Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62152
Title: Prediction of diffuse solar irradiance using machine learning and multivariable regression
Authors: Lou, S
Li, DHW
Lam, JC
Chan, W 
Keywords: Boosted regression tree
Diffuse irradiance
Logistic regression
Solar energy
Issue Date: 2016
Publisher: Pergamon Press
Source: Applied energy, 2016, v. 181, p. 367-374 How to cite?
Journal: Applied energy 
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/m2 and 30 W/m2 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.
URI: http://hdl.handle.net/10397/62152
ISSN: 0306-2619
EISSN: 1872-9118
DOI: 10.1016/j.apenergy.2016.08.093
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