Prediction of diffuse solar irradiance using machine learning and multivariable regression

Siwei Lou, Danny H.W. Li, Joseph C. Lam, Wai Hung Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

86 Citations (Scopus)


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/m2and 30 W/m2for 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.
Original languageEnglish
Pages (from-to)367-374
Number of pages8
JournalApplied Energy
Publication statusPublished - 1 Nov 2016


  • Boosted regression tree
  • Diffuse irradiance
  • Logistic regression
  • Solar energy

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • General Energy
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law


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