Heliostat Surface Prediction via Physics-Aware Deep Learning

Abstract

Accurate characterization of heliostat surface errors is essential for the efficiency of concentrating solar power (CSP) plants, yet direct measurement methods such as deflectometry remain costly and impractical at scale. This thesis investigates a physics-informed deep learning approach to reconstruct heliostat surfaces from flux density images alone—a fundamentally ill-posed problem in which many distinct surfaces can yield similar flux patterns. The proposed framework integrates simulated datasets, augmentation of real surface measurements, and a raytracing-based training loop, with additional regularization strategies to mitigate the ill-posed nature of the inverse problem. The best model achieved a median flux prediction accuracy of 84%, approaching the 92% of supervised benchmarks. For surface reconstruction, training on synthetic datasets with heliostat positions close to the receiver yielded the lowest median Mean Absolute Error (MAE) of 2.4×10−4, compared to 1.4×10−4 in the supervised case. While individual surface reconstructions remained limited, the model reproduced some mean structural patterns of the training set, indicating partial learning of underlying geometric behavior. These findings demonstrate both the potential and current limitations of deep learning for heliostat surface reconstruction. With further advances in regularization, dataset design, and real-world validation, the approach may provide a scalable tool for CSP field calibration and optimization in the future.