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Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model

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  • Lu, Yunbo
  • Wang, Lunche
  • Zhu, Canming
  • Zou, Ling
  • Zhang, Ming
  • Feng, Lan
  • Cao, Qian

Abstract

Solar radiation is one of the cleanest sources of renewable energy, and it affects the carbon sink functions of terrestrial ecosystems. Although efforts have been made to establish solar radiation observation stations around the world, their coverage remains limited. Hence, the development of a wide variety of models and techniques is indispensable for obtaining effective solar radiation data. The aim of this study is to develop hybrid models with high computational speed and high accuracy to estimate global solar radiation (GSR) and quantify the uncertainty in GSR simulations caused by uncertainty in the measurements of atmospheric and surface parameters. The radiative transfer model (RTM) library for radiative transfer (LibRadtran) was coupled with six machine learning models: extreme gradient boosting (XGBoost), random forest (RF), multivariate adaptive regression splines (MARS), multilayer perceptron (MLP), deep neural networks (DNNs), and light gradient boosting machine (LightGBM). The estimated GSR was first compared to the inversion values of the GSR provided by the Aerosol Robotic Network (AERONET) and then validated using ground-based measurements at three locations in China from 2005 to 2018. The results showed that the RTM-RF is superior in terms of computational efficiency and performance, with a mean absolute errors (MAE) and coefficients of determination (R2) of 15.57 W m−2 and 0.98, respectively. Under clear sky conditions, aerosol optical depth (AOD) contributed the most to the accuracy of GSR estimates, with an average contribution of 57.95%. The measurement uncertainty due to the asymmetry factor, AOD, single-scattering albedo, and land surface albedo (LSA) can explain the differences in GSR between RTM estimates and GSR observations at the Lulin (20.33 vs. 20.91 W m−2), Wuhan (−1.40 vs. 14.58 W m−2), and Xianghe (7.28 vs. 14.32 W m−2) sites. Our study supports the use of physical models combined with machine learning models to estimate GSR and provides valuable scientific information for large-area solar radiation estimations.

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  • Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:rensus:v:173:y:2023:i:c:s1364032122009868
    DOI: 10.1016/j.rser.2022.113105
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