A novel method for modeling renewable power production using ERA5: Spanish solar PV energy

In this study, a novel methodology for the estimation of solar PV resources at different regional levels was assessed. The method consists of using a machine learning model over a virtual power plant at the center of the region, having as input ERA5-derived meteorological variables and real installed capacities and generation data. For solar PV capacity factors (CFs) estimates in Spain, the methodology showed a high level of accuracy, with MBE0.12 and R>0.87 for all the analyzed regions. As an application of the proposed methodology, an enhanced open access database of Spanish solar PV energy resources (SHIRENDA_PV) was built. This database consists of hourly values of solar PV CFs for the Spanish NUTS 3 regions covering the period of 1990–2020. The analysis of the solar PV energy resources from SHIRENDA_PV revealed a mean diurnal CF of 0.467 for the entire period and a notable spatial and interannual variability in the study region, especially during winter. These changes were found mainly driven by the interannual changes in the winter NAO phases and intensities, that can produce changes in CFs of up to +16%/-10% under extreme phases.

A novel method for modeling renewable power production using ERA5: Spanish solar PV energy Articles