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The Scientific World JournalVolume 2013 2013, Article ID 548370, 8 pages

Research ArticleSchool of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Republic of Korea

Received 31 August 2013; Accepted 8 October 2013

Academic Editors: M. L. Ferrari and D. C. Rakopoulos

Copyright © 2013 Sivanagaraja Tatinati and Kalyana C. Veluvolu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose a hybrid method for forecasting the wind speed. The wind speed datais first decomposed into intrinsic mode functions IMFs with empirical mode decomposition. Basedon the partial autocorrelation factor of the individual IMFs, adaptive methods are thenemployed for the prediction of IMFs. Least squares-support vector machines are employed for IMFswith weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs withhigh correlation factor. Multistep prediction with the proposed hybrid method resulted in improvedforecasting. Results with wind speed data show that the proposed method provides better forecastingcompared to the existing methods.





Author: Sivanagaraja Tatinati and Kalyana C. Veluvolu

Source: https://www.hindawi.com/



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