Non-Revenue Water (NRW) remains one of the most pressing issues for water utilities. It leads to financial losses, resource waste, and increased operational costs. Reducing NRW requires more than just reactive leak repair, it demands real-time insight, predictive modelling, and data-driven decisions. This is where Digital Twin technology provides a clear advantage.

A Digital Twin is a dynamic, data-integrated replica of a water network. It connects physical assets with digital systems through real-time data feeds, simulation tools, and analytics engines. When applied to NRW management, a Digital Twin allows utilities to detect leaks faster, manage pressure more precisely, and predict issues before they occur.

This article explains the core technical components of a Digital Twin and how each one contributes to measurable NRW reduction.

Aqua Analytics is a proven leader in Digital Twin development for water utilities across Australia. Our team brings together technical expertise and field experience to deliver platforms that reduce Non-Revenue Water, improve efficiency, and support smarter decision-making. If you are planning to implement a Digital Twin, we can help you build a system that delivers measurable results.

Table of Contents

Digital Twin Architecture

A Digital Twin relies on a strong data architecture to provide accurate and timely insights into water network performance. It brings together multiple data sources, processes that data in real time or in batches, and delivers actionable outputs through an integrated user interface.

Key Data Sources

The effectiveness of a Digital Twin begins with data. Common inputs include:

  • SCADA systems for real-time operational data
  • GIS for asset location and network layout
  • Flow and pressure sensors for hydraulic behaviour
  • Hydraulic models to simulate system performance
  • Customer meters for demand monitoring and consumption patterns

These sources create a connected view of the network, allowing utilities to link physical behaviour with digital outputs.

Data Integration and Processing

Data from each source is processed using Extract, Transform, Load (ETL) pipelines. These pipelines clean and organise the data so that it can be analysed effectively. APIs enable integration between systems, while data lakes or cloud platforms store the information securely.

Processing may happen in real time or in scheduled batches, depending on the source. For NRW applications, real-time processing supports faster leak detection and response, while batch processing is useful for long-term trend analysis and reporting.

Core Components of the Digital Twin Platform

  • 3D Visualisation: Provides an interactive view of network infrastructure, highlighting pressure zones, sensor locations, and detected anomalies
  • Analytics Engine: Processes large volumes of data to detect leaks, simulate outcomes, and identify inefficiencies
  • Rule Engines and Alerts: Trigger warnings based on pre-set thresholds for pressure, flow variation, or suspected leakage
  • User Interface: Presents data clearly through dashboards, maps, and reports, enabling decision-makers to act quickly

This architecture creates the foundation for real-time leak detection monitoring and predictive control, including the use of AquaNRW. For NRW reduction, it enables teams to locate hidden losses, assess pressure zones, and deploy resources based on reliable data rather than estimates.

Modelling and Simulation for NRW Reduction

Digital Twins use modelling and simulation to improve visibility and control across a water network. This capability is critical for identifying pressure-related issues, forecasting demand, and predicting potential leaks before they result in significant losses.

Hydraulic Modelling for Pressure and Leakage Management

Hydraulic models simulate the movement of water through pipes, valves, and reservoirs under varying conditions. By linking these models with real-time sensor data, utilities can:

  • Identify areas of overpressure that increase the risk of pipe bursts
  • Simulate leakage under different operating scenarios
  • Optimise pressure zones to balance service delivery with asset protection
  • Forecast demand patterns and adjust operations to reduce stress on the system

These insights help operators move from reactive to proactive network management, improving overall system efficiency.

Machine Learning and AI Applications

Digital Twins go further with the integration of machine learning and artificial intelligence. These technologies analyse historical and real-time data to detect patterns and predict issues before they escalate.

Examples include:

  • Anomaly Detection: Identifies unusual changes in flow or pressure that suggest a leak or unauthorised consumption
  • Predictive Maintenance: Flags assets that are likely to fail, based on condition trends and historical failures
  • Leak Localisation: Narrows down likely leak locations using pressure transient analysis and pattern recognition
  • Demand Forecasting: Anticipates short-term and long-term consumption trends to reduce unnecessary strain on the network

By combining these models with live data, Digital Twins support faster and more accurate decisions that directly reduce Non-Revenue Water.

Sensor Technologies and Data Acquisition

Accurate data is essential for a Digital Twin to deliver meaningful insights. This starts with reliable sensors that collect flow, pressure, and acoustic data across the network. The right sensor strategy improves visibility, supports faster leak detection, and enables better pressure control.

Types of Sensors for NRW Monitoring

To minimise Non-Revenue Water, utilities deploy a range of sensor types across the network:

  • Flow meters: Measure water volume at key points in the system
  • Pressure transducers: Monitor pressure fluctuations that may indicate leaks or bursts
  • Acoustic sensors: Detect the sound of escaping water in buried pipes

Each sensor type provides specific data that contributes to leak identification, pressure optimisation, and condition monitoring.

Communication Technologies

For real-time data acquisition, sensors need reliable and low-power communication networks. Many utilities now use cellular technologies such as Narrowband IoT (NB-IoT), which offers strong underground penetration, long battery life, and low data transmission costs.

These communication networks ensure that high-frequency sensor data reaches the Digital Twin platform without delay, supporting continuous monitoring and fast response times.

Strategic Sensor Placement

Sensor performance depends not just on technology, but also on location. Placement strategies aim to cover critical points in the network, such as district metered areas (DMAs), pressure zones, and known high-risk areas. Increasing sensor density improves spatial accuracy, helping to pinpoint leaks and pressure issues faster.

A well-planned sensor network strengthens the Digital Twin’s ability to detect, localise, and reduce Non-Revenue Water with precision.

Integration with Existing Systems

To reduce Non-Revenue Water effectively, a Digital Twin must work seamlessly with existing operational platforms. Integration ensures that data is not siloed, actions are automated, and teams can act on insights without delay.

Core Systems to Integrate

A well-functioning Digital Twin connects to the following systems:

  • SCADA (Supervisory Control and Data Acquisition): Provides real-time operational data such as flow, pressure, and tank levels
  • GIS (Geographic Information System): Supplies asset location and network topology
  • Work Order Management Systems (WOMS): Links data-driven insights to maintenance actions and field team scheduling
  • Customer Information Systems (CIS): Adds consumption patterns and complaint data to the overall network view

Integrating these systems allows the Digital Twin to generate a complete operational picture and link digital insights directly to physical actions.

Benefits of Integration

  • 1. Closed-loop monitoring: Alarms or thresholds in the Digital Twin trigger automated workflows in SCADA or WOMS systems
  • 2. Faster maintenance response: Leak alerts generate work orders directly, reducing downtime and water loss
  • 3. Improved coordination: Field teams, operators, and planners work from a unified data source
  • 4. Data accuracy and consistency: Synchronised systems reduce errors and ensure reliable performance tracking

System integration is essential to making Digital Twin insights actionable. It closes the gap between detection and resolution, helping utilities reduce water loss with less manual effort.

Performance Metrics and KPIs

To evaluate how well a Digital Twin reduces Non-Revenue Water, utilities must track clear performance metrics. These indicators show where improvements are happening and help justify continued investment in technology, staffing, and infrastructure upgrades.

Key Metrics to Track

The following KPIs are commonly used to assess the impact of a Digital Twin on NRW:

  • Percentage reduction in NRW: Measures overall efficiency improvement
  • Litres per connection per day (L/conn/day): Tracks daily water loss per customer
  • Kilolitres per kilometre per day (kL/km/day): Monitors loss across the pipe network
  • Number of leaks repaired: Indicates operational responsiveness and detection accuracy
  • Water loss volume reduction (ML/year): Measures total recovered supply
  • Average response time: Tracks the time from leak detection to repair
  • Operational cost savings: Assesses the financial return from reduced water loss and optimised workflows
  • Carbon footprint reduction: Reflects lower pumping and treatment energy due to less lost water

These metrics can be calculated for the entire network or focused on specific regions, such as DMAs. Localised tracking allows for targeted interventions and a clearer view of performance over time.

By using these KPIs, utilities can measure the direct benefits of Digital Twin technology in reducing water loss, improving service delivery, and supporting sustainability targets.

Tired of guessing where your water losses are happening?

Curious how Digital Twins perform in real-world water networks?

We’ve delivered successful outcomes for councils and utilities across Australia, reducing NRW, improving visibility, and driving cost savings. 

Contact us for a free demo and to explore how using our AquaNRW digital twin solution can assist with reducing water loss in your supply system.