This document summarizes a presentation on hourly probabilistic solar power forecasting. It discusses: 1) The need for solar power forecasting to address the variability of solar generation. Various forecasting models and horizons are used. 2) The combined physical and statistical approach used to generate solar power forecasts, which includes solar plant modeling, numerical weather prediction, and statistical corrections. 3) The models evaluated include multiple linear regression, artificial neural networks, and support vector regression. Ensemble learning with random forests is used to combine the models' outputs. 4) Probabilistic forecasting methods evaluated include ensemble-based, analog ensemble, and persistence approaches. Different probability distributions are generated. 5)