Electrical solar power generation has a very decentral character which is posing significant challenges to monitoring and assessment due to the high number of considered systems. At the same time, with the advent of smart metering, huge amounts of data are becoming available and demand for automated approaches that are robust enough to deal with measuring gaps and noise. We propose and introduce a combined method consisting of a fitting algorithm, which works with the high temporal resolution of the smart meter data used (15 s), and an infrared thermography measurement. The results are region-specific irradiance characteristics, precise description of individual skylines for single systems derived from calculations, indication of abnormalities that should be compared with the abnormalities identified in thermal images and identification of reasons for underperformance.