  Advancing probabilistic solar forecasting methods is essential to supporting
the integration of solar energy into the electricity grid. In this work, we
develop a variety of state-of-the-art probabilistic models for forecasting
solar irradiance. We investigate the use of post-hoc calibration techniques for
ensuring well-calibrated probabilistic predictions. We train and evaluate the
models using public data from seven stations in the SURFRAD network, and
demonstrate that the best model, NGBoost, achieves higher performance at an
intra-hourly resolution than the best benchmark solar irradiance forecasting
model across all stations. Further, we show that NGBoost with CRUDE post-hoc
calibration achieves comparable performance to a numerical weather prediction
model on hourly-resolution forecasting.
