# Load Encoding for Learning AC-OPF

arXiv: https://arxiv.org/abs/2101.03973 · HTML: https://mlsanity.com/paper/2101.03973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods](https://mlsanity.com/paper/1909.10461/) (2019)
- [The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms](https://mlsanity.com/paper/1908.02788/) (2019)
- [DeepOPF: Deep Neural Network for DC Optimal Power Flow](https://mlsanity.com/paper/1905.04479/) (2019)
- [NESTA, The NICTA Energy System Test Case Archive](https://mlsanity.com/paper/1411.0359/) (2014)

## Abstract (arXiv)

The AC Optimal Power Flow (AC-OPF) problem is a core building block in electrical transmission system. It seeks the most economical active and reactive generation dispatch to meet demands while satisfying transmission operational limits. It is often solved repeatedly, especially in regions with large penetration of wind farms to avoid violating operational and physical limits. Recent work has shown that deep learning techniques have huge potential in providing accurate approximations of AC-OPF solutions. However, deep learning approaches often suffer from scalability issues, especially when applied to real life power grids. This paper focuses on the scalability limitation and proposes a load compression embedding scheme to reduce training model sizes using a 3-step approach. The approach is evaluated experimentally on large-scale test cases from the PGLib, and produces an order of magnitude improvements in training convergence and prediction accuracy.