  The existence of multiple load-solution mappings of non-convex AC-OPF
problems poses a fundamental challenge to deep neural network (DNN) schemes. As
the training dataset may contain a mixture of data points corresponding to
different load-solution mappings, the DNN can fail to learn a legitimate
mapping and generate inferior solutions. We propose DeepOPF-AL as an
augmented-learning approach to tackle this issue. The idea is to train a DNN to
learn a unique mapping from an augmented input, i.e., (load, initial point), to
the solution generated by an iterative OPF solver with the load and initial
point as intake. We then apply the learned augmented mapping to solve AC-OPF
problems much faster than conventional solvers. Simulation results over IEEE
test cases show that DeepOPF-AL achieves noticeably better optimality and
similar feasibility and speedup performance, as compared to a recent DNN
scheme, with the same DNN size yet elevated training complexity.
