  A large amount of data has been generated by grid operators solving AC
optimal power flow (ACOPF) throughout the years, and we explore how leveraging
this data can be used to help solve future ACOPF problems. We use this data to
train a Random Forest to predict solutions of future ACOPF problems. To
preserve correlations and relationships between predicted variables, we utilize
a multi-target approach to learn approximate voltage and generation solutions
to ACOPF problems directly by only using network loads, without the knowledge
of other network parameters or the system topology. We explore the benefits of
using the learned solution as an intelligent warm start point for solving the
ACOPF, and the proposed framework is evaluated numerically using multiple IEEE
test networks. The benefit of using learned ACOPF solutions is shown to be
solver and network dependent, but shows promise for quickly finding approximate
solutions to the ACOPF problem.
