Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



+44 (0)20 7594 8375j.mccann Website




Miss Teresa Ng +44 (0)20 7594 8300




260ACE ExtensionSouth Kensington Campus






BibTex format

author = {Carboni, D and Gluhak, A and McCann, JA and Beach, TH},
doi = {10.3390/s16050738},
journal = {Sensors},
title = {Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches},
url = {},
volume = {16},
year = {2016}

RIS format (EndNote, RefMan)

AB - Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included.
AU - Carboni,D
AU - Gluhak,A
AU - McCann,JA
AU - Beach,TH
DO - 10.3390/s16050738
PY - 2016///
SN - 1424-8239
TI - Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches
T2 - Sensors
UR -
UR -
VL - 16
ER -