  Despite the increasing importance of forecasts of renewable energy, current
planning studies only address a general estimate of the forecast quality to be
expected and selected forecast horizons.
  However, these estimates allow only a limited and highly uncertain use in the
planning of electric power distribution. More reliable planning processes
require considerably more information about future forecast quality.
  In this article, we present an in-depth analysis and comparison of
influencing factors regarding uncertainty in wind and photovoltaic power
forecasts, based on four different machine learning (ML) models.
  In our analysis, we found substantial differences in uncertainty depending on
ML models, data coverage, and seasonal patterns that have to be considered in
future planning studies.
