  Generating power system states that have similar distribution and dependency
to the historical ones is essential for the tasks of system planning and
security assessment, especially when the historical data is insufficient. In
this paper, we described a generative model for load profiles of industrial and
commercial customers, based on the conditional variational autoencoder (CVAE)
neural network architecture, which is challenging due to the highly variable
nature of such profiles. Generated contextual load profiles were conditioned on
the month of the year and typical power exchange with the grid. Moreover, the
quality of generations was both visually and statistically evaluated. The
experimental results demonstrate our proposed CVAE model can capture temporal
features of historical load profiles and generate `realistic' data with
satisfying univariate distributions and multivariate dependencies.
