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Traditional smart meters, which measure energy usage every 15 minutes or moreand report it at least a few hours later, lack the granularity needed forreal-time decision-making. To address this practical problem, we introduce anew method using generative adversarial networks (GAN) that enforces temporalconsistency on its high-resolution outputs via hard inequality constraintsusing convex optimization. A unique feature of our GAN model is that it istrained solely on slow timescale aggregated historical energy data obtainedfrom smart meters. The results demonstrate that the model can successfullycreate minute-by-minute temporally correlated profiles of power usage from15-minute interval average power consumption information. This innovativeapproach, emphasizing inter-neuron constraints, offers a promising avenue forimproved high-speed state estimation in distribution systems and enhances theapplicability of data-driven solutions for monitoring and subsequentlycontrolling such systems.

Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks