  Capturing the uncertainty in probabilistic wind power forecasts is
challenging, especially when uncertain input variables, such as the weather,
play a role. Since ensemble weather predictions aim to capture the uncertainty
in the weather system, they can be used to propagate this uncertainty through
to subsequent wind power forecasting models. However, as weather ensemble
systems are known to be biased and underdispersed, meteorologists post-process
the ensembles. This post-processing can successfully correct the biases in the
weather variables but has not been evaluated thoroughly in the context of
subsequent forecasts, such as wind power generation forecasts. The present
paper evaluates multiple strategies for applying ensemble post-processing to
probabilistic wind power forecasts. We use Ensemble Model Output Statistics
(EMOS) as the post-processing method and evaluate four possible strategies:
only using the raw ensembles without post-processing, a one-step strategy where
only the weather ensembles are post-processed, a one-step strategy where we
only post-process the power ensembles, and a two-step strategy where we
post-process both the weather and power ensembles. Results show that
post-processing the final wind power ensemble improves forecast performance
regarding both calibration and sharpness, whilst only post-processing the
weather ensembles does not necessarily lead to increased forecast performance.
