# Deep learning architectures for inference of AC-OPF solutions

@article{Falconer2020DeepLA, title={Deep learning architectures for inference of AC-OPF solutions}, author={Thomas Falconer and Letif Mones}, journal={ArXiv}, year={2020}, volume={abs/2011.03352} }

We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. Using fully connected NNs as a baseline we demonstrate the efficacy of leveraging network topology in the models by constructing abstract representations of electrical grids in the graph domain, for both convolutional and graph NNs. The performance of the NN architectures is compared for regression (predicting optimal generator set-points) and classification (predicting the active set…

## 2 Citations

### Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow

- EngineeringArXiv
- 2022

-- The optimal power flow (OPF) problem, as a critical component of power system operations, becomes increasingly difficult to solve due to the variability, intermittency, and unpredictability of…

### Leveraging power grid topology in machine learning assisted optimal power flow

- Computer ScienceArXiv
- 2021

This work introduces a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regres- sion (predicting optimal generator set-points) and classiﬁcation (p Predicting the active set of constraints).

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