Building Digital Twin Energy Optimization

I need a digital twin that zeroes-in on my building’s energy performance. Right now I only have partial data—BMS exports, sub-metering on a handful of panels, and monthly utility bills—so the model must be able to start with what’s available, validate any gaps, then scale as more sensors come online.

Primary improvement targets:
• Heating and cooling systems
• Overall energy consumption

The twin should connect to the existing BIM (Revit) files, ingest the live and historical data, and run calibrated simulations—EnergyPlus or an equivalent physics-based engine is fine—so I can visualise real-time KPIs and generate “what-if” scenarios for retrofit ideas. I’m interested in clear, actionable outputs such as predicted savings, payback periods, and dynamic dashboards (Power BI or similar) that a facilities team can understand without a steep learning curve.

Deliverables:
1. A fully-calibrated digital twin model focused on HVAC and whole-building demand.
2. Data integration scripts or connectors (BACnet/IP and CSV imports at minimum).
3. A web-based dashboard highlighting consumption trends, anomaly alerts, and optimisation recommendations.
4. A short hand-off guide plus a live walkthrough so my team can maintain and expand the model.

Success will be measured by the accuracy of predicted versus actual energy use over a two-week validation window and the clarity of the optimisation recommendations. If you’ve built twins for commercial buildings before—especially where initial data was incomplete—I’d love to see a quick outline of your approach and any relevant demos.