  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.
