  Probabilistic wind power forecasting approaches have significantly advanced
in recent decades. However, forecasters often assume data completeness and
overlook the challenge of missing values resulting from sensor failures,
network congestion, etc. Traditionally, this issue is addressed during the data
preprocessing procedure using methods such as deletion and imputation.
Nevertheless, these ad-hoc methods pose challenges to probabilistic wind power
forecasting at both parameter estimation and operational forecasting stages. In
this paper, we propose a resilient probabilistic forecasting approach that
smoothly adapts to missingness patterns without requiring preprocessing or
retraining. Specifically, we design an adaptive quantile regression model with
parameters capable of adapting to missing patterns, comprising two modules. The
first is a feature extraction module where weights are kept static and biases
are designed as a function of missingness patterns. The second is a
non-crossing quantile neural network module, ensuring monotonicity of
quantiles, with higher quantiles derived by adding non-negative amounts to
lower quantiles. The proposed approach is applicable to cases under all
missingness mechanisms including missing-not-at-random cases. Case studies
demonstrate that our proposed approach achieves state-of-the-art results in
terms of the continuous ranked probability score, with acceptable computational
cost.
