Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches

Carboni, Davide, Gluhak, Alex, McCann, Julie and Beach, Thomas ORCID: https://orcid.org/0000-0001-5610-8027 2016. Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches. Sensors 16 (5) , 738. 10.3390/s16050738

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TC Hydraulic engineering. Ocean engineering
Publisher:	MDPI Publishing
ISSN:	1424-8220
Funders:	EU
Date of First Compliant Deposit:	8 June 2016
Date of Acceptance:	16 May 2016
Last Modified:	04 May 2023 23:31
URI:	https://orca.cardiff.ac.uk/id/eprint/91513

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