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
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