Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

TL;DR

This paper introduces an unsupervised method leveraging domain knowledge to disaggregate water heater power consumption from low-frequency energy data, effectively identifying CWHs and detecting misconfigurations in residential homes.

Contribution

The study presents a novel unsupervised approach that uses power spike shape and timing, along with domain knowledge, to reliably identify CWHs in low-resolution energy data.

Findings

Successfully identified CWHs in most cases with declared usage.

Detected misconfigured CWHs based on off-peak trigger patterns.

Demonstrated applicability on large-scale residential energy datasets.

Abstract

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.