Journal
APPLIED ENERGY
Volume 181, Issue -, Pages 367-374Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.08.093
Keywords
Solar energy; Diffuse irradiance; Boosted regression tree; Logistic regression
Categories
Funding
- Research Grant Council of the Hong Kong Special Administrative Region, China [9041896 (CityU 117713)]
- City University of Hong Kong
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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. (C) 2016 Elsevier Ltd. All rights reserved.
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