Active Bucketized Learning for ACOPF Optimization Proxies. (arXiv:2208.07497v1 [cs.LG])

This paper considers optimization proxies for Optimal Power Flow (OPF), i.e.,
machine-learning models that approximate the input/output relationship of OPF.
Recent work has focused on showing that such proxies can be of high fidelity.
However, their training requires significant data, each instance necessitating
the (offline) solving of an OPF for a sample of the input distribution. To meet
the requirements of market-clearing applications, this paper proposes Active
Bucketized Sampling (ABS), a novel active learning framework that aims at
training the …

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