# Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow

arXiv: https://arxiv.org/abs/2408.05228 · HTML: https://mlsanity.com/paper/2408.05228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow](https://mlsanity.com/paper/1910.01213/) (2019)
- [DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow](https://mlsanity.com/paper/1910.14448/) (2019)
- [Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks](https://mlsanity.com/paper/2103.14779/) (2021)
- [OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets](https://mlsanity.com/paper/2111.01228/) (2021)
- [Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence](https://mlsanity.com/paper/1405.0766/) (2014)
- [Solutions of DC OPF are Never AC Feasible](https://mlsanity.com/paper/1912.00319/) (2019)
- [Mathematical Programming formulations for the Alternating Current Optimal Power Flow problem](https://mlsanity.com/paper/2007.05334/) (2020)
- [Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC Feasibility](https://mlsanity.com/paper/1902.04815/) (2019)
- [Data Based Linearization: Least-Squares Based Approximation](https://mlsanity.com/paper/2007.02494/) (2020)
- [An Exact Sequential Linear Programming Algorithm for the Optimal Power Flow Problem](https://mlsanity.com/paper/2107.05164/) (2021)
- [Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time](https://mlsanity.com/paper/2207.05216/) (2022)
- [Optimizing Parameters of the DC Power Flow](https://mlsanity.com/paper/2310.00447/) (2023)

## Built on by (most cited first, 1 of 1)

- [PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learning](https://mlsanity.com/paper/2603.16879/) (2026)

## Abstract (arXiv)

The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the linearized DC optimal power flow (DC-OPF) problem, which can be solved to global optimality, but whose optimal solution is always infeasible in the original AC-OPF problem. Recently, neural networks (NN) have been introduced for solving the AC-OPF problem at significantly faster computation times. However, these methods necessitate extensive datasets, are difficult to train, and are often viewed as black boxes, leading to resistance from operators who prefer more transparent and interpretable solutions. In this paper, we introduce a novel learning-based approach that merges simplicity and interpretability, providing a bridge between traditional approximation methods and black-box learning techniques. Our approach not only provides transparency for operators but also achieves competitive accuracy. Numerical results across various power networks demonstrate that our method provides accuracy comparable to, and often surpassing, that of neural networks, particularly when training datasets are limited.