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.

通过使用合成数据，本文介绍了基于 Variational Auto-encoder (VAE) 模型的 Faraday，该模型通过训练英国一个能源供应商的 3 亿次智能电表数据读数，并结合属性和低碳技术的信息，产生与实际数据相当的家庭负荷曲线，为未来能源网络的网格建模人员提供了实际应用的参考。