  Recent years have seen a rich literature of data-driven approaches designed
for power grid applications. However, insufficient consideration of domain
knowledge can impose a high risk to the practicality of the methods.
Specifically, ignoring the grid-specific spatiotemporal patterns (in load,
generation, and topology, etc.) can lead to outputting infeasible,
unrealizable, or completely meaningless predictions on new inputs. To address
this concern, this paper investigates real-world operational data to provide
insights into power grid behavioral patterns, including the time-varying
topology, load, and generation, as well as the spatial differences (in peak
hours, diverse styles) between individual loads and generations. Then based on
these observations, we evaluate the generalization risks in some existing ML
works causedby ignoring these grid-specific patterns in model design and
training.
