  The field of electricity price forecasting has seen significant advances in
the last years, including the development of new, more accurate forecast
models. These models leverage statistical relationships in previously observed
data to predict the future; however, there is a lack of analysis explaining
these models, which limits their real world applicability in critical
infrastructure. In this paper, using data from the Belgian electricity markets,
we explore a state-of-the-art forecasting model to understand if its
predictions can be trusted in more general settings than the limited context it
is trained in. If the model produces poor predictions in extreme conditions or
if its predictions are inconsistent with reality, it cannot be relied upon in
real-world where these forecasts are used in downstream decision-making
activities. Our results show that, despite being largely accurate enough in
general, even state of the art forecasts struggle with remaining consistent
with reality.
