The PAINT Paper

The PAINT database is described in detail in Nature Energy:

• K. Phipps, M. Kuhl, M. Weiel, M. Busch, J. Lewen, N. Blumenröhr, D. Maldonado Quinto, C. Debus, F. Göhring, O. Kaufhold, A. Streit, R. Pitz-Paal, M. Götz, M. Pargmann "The PAINT database for operational concentrating solar power plant data following FAIR data principles" Nature Energy (2026), doi: 10.1038/s41560-026-02070-1.

Additional papers based on PAINT

• M. Pargmann, J. Ebert, M. Götz, D.M. Quinto, R. Pitz-Paal, S. Kesselheim "Automatic Heliostat Learning for In-situ Concentrating Solar Power Plant Metrology with Differentiable Ray Tracing" Nature Communications 15(1) (2024): 6997, doi: 10.1038/s41467-024-51019-z.
• M. Kuhl, M. Pargmann, M. Cherti, J. Jitsev, D.M. Quinto, R. Pitz-Paal "Flux density distribution forecasting in concentrated solar tower plants: A data-driven approach" Solar Energy 282 (2024): 112894, doi: 10.1016/j.solener.2024.112894.
• M. Pargmann, D.M. Quinto, P. Schwarzbözl, R. Pitz-Paal "High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks." Solar Energy 218 (2021): 48-56, doi: 10.1016/j.solener.2021.01.046.