  The output of solar power generation is significantly dependent on the
available solar radiation. Thus, with the proliferation of PV generation in the
modern power grid, forecasting of solar irradiance is vital for proper
operation of the grid. To achieve an improved accuracy in prediction
performance, this paper discusses a Bayesian treatment of probabilistic
forecasting. The approach is demonstrated using publicly available data
obtained from the Florida Automated Weather Network (FAWN). The algorithm is
developed in Python and the results are compared with point forecasts, other
probabilistic methods and actual field results obtained for the period.
