The PAINT database for operational concentrating solar power plant data following FAIR data principles

Abstract

Integrating renewable energy is critical for maintaining grid stability while striving towards global climate goals. Concentrating solar power tower plants offer a promising solution but their competitiveness is currently hindered by high operational costs, limited data availability and slow adoption of emerging technologies. To address these barriers, here we introduce PAINT, a FAIR (findable, accessible, interoperable and reusable) open-access database for operational solar tower plant data. PAINT provides 849 GB of high-resolution data collected over multiple years from the Jülich solar tower plant, including heliostat properties, calibration and deflectometry measurements and fine-grained weather data. The database is organized using the SpatioTemporal Asset Catalog metadata specification and supports the development of digital twins, artificial intelligence-based calibration methods, predictive maintenance and improved solar flux prediction. We also introduce standardized benchmarks to promote reproducibility and fair comparisons. Providing access to high-quality data, PAINT enables broader participation in solar research, accelerates innovation and facilitates data-driven solutions in solar tower power plant research. The limited availability of operational data slows the development of concentrating solar power. To address this issue, Phipps et al. developed PAINT, a FAIR, open-access database that compiles multi-year data from a plant in Jülich, Germany.