Technology
Digital twins for utilities and public infrastructure
What a digital twin is in practical terms, and where the concept delivers value for water, power, and facilities.
A digital twin is a data model of a physical system, such as a water network, an electrical feeder, or a building, that is kept current with live data and can be used to simulate what would happen under different conditions. Stripped of marketing, it is the combination of an accurate asset model, real-time data, and a simulation or analytics engine.
What it requires
An accurate asset model. GIS and asset management data that reflects what is actually in the ground, with connectivity. Most utilities find this is the largest gap.
Live data. Sensor, meter, and control system data flowing into the model.
Simulation or analytics. Hydraulic models, load flow models, energy models, or machine learning that use the model and data to predict behavior.
Interfaces. Views for operators, planners, and engineers, integrated with work management.
Practical uses
- Predicting the effect of a valve closure or main break on customers before acting
- Locating leaks by comparing modeled and observed pressures
- Planning capital projects with simulated demand growth
- Optimizing pump schedules for energy cost
- Fault detection in building systems
- Training operators on scenarios
A realistic path
Start with the asset data. Reconcile GIS with field reality, capture connectivity, and establish maintenance workflows. Add live data from existing sensors. Apply an existing engineering model before buying a platform. Expand the twin where the analysis has shown value.
Cautions
Vendor platforms promise a lot. Without accurate asset data and reliable sensor feeds, the twin reflects nothing. Cost and data ownership terms deserve scrutiny.
Procurement
Separate the data foundation work from any platform purchase. Require open data formats and export. Define the decisions the twin will support and evaluate against them.
Where HQT fits
Data integration between GIS, asset management, control systems, and sensor platforms; data quality tooling; analytics; and interfaces for operations and planning staff.