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.

翻译：近年来，针对电力系统应用的数据驱动方法研究日益丰富。然而，对领域知识的考量不足可能导致这些方法的实用性面临较高风险。具体而言，忽视电网特有的时空模式（如负荷、发电和拓扑结构等），可能使模型针对新输入数据输出不可行、不可实现或完全无意义的预测。为解决这一问题，本文通过分析真实运行数据，深入探讨电力系统行为模式，包括时变拓扑、负荷与发电特性，以及个体负荷与发电之间的空间差异（如高峰时段特征、风格多样性）。基于这些观察，本文评估了现有部分机器学习工作中因在模型设计与训练过程中忽视电网特有模式而导致的泛化风险。