Continuous monitoring of solar energy from space is critical for its efficient exploitation and distribution. For this reason we developed neural-network- and function-based real-time models, which are capable of producing massive radiation outputs in high spectral, spatial and temporal resolution. The models' performance against ground-based measurements revealed a dependence on input quality and resolution, and an overall accuracy under cloudless and high solar energy potential conditions.