  In this article we present an unsupervised low-frequency method aimed at
detecting and disaggregating the power used by Cumulative Water Heaters (CWH)
in residential homes. Our model circumvents the inherent difficulty of
unsupervised signal disaggregation by using both the shape of a power spike and
its time of occurrence to identify the contribution of CWH reliably. Indeed,
many CHWs in France are configured to turn on automatically during off-peak
hours only, and we are able to use this domain knowledge to aid peak
identification despite the low sampling frequency. In order to test our model,
we equipped a home with sensors to record the ground-truth consumption of a
water heater. We then apply the model to a larger dataset of energy consumption
of Hello Watt users consisting of one month of consumption data for 5k homes at
30-minute resolution. In this dataset we successfully identified CWHs in the
majority of cases where consumers declared using them. The remaining part is
likely due to possible misconfiguration of CWHs, since triggering them during
off-peak hours requires specific wiring in the electrical panel of the house.
Our model, despite its simplicity, offers promising applications: detection of
mis-configured CWHs on off-peak contracts and slow performance degradation.
