Advanced household profiling using digital water meters

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.