  In the residential sector, electric water heaters are appliances with a
relatively high power consumption and a significant thermal inertia, which is
particularly suitable for Demand Response schemes. The success of efficient DR
schemes via the control of water heaters presupposes an accurate estimate of
their power demand at each instant. Although the load of water heaters is
rarely directly measured, a large penetration of Smart Meters (SMs) in
distribution grids enables to indirectly infer this information on a large
scale via load disaggregation. For that purpose, a considerable number of
Non-Intrusive Load Monitoring (NILM) approaches are suggested in the
literature. However, they require data streams at a time resolution in the
range of one second or higher, which is not realistic for standard SMs. Hence,
this paper proposes an unsupervised approach to detect and disaggregate the
load profile of water heaters from standard SM data with a time resolution in
the minute range. Evaluated on multiple real loads with sub-metering, the
proposed approach achieves a Normalized Mean Absolute Error (NMAE) lower than
2% and a precision generally higher than 92% with time resolutions between 5
and 15 minutes.
