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

fYear

2012

fDate

29-31 May 2012

Firstpage

385

Lastpage

388

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2012 Eighth International Conference on

Conference_Location

Chongqing

ISSN

2157-9555

Print_ISBN

978-1-4577-2130-4

Type

conf

DOI

10.1109/ICNC.2012.6234680

Filename

6234680