  The implementation of efficient demand response (DR) programs for household
electricity consumption would benefit from data-driven methods capable of
simulating the impact of different tariffs schemes. This paper proposes a novel
method based on conditional variational autoencoders (CVAE) to generate, from
an electricity tariff profile combined with exogenous weather and calendar
variables, daily consumption profiles of consumers segmented in different
clusters. First, a large set of consumers is gathered into clusters according
to their consumption behavior and price-responsiveness. The clustering method
is based on a causality model that measures the effect of a specific tariff on
the consumption level. Then, daily electrical energy consumption profiles are
generated for each cluster with CVAE. This non-parametric approach is compared
to a semi-parametric data generator based on generalized additive models and
that uses prior knowledge of energy consumption. Experiments in a publicly
available data set show that, the proposed method presents comparable
performance to the semi-parametric one when it comes to generating the average
value of the original data. The main contribution from this new method is the
capacity to reproduce rebound and side effects in the generated consumption
profiles. Indeed, the application of a special electricity tariff over a time
window may also affect consumption outside this time window. Another
contribution is that the clustering approach segments consumers according to
their daily consumption profile and elasticity to tariff changes. These two
results combined are very relevant for an ex-ante testing of future DR policies
by system operators, retailers and energy regulators.
