  Access to smart meter data is essential to rapid and successful transitions
to electrified grids, underpinned by flexibility delivered by low carbon
technologies, such as electric vehicles (EV) and heat pumps, and powered by
renewable energy. Yet little of this data is available for research and
modelling purposes due consumer privacy protections. Whilst many are calling
for raw datasets to be unlocked through regulatory changes, we believe this
approach will take too long. Synthetic data addresses these challenges directly
by overcoming privacy issues. In this paper, we present Faraday, a Variational
Auto-encoder (VAE)-based model trained over 300 million smart meter data
readings from an energy supplier in the UK, with information such as property
type and low carbon technologies (LCTs) ownership. The model produces
household-level synthetic load profiles conditioned on these labels, and we
compare its outputs against actual substation readings to show how the model
can be used for real-world applications by grid modellers interested in
modelling energy grids of the future.
