  Massive informations about individual (household, small and medium
enterprise) consumption are now provided with new metering technologies and the
smart grid. Two major exploitations of these data are load profiling and
forecasting at different scales on the grid. Customer segmentation based on
load classification is a natural approach for these purposes. We propose here a
new methodology based on mixture of high-dimensional regression models. The
novelty of our approach is that we focus on uncovering classes or clusters
corresponding to different regression models. As a consequence, these classes
could then be exploited for profiling as well as forecasting in each class or
for bottom-up forecasts in a unified view. We consider a real dataset of Irish
individual consumers of 4,225 meters, each with 48 half-hourly meter reads per
day over 1 year: from 1st January 2010 up to 31st December 2010, to demonstrate
the feasibility of our approach.
