Towards a Neural Network Based Flux Density Prediction - Using Generative Models to Enhance CSP Raytracing

Abstract

Each solar tower power plant is designed for a pre-calculated optimal flux density distribution. Any deviation from this has a direct impact on the output power as well as the durability of the components. An accurate knowledge of the current and predicted flux density is therefore essential. Also, because this is one of the most important input variables for all subsequent power plant processes. But due to individual errors of each heliostat, this theoretical flux density is very difficult to obtain. This includes, that common methods for measuring the flux density are either inaccurate, complicated, or expensive. Although raytracers exists, which can predict a flux density by analytical calculations, such an approach does not reflect reality sufficiently. We present a novel AI based method to predict the flux density map, which is capable to include heliostat specific errors, without having to measure the heliostats surface. Furthermore, we compare the advantages and disadvantages of different network structures for this approach and show first results archiving at best a Peak Signal to Noise Ratio (PSNR) value of up to 27.8 using neural radiance fields (NeRFs) .