  In order to enable the transition towards renewable energy sources,
probabilistic energy forecasting is of critical importance for incorporating
volatile power sources such as solar energy into the electrical grid. Solar
energy forecasting methods often aim to provide probabilistic predictions of
solar irradiance. In particular, many hybrid approaches combine physical
information from numerical weather prediction models with statistical methods.
Even though the physical models can provide useful information at intra-day and
day-ahead forecast horizons, ensemble weather forecasts from multiple model
runs are often not calibrated and show systematic biases. We propose a
post-processing model for ensemble weather predictions of solar irradiance at
temporal resolutions between 30 minutes and 6 hours. The proposed models
provide probabilistic forecasts in the form of a censored logistic probability
distribution for lead times up to 5 days and are evaluated in two case studies
covering distinct physical models, geographical regions, temporal resolutions,
and types of solar irradiance. We find that post-processing consistently and
significantly improves the forecast performance of the ensemble predictions for
lead times up to at least 48 hours and is well able to correct the systematic
lack of calibration.
