Title :
Wind Park Power Forecasting Models and Comparison
Author :
Zhang, Qian ; Lai, Kin Keung ; Niu, Dongxiao ; Wang, Qiang
Author_Institution :
Sch. of Econ. & Manage., North China Electr. Power Univ. Baoding, Baoding, China
Abstract :
In this paper, four new forecasting models - univariate LS-SVM model and three hybrid models of ARIMA and LS-SVM models are introduced for wind power output forecasting. Historical data of 78 wind farms are used to compare and evaluate the performance of the best models. Empirical analysis indicates that the proposed univariate LS-SVM model and hybrid models can not significantly outperform linear models but they are not inferior to linear models.
Keywords :
forecasting theory; least squares approximations; power engineering computing; support vector machines; wind power plants; ARIMA; LS-SVM models; hybrid models; linear models; univariate LS-SVM model; wind farms; wind park power forecasting models; wind power output forecasting; Accuracy; Analytical models; Biological system modeling; Forecasting; Predictive models; Wind forecasting; Wind power generation; ARIMA; LS-SVM; hybrid models; wind power forecasting;
Conference_Titel :
Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4673-1365-0
DOI :
10.1109/CSO.2012.15
