Title: Revisiting Deep AC-OPF Show Chinese title

Title: 重新审视深度AC-OPF

Abstract: Recent work has proposed machine learning (ML) approaches as fast surrogates for solving AC optimal power flow (AC-OPF), with claims of significant speed-ups and high accuracy. In this paper, we revisit these claims through a systematic evaluation of ML models against a set of simple yet carefully designed linear baselines. We introduce OPFormer-V, a transformer-based model for predicting bus voltages, and compare it to both the state-of-the-art DeepOPF-V model and simple linear methods. Our findings reveal that, while OPFormer-V improves over DeepOPF-V, the relative gains of the ML approaches considered are less pronounced than expected. Simple linear baselines can achieve comparable performance. These results highlight the importance of including strong linear baselines in future evaluations. Show Chinese abstract

Abstract: 最近的工作提出了机器学习（ML）方法作为解决交流最优功率流（AC-OPF）的快速替代方案，声称能够显著提高速度并保持高精度。 在本文中，我们通过将ML模型与一组简单但精心设计的线性基线进行系统评估，重新审视了这些声明。 我们引入了OPFormer-V，一个基于Transformer的模型，用于预测节点电压，并将其与最先进的DeepOPF-V模型以及简单的线性方法进行比较。 我们的研究结果表明，尽管OPFormer-V优于DeepOPF-V，但所考虑的ML方法的相对增益不如预期明显。 简单的线性基线可以实现相当的性能。 这些结果突显了在未来评估中包含强大线性基线的重要性。