The quasi‐maximum likelihood estimator was used to estimate parameters of the proposed model. An improved particle swarm optimization was proposed to solve the solution of the autoregressive moving average/generalized autoregressive conditional heteroscedasticity model through the log‐quasi‐likelihood function. Four different indices are introduced to demonstrate the performance of the proposed model. Generated results of different season sample sets were compared with their corresponding values when using the autoregressive moving average model. The simulation results validate the effectiveness, accuracy, and superiority of the proposed model for wind speed prediction. Copyright © 2011 John Wiley & Sons, Ltd.