DocumentCode :
3269257
Title :
Solar radiation prediction using statistical approaches
Author :
Ji, Wu ; Chan ; Loh, Jw ; Choo, Fh ; Chen, Lh
Author_Institution :
Sch. of Electr.&Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear :
2009
fDate :
8-10 Dec. 2009
Firstpage :
1
Lastpage :
5
Abstract :
Statistical approach is often used in time series analysis. One of its uses is to predict the future trend of a time series. This can be applied in many applications such as solar radiation, economics and other researches related to time series. In this paper, we use several classic statistical models to fit the solar radiation time series. The goal is to find a suitable radiation model in predicting the trend of solar radiation time series. The simulation result shows that linear regression has better performance than other models such as the auto regression, auto regression integrate moving average. The linear regression method requires a number of previous data for prediction. Simulation shows that a list of 10 to 15 past data values yields optimal result.
Keywords :
regression analysis; statistical analysis; sunlight; time series; Singapore; auto regression integrate moving average; classic statistical models; linear regression method; solar radiation prediction; solar radiation time series; sunlight conversion; Data acquisition; Data mining; Kernel; Linear regression; Nearest neighbor searches; Predictive models; Smoothing methods; Solar energy; Solar radiation; Time series analysis; solar radiation prediction; statistical approach;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing, 2009. ICICS 2009. 7th International Conference on
Conference_Location :
Macau
Print_ISBN :
978-1-4244-4656-8
Electronic_ISBN :
978-1-4244-4657-5
Type :
conf
DOI :
10.1109/ICICS.2009.5397540
Filename :
5397540
Link To Document :
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