A Hybrid Approach for Short-Term Forecasting of Wind Speed

We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs...

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Veröffentlicht in:TheScientificWorld Jg. 2013; H. 2013; S. 1 - 8
Hauptverfasser: Tatinati, Sivanagaraja, Veluvolu, Kalyana C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
John Wiley & Sons, Inc
Wiley
Schlagworte:
Algorithms
Alternative energy sources
Autoregressive models
Correlation coefficients
Decomposition
Forecasting
Forecasting - methods
Indexing in process
Kalman filters
Least-Squares Analysis
Meteorology
Models, Statistical
Models, Theoretical
Regression Analysis
Support Vector Machine
Support vector machines
Wind
Wind speed
ISSN:2356-6140, 1537-744X, 1537-744X
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Zusammenfassung:We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.
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Academic Editors: M. L. Ferrari and D. C. Rakopoulos
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2013/548370