  The optimal power flow is an optimization problem used in power systems
operational planning to maximize economic efficiency while satisfying demand
and maintaining safety margins. Due to uncertainty and variability in renewable
energy generation and demand, the optimal solution needs to be updated in
response to observed uncertainty realizations or near real-time forecast
updates. To address the challenge of computing such frequent real-time updates
to the optimal solution, recent literature has proposed the use of machine
learning to learn the mapping between the uncertainty realization and the
optimal solution. Further, learning the active set of constraints at
optimality, as opposed to directly learning the optimal solution, has been
shown to significantly simplify the machine learning task, and the learnt model
can be used to predict optimal solutions in real-time. In this paper, we
propose the use of classification algorithms to learn the mapping between the
uncertainty realization and the active set of constraints at optimality, thus
further enhancing the computational efficiency of the real-time prediction. We
employ neural net classifiers for this task and demonstrate the excellent
performance of this approach on a number of systems in the IEEE PES PGLib-OPF
benchmark library.
