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Machine Learning for AC Optimal Power Flow
Neel Guha · Zhecheng Wang

Fri Jun 14 04:30 PM -- 04:40 PM (PDT) @

We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.

Author Information

Neel Guha (Carnegie Mellon University)
Zhecheng Wang (Stanford University)

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