  Deep learning approaches for the Alternating Current-Optimal Power Flow
(AC-OPF) problem are under active research in recent years. A common
shortcoming in this area of research is the lack of a dataset that includes
both a realistic power network topology and the corresponding realistic loads.
To address this issue, we construct an AC-OPF formulation-ready dataset called
TAS-97 that contains realistic network information and realistic bus loads from
Tasmania's electricity network. We found that the realistic loads in Tasmania
are correlated between buses and they show signs of an underlying multivariate
normal distribution. Feasibility-optimized end-to-end deep neural network
models are trained and tested on the constructed dataset. Trained on samples
with bus loads generated from a fitted multivariate normal distribution, our
learning-based AC-OPF solver achieves 0.13% cost optimality gap, 99.73%
feasibility rate, and 38.62 times of speedup on realistic testing samples when
compared to PYPOWER.
