Forecasting day-ahead 1-minute irradiance variability from numerical weather predictions

Accurate forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore reliable forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly, missing crucial information on meteorological variability such as clouds. In this work we present a methodology to forecast minute-to-minute irradiance variability in terms of its probability density function (PDF) based on hourly NWP results, by applying statistical postprocessing using machine learning. The algorithm is tested using the 2.5 × 2.5 km2 HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 meteorological stations throughout the Netherlands used as a ground truth. The applicability of the algorithm to 31 × 31 km2 global-scale models is investigated using ERA5 reanalysis data, which yields comparable accuracies. We find that almost half of the inaccuracy of the postprocessed result is due to errors in the radiation forecast of the NWP model used as input. Finally, the proposed post-processing algorithm is compared to the next generation weather models based on high resolution Large Eddy Simulation (LES), at 75 m horizontal grid spacing, on a case study spanning four days. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of cloud and irradiance dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.