  The increasing importance of solar power for electricity generation leads to
an increasing demand for probabilistic forecasting of local and aggregated PV
yields. In this paper we use an indirect modeling approach for hourly medium to
long term local PV yields based on publicly available irradiation data. We
suggest a time series model for global horizontal irradiation for which it is
easy to generate an arbitrary number of scenarios and thus allows for
multivariate probabilistic forecasts for arbitrary time horizons. In contrast
to many simplified models that have been considered in the literature so far it
features several important stylized facts. Sharp time dependent lower and upper
bounds of global horizontal irradiations are estimated that improve the often
used physical bounds. The parameters of the beta distributed marginals of the
transformed data are allowed to be time dependent. A copula-based time series
model is introduced for the hourly and daily dependence structure based on a
simple graphical structure known from the theory of vine copulas. Non-Gaussian
copulas like Gumbel and BB1 copulas are used that allow for the important
feature of so-called tail dependence. Evaluation methods like the continuous
ranked probability score (CRPS), the energy score (ES) and the variogram score
(VS) are used to compare the power of the model for multivariate probabilistic
forecasting with other models used in the literature showing that our model
outperforms other models in many respects.
