# Active Bucketized Learning for ACOPF Optimization Proxies

@article{Klamkin2022ActiveBL, title={Active Bucketized Learning for ACOPF Optimization Proxies}, author={Michael Klamkin and Mathieu Tanneau and Terrence W.K. Mak and Pascal Van Hentenryck}, journal={ArXiv}, year={2022}, volume={abs/2208.07497} }

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 fo- cused on showing that such proxies can be of high ﬁdelity. However, their training requires signiﬁcant data, each in- stance 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…

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