DocumentCode :
3405329
Title :
Short-term prediction of wind power combining GM(1,1) model with cloud model
Author :
Xiaojuan Han ; Fangyuan Meng ; Zhihui Song ; Xiangjun Li
Author_Institution :
Coll. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China
fYear :
2012
fDate :
15-17 Aug. 2012
Firstpage :
191
Lastpage :
195
Abstract :
This paper proposes a new method to predict wind power of wind farm using the combination of GM(1,1) model and cloud model. The original wind power signals are decomposed into high frequency part and low frequency part by wavelet decomposition. Cloud model is constructed to predict wind power of high frequency part and GM(1,1) model is used to predict wind power of low frequency part. The predicted power can be obtained by high frequency part and low frequency part. The simulation example shows that the method proposed in this paper is obviously better than single predicting method and the effectiveness of the method is verified by the predicting results.
Keywords :
queueing theory; wavelet transforms; wind power plants; GM(1,1) model; cloud model; high frequency part; low frequency part; short-term prediction; wavelet decomposition; wind farm; wind power; wind power signals; Computational modeling; Entropy; Forecasting; Generators; Predictive models; Wind power generation; Wind speed; GM(1,1); cloud model; combination prediction; power prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Logistics (ICAL), 2012 IEEE International Conference on
Conference_Location :
Zhengzhou
ISSN :
2161-8151
Print_ISBN :
978-1-4673-0362-0
Electronic_ISBN :
2161-8151
Type :
conf
DOI :
10.1109/ICAL.2012.6308195
Filename :
6308195
Link To Document :
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