  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.
