DocumentCode :
676641
Title :
Wind speed prediction based on empirical mode decomposition and improved LS-SVM
Author :
Chengchen Sun ; Yue Yuan
Author_Institution :
Coll. of Energy & Electr. Eng., Hohai Univ., Nanjing, China
fYear :
2013
fDate :
9-11 Sept. 2013
Firstpage :
1
Lastpage :
4
Abstract :
Wind speed forecasting plays an important role in sizing the capacity of the energy storage system and guaranteeing the security and stability of power system. In order to forecast wind speeds more accurately, a hybrid forecasting method based on empirical mode decomposition (EMD) and an improved least square support vector machine mode (LSSVM) has been proposed in this paper. Employing the EMD technique to decompose the measured wind speeds into many intrinsic mode function (IMF) components and a residue, which represent the original signal in both high-frequency and low-frequency signals. Meanwhile each IMF is analyzed and predicted using LS-SVM (high-frequency signals) and Persistence Approach (low-frequency signals), so does the residue. The sum of the predictive value for each decomposed component is the forecasted data. The proposed method was applied to the modeling and forecasting of a set of data from a given wind farm in Jiangsu Province, China. The results demonstrate the validity and practicability of the novel method. The forecasted results were compared to the measured values as well as those predicted with other traditional methods. The results indicate that the forecasting precision can be improved with the developed model.
Keywords :
least squares approximations; load forecasting; power system security; power system stability; support vector machines; wind power plants; China; IMF; Jiangsu province; LS-SVM; empirical mode decomposition; energy storage system; hybrid forecasting method; intrinsic mode function; least square support vector machine; power system security; power system stability; wind farm; wind speed forecasting; wind speed prediction; Empirical Mode Decomposition; Least Square - Support Vector Machine; Wind speed prediction;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Renewable Power Generation Conference (RPG 2013), 2nd IET
Conference_Location :
Beijing
Electronic_ISBN :
978-1-84919-758-8
Type :
conf
DOI :
10.1049/cp.2013.1853
Filename :
6718764
Link To Document :
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