  A framework for the generation of synthetic time-series transmission-level
load data is presented. Conditional generative adversarial networks are used to
learn the patterns of a real dataset of hourly-sampled week-long load profiles
and generate unique synthetic profiles on demand, based on the season and type
of load required. Extensive testing of the generative model is performed to
verify that the synthetic data fully captures the characteristics of real loads
and that it can be used for downstream power system and/or machine learning
applications.
