Prediction of diffuse solar irradiance using machine learning and multivariable regression

Siwei Lou, Danny H.W. Li, Joseph C. Lam and Wilco W.H. Chan

Applied Energy, 2016, vol. 181, issue C, 367-374

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 21.5W/m2 and 30W/m2 for Hong Kong and Denver, USA, respectively. With the systematic recording of the five variables for more than 35years, 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.

Keywords: Solar energy; Diffuse irradiance; Boosted regression tree; Logistic regression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:181:y:2016:i:c:p:367-374

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DOI: 10.1016/j.apenergy.2016.08.093

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