DocumentCode :
570549
Title :
Short-term wind speed forecasting based on non-parametric kernel density estimation
Author :
Jiegui Zhou ; Jia Hongjie ; Zhen Tian ; Linlin Hu
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
fYear :
2012
fDate :
21-24 May 2012
Firstpage :
1
Lastpage :
5
Abstract :
In this paper, a wind speed forecasting method based on non-parametric kernel density estimation is proposed. This method does not need to use any prior knowledge or make any additional assumptions of distributed data. This paper will show how to use N-W method which is one of non-parametric kernel estimator methods in wind speed forecasting, including how to get the stationary series, choose the dimension, the bandwidth, and how to calculate the confidence interval. The method is tested by utilizing the measured wind speed data of a wind farm from one month period of May 2010.
Keywords :
probability; wind power; AD 2010 05; N-W method; confidence interval; distributed data; nonparametric kernel density estimation; short-term wind speed forecasting; stationary series; wind farm; wind speed data; Correlation; Educational institutions; Estimation; Forecasting; Kernel; Predictive models; Wind speed; N-W estimation; confidence interval; data stationary; non-parametric kernel density estimation; wind speed forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Smart Grid Technologies - Asia (ISGT Asia), 2012 IEEE
Conference_Location :
Tianjin
Print_ISBN :
978-1-4673-1221-9
Type :
conf
DOI :
10.1109/ISGT-Asia.2012.6303377
Filename :
6303377
Link To Document :
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