Prediction of Solar Flux Density Distribution Concentrated by a Heliostat Using a Ray Tracing-Assisted Generative Adversarial Neural Network

Fen Xu (), Yanpeng Sun and Minghuan Guo
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Fen Xu: School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China
Yanpeng Sun: School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China
Minghuan Guo: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2025, vol. 18, issue 6, 1-17

Abstract: Predicting the solar flux density distribution formed by heliostats in a concentrated solar tower power (CSP) plant is important for the optimization and stable operation of a CSP plant. However, the high temperature and blackbody attribute of the receiver makes direct measurement of the concentrated solar irradiance distribution a difficult task. To address this issue, indirect methods have been proposed. Nevertheless, these methods are either costly or not accurate enough. This study proposes a ray tracing-assisted deep learning method for the prediction of the concentrated solar flux density distribution formed by a heliostat. Namely, a generative adversarial neural network (GAN) model using Monte Carlo ray tracing results as the input was built for the prediction of solar flux density distribution concentrated by a heliostat. Experiments showed that the predicted solar flux density distributions were highly consistent with the concentrated solar spots on the Lambertian target formed by the same heliostat. This ray tracing-assisted deep learning method can be extended to other heliostats in the CSP plant and pave the way for the prediction of the solar flux density distribution concentrated by the whole heliostat field in a CSP plant.

Keywords: ray tracing; generative adversarial network; heliostat; flux density distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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