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Related Experiment Video

Updated: Nov 11, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Advanced household profiling using digital water meters.

Md Shamsur Rahim¹, Khoi Anh Nguyen², Rodney Anthony Stewart²

¹Australian Artificial Intelligence Institute, School of Computer Science, University of Technology Sydney, Sydney, Australia.

Journal of Environmental Management

|March 29, 2021

Summary

This summary is machine-generated.

Advanced digital water meter data analytics enables detailed household profiling to promote water conservation. This machine learning approach identifies unseen water usage patterns, supporting long-term conservation efforts.

Keywords:

Behaviour change Digital water meter Recommender system User profiling Water conservation Water consumption data

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Area of Science:

Environmental Science
Data Science
Water Resource Management

Background:

Digital water metering provides near real-time data for customer feedback and conservation.
Understanding household water consumption patterns is crucial for effective water demand management.
Existing customer profiling methods may not capture dynamic behavioral changes.

Purpose of the Study:

To develop and apply advanced machine learning techniques for in-depth household water customer profiling.
To analyze high-resolution, disaggregated water consumption data from residential digital meters.
To identify previously unseen and evolving water consumption behaviors for enhanced conservation strategies.

Main Methods:

Utilized machine learning-based customer profiling techniques on residential digital water meter data.
Analyzed disaggregated end-use water consumption data (e.g., showers, taps) at various time intervals (15, 30, 60 min).
Implemented a user profiling approach with learning functionality to adapt to changing customer behaviors.

Main Results:

Successfully created in-depth customer profiles by analyzing high-resolution water usage data.
Identified novel and previously unseen residential customer behaviors related to water consumption.
Demonstrated the capability of the profiling technique to ascertain and accommodate changing behaviors over time.

Conclusions:

Advanced customer profiling using digital water meter data analytics is effective for promoting water conservation.
The developed machine learning technique offers a powerful tool for identifying dynamic behavioral changes and supporting long-term conservation.
Findings have significant implications for water demand management, policy advising, and resource conservation efforts.