  Traditional smart meter measurements lack the granularity needed for
real-time decision-making. To address this practical problem, we create a
generative adversarial networks (GAN) model that enforces temporal consistency
on its high-resolution outputs via hard inequality constraints using a convex
optimization layer. A unique feature of our GAN model is that it is trained
solely on slow timescale aggregated power information obtained from historical
smart meter data. The results demonstrate that the model can successfully
create minutely interval temporally-correlated instantaneous power injection
profiles from 15-minute average power consumption information. This innovative
approach, emphasizing inter-neuron constraints, offers a promising avenue for
improved high-speed state estimation in distribution systems and enhances the
applicability of data-driven solutions for monitoring such systems.
