1. Prediction of diffuse solar irradiance using machine learning and multivariable regression.
- Author
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Lou, Siwei, Li, Danny H.W., Lam, Joseph C., and Chan, Wilco W.H.
- Subjects
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MACHINE learning , *SPECTRAL irradiance , *CLOUDINESS , *CLIMATE change , *ATMOSPHERIC temperature , *MULTIVARIATE analysis - Abstract
The paper studies the horizontal global, direct-beam and sky-diffuse solar irradiance data measured in Hong Kong from 2008 to 2013. A machine learning algorithm was employed to predict the horizontal sky-diffuse irradiance and conduct sensitivity analysis for the meteorological variables. Apart from the clearness index (horizontal global/extra atmospheric solar irradiance), we found that predictors including solar altitude, air temperature, cloud cover and visibility are also important in predicting the diffuse component. The mean absolute error ( MAE ) of the logistic regression using the aforementioned predictors was less than 21.5 W/m 2 and 30 W/m 2 for Hong Kong and Denver, USA, respectively. With the systematic recording of the five variables for more than 35 years, the proposed model would be appropriate to estimate of long-term diffuse solar radiation, study climate change and develope typical meteorological year in Hong Kong and places with similar climates. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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