  In recent years, probabilistic forecasts techniques were proposed in research
as well as in applications to integrate volatile renewable energy resources
into the electrical grid. These techniques allow decision makers to take the
uncertainty of the prediction into account and, therefore, to devise optimal
decisions, e.g., related to costs and risks in the electrical grid. However, it
was yet not studied how the input, such as numerical weather predictions,
affects the model output of forecasting models in detail. Therefore, we examine
the potential influences with techniques from the field of sensitivity analysis
on three different black-box models to obtain insights into differences and
similarities of these probabilistic models. The analysis shows a considerable
number of potential influences in those models depending on, e.g., the
predicted probability and the type of model. These effects motivate the need to
take various influences into account when models are tested, analyzed, or
compared. Nevertheless, results of the sensitivity analysis will allow us to
select a model with advantages in the practical application.
