  Wind power forecasting is essential to power system operation and electricity
markets. As abundant data became available thanks to the deployment of
measurement infrastructures and the democratization of meteorological
modelling, extensive data-driven approaches have been developed within both
point and probabilistic forecasting frameworks. These models usually assume
that the dataset at hand is complete and overlook missing value issues that
often occur in practice. In contrast to that common approach, we rigorously
consider here the wind power forecasting problem in the presence of missing
values, by jointly accommodating imputation and forecasting tasks. Our approach
allows inferring the joint distribution of input features and target variables
at the model estimation stage based on incomplete observations only. We place
emphasis on a fully conditional specification method owing to its desirable
properties, e.g., being assumption-free when it comes to these joint
distributions. Then, at the operational forecasting stage, with available
features at hand, one can issue forecasts by implicitly imputing all missing
entries. The approach is applicable to both point and probabilistic
forecasting, while yielding competitive forecast quality within both simulation
and real-world case studies. It confirms that by using a powerful universal
imputation method like fully conditional specification, the proposed approach
is superior to the common approach, especially in the context of probabilistic
forecasting.
