Deep learning architectures for inference of AC-OPF solutions

• URL: http://arxiv.org/abs/2011.03352v2
• Date: Tue, 1 Dec 2020 10:03:18 GMT
• Title: Deep learning architectures for inference of AC-OPF solutions
• Authors: Thomas Falconer and Letif Mones
• Abstract summary: We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. We demonstrate the efficacy of leveraging network topology in the models by constructing abstract representations of electrical grids in the graph domain.
• Score: 0.4061135251278187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 of constraints) settings. Computational gains for obtaining optimal solutions are also presented.

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