# Revisiting Deep AC-OPF

## TL;DR

This paper critically evaluates machine learning models for AC optimal power flow, showing that simple linear methods can perform comparably to complex ML models, emphasizing the need for strong baselines in future research.

## Contribution

Introduces OPFormer-V, a transformer-based model for predicting bus voltages, and systematically compares it with existing ML and linear baselines.

## Key findings

- ML models show limited advantage over linear baselines
- OPFormer-V outperforms DeepOPF-V but with modest gains
- Linear methods achieve comparable accuracy to complex ML models

## 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.