对比深度学习与线性模型，发现简单线性方法在电力系统优化中表现不逊色。
Revisiting Deep AC-OPF
- 用Transformer模型预测节点电压，对比现有深度学习方法
- 线性基线模型性能接近甚至超过复杂深度学习模型
- 强调未来评估需纳入强线性基准，避免高估算法效果
近期研究提出机器学习方法作为求解交流最优潮流（AC-OPF）的快速代理模型，宣称具有显著加速和高精度。本文通过系统评估机器学习模型与一组精心设计的简单线性基线模型，重新审视这些宣称。我们引入基于Transformer的OPFormer-V模型用于预测节点电压，并与当前最先进的DeepOPF-V模型及线性方法进行比较。结果表明，尽管OPFormer-V优于DeepOPF-V，但所考虑的机器学习方法的相对提升远低于预期；简单线性基线可达到相当的性能。这一发现凸显了在未来评估中纳入强线性基线的重要性。
原文摘要 · Abstract (English)
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.
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