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

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 black-body 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 simulation-assisted deep learning method for the prediction of concentrated solar flux density distribution formed by a heliostat. Namely, a generative adversarial neural network (GAN) model using Monte Carlo ray tracing result 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 either heliostat in the CSP plant and pave the way for the prediction of the solar flux density distribution concentrated by whole heliostat field in a CSP plant.