  In this paper, we introduce mREAL-GAN, a generative adversarial network (GAN)
for the parallel generation of multiple residential electrical appliance load
(mREAL) profiles. mREAL-GAN is intended for use in community-scale low-voltage
network analysis, and represents a departure from previous methods for this
purpose, which break the generation of appliance load profiles into several
steps and largely model each appliance independently. Instead, mREAL-GAN models
appliance load profiles in an end-to-end manner, and generates multiple
appliance load profiles in parallel in a way that captures inter-dependencies.
We show that mREAL-GAN generates load profiles for individual appliance-types
with greater fidelity than a popular example of previous methods, and
demonstrate its ability to capture inter-dependencies between appliances.
