Decomposing global solar radiation into its direct and diffuse components
Boland, J., Huang, J. and Ridley, B.
2013 Renewable and Sustainable Energy Reviews, Volume 28, December 2013, Pages 749-756
BRL model; Direct normal radiation; Diffuse radiation; Statistical modelling
Boland, J., Huang, J. and Ridley, B., (2013), "Decomposing global solar radiation into its direct and diffuse components", Renewable and Sustainable Energy Reviews, Volume 28, December 2013, Pages 749-756.
Abstract:
Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.
This publication in whole or part may be found online at: here.
