Title :
The Study of Wind Power Combination Prediction
Author :
Han, Shuang ; Liu, Yongqian
Author_Institution :
Renewable Energy Sch., North China Electr. Power Univ., Beijing, China
Abstract :
According to the maximum entropy principle and the characteristic of wind farm power series, a combined wind power prediction model was proposed. The wind power series is non-gauss distribution, so high central moment were added to prediction model besides the second central moment. The prediction results showed that the proposed model can improve the prediction precision.
Keywords :
maximum entropy methods; wind power plants; maximum entropy principle; non-gauss distribution; wind farm power series; wind power combination prediction; wind power series; Artificial neural networks; Information entropy; Information theory; Load modeling; Prediction methods; Predictive models; Renewable energy resources; Support vector machines; Wind energy; Wind farms;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
DOI :
10.1109/APPEEC.2010.5448145
