Title :
Application of artificial intelligence to wind forecasting: An enhanced combined approach
Author :
Wan, Yan ; Zhang, Han
Author_Institution :
Sch. of Econ. & Manage., Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Along with large-scale application of wind power, power forecasting becomes increasingly important in handling wind intermittency and integrating wind power to electric grid. This paper proposes a forecasting combination approach which makes use of the forecast results of NN (Neural Networks), SVM (Support Vector Machine), and FIS (Fuzzy Inference System) models to improve the forecast accuracy. Three types of combination methods have been tested in this paper and the one based on MSE is proved to be most effective in terms of NMAE (Normalized Mean Absolute Error) and NRMSE (Normalized Root Mean Squared Error). An improved data selection scheme is also put forward to further enhance forecast accuracy.
Keywords :
artificial intelligence; fuzzy set theory; inference mechanisms; load forecasting; mean square error methods; neural nets; power engineering computing; power grids; support vector machines; wind power; FIS; NMAE; NN; NRMSE; SVM; artificial intelligence; data selection scheme; electric grid; fuzzy inference system; neural network; normalized mean absolute error; normalized root mean squared error; power forecasting; support vector machine; wind forecasting; wind intermittency; wind power; Accuracy; Forecasting; Predictive models; Support vector machines; Wind forecasting; Wind power generation; Back-Propagation Neural Networks (BP NN); Fuzzy Inference System (FIS); Support Vector Machine (SVM); forecast combination; power forecast;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234680
