Next Article in Journal
An Approach to the Prototyping of an Optimized Limited Stroke Actuator to Drive a Low Pressure Exhaust Gas Recirculation Valve
Previous Article in Journal
Design and Implementation of a Novel Compatible Encoding Scheme in the Time Domain for Image Sensor Communication
Article Menu

Export Article

Open AccessReview
Sensors 2016, 16(5), 738; doi:10.3390/s16050738

Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches

1
ICRI Sustainable Connected Cities, Intel Corp., London SW7 2AZ, UK
2
Digital Catapult, London NW1 2RA, UK
3
Department of Computing, Imperial College London, London SW7 2AZ, UK
4
School of Engineering, Cardiff University, Cardiff CF243AA, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 16 March 2016 / Revised: 14 May 2016 / Accepted: 16 May 2016 / Published: 20 May 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1297 KB, uploaded 20 May 2016]   |  

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. View Full-Text
Keywords: water usage disaggregation; water monitoring; disaggregation algorithms; machine learning; water management water usage disaggregation; water monitoring; disaggregation algorithms; machine learning; water management
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Carboni, D.; Gluhak, A.; McCann, J.A.; Beach, T.H. Contextualising Water Use in Residential Settings: A Survey of Non-Intrusive Techniques and Approaches. Sensors 2016, 16, 738.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top