Forecast tomorrow's load before dispatch
Use weather-integrated prediction to understand tomorrow's demand curve, prepare for cold snaps, and avoid over-heating the network during mild periods.
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HeatOps
AI Operations for District Heating
Run district heating networks with AI-native demand forecasting, supply temperature orchestration, hydraulic balance analysis, and digital-twin visibility.
Key Capabilities
Core building blocks that define how this page delivers operational value.
- Demand forecasting
Forecast heating load using weather signals, building behavior, and historical consumption so operators can plan hours ahead instead of reacting after comfort drops.
- Supply temperature orchestration
Optimize supply and return temperature setpoints across the network based on predicted demand, thermal inertia, and operational limits.
- Hydraulic balance intelligence
Detect imbalances in pressure and flow, identify weak circuits, and recommend valve or pump adjustments before complaints escalate.
- Heat loss and anomaly detection
Continuously surface suspicious losses, bypass behavior, insulation degradation, and distribution anomalies from live network data.
- Comfort compliance tracking
Track whether delivery points are actually staying inside target temperature bands, not just whether the plant is hitting a nominal setpoint.
- Operator copilot
Give control-room and field teams one workspace to review forecasts, validate actions in the twin, and coordinate operational response.
Use Cases
Practical applications and proven success scenarios across industries.
Tune supply temperature by zone and weather
Move from fixed temperature curves to dynamic setpoint control that adapts by zone, operating condition, and expected thermal demand.
Diagnose imbalance and distribution losses
Identify under-performing circuits, pressure imbalance, and abnormal loss patterns across the network before they show up as service complaints.
Coordinate maintenance from a twin view
Tie network anomalies to assets, locations, and maintenance workflows so operations teams can move from analysis to action in one system.
District heating should run like an operations system, not a seasonal spreadsheet
Heat networks are dynamic systems. Weather shifts, occupancy changes, and hydraulic conditions can all move faster than fixed supply-temperature curves. HeatOps gives operators a single operational layer to forecast demand, understand the live state of the network, and coordinate the right response before service quality drops.
From weather signal to operator action
HeatOps combines FactVerse AI Agent, DFS, and the digital twin to close the loop between prediction and execution. Teams can move from forecast to setpoint recommendation, network diagnosis, and maintenance follow-up without switching between disconnected tools.
What operators actually need every morning
Operators do not need another static dashboard. They need to know where load is building, which circuits are drifting out of balance, which losses are abnormal, and which action is most likely to improve service without wasting energy. HeatOps is built around those daily operational questions.
Why HeatOps
| Traditional network operations | HeatOps with DataMesh |
|---|---|
| Fixed outdoor-temperature curves | Forecast-led temperature orchestration |
| Reactive response after complaints | Pre-emptive action before comfort drops |
| Separate SCADA, reports, and maintenance tools | One operational layer across data, twin, and execution |
| Manual hydraulic reviews | Continuous imbalance analysis with recommendations |
| Aggregate reporting at plant level | Circuit-aware visibility and comfort compliance tracking |
Where it fits
- FactVerse AI Agent powers forecasting, anomaly detection, and decision support
- DFS connects SCADA, meter, historian, and weather sources
- Predictive Maintenance closes the loop when anomalies require intervention
- Smart Facility Management extends the same operating model into broader building and campus systems
Frequently Asked Questions
Typical starting points are supply and return temperatures, flow, pressure, outdoor temperature, weather forecast data, and topology or asset context. DFS connects these sources through standard interfaces.
No. HeatOps is designed to sit above existing SCADA, historian, meter, and building data systems. The goal is to create an operational layer, not replace the physical network.
Dashboards show values. HeatOps turns those values into a decision loop: forecast demand, analyze hydraulic state, recommend actions, validate in the twin, and support execution.
Yes. Control-room teams use it for prediction, optimization, and network monitoring; field teams use the same operational context to prioritize inspections, balancing, and maintenance work.