This is like creating a very detailed “digital twin” of a building that predicts how its temperature changes over the day so that the heating and cooling system can be run in the smartest, cheapest way possible without making people uncomfortable.
Building owners and operators struggle to keep energy costs down while maintaining comfort because HVAC systems are often run using simple rules or static schedules that don’t account for how a specific building actually behaves thermally. Thermal building models allow energy management systems to anticipate heating and cooling needs and optimize HVAC control dynamically, reducing waste and improving comfort.
Domain-specific thermal models calibrated to real building data, combined with integration into existing building automation/energy management workflows, can be hard to replicate and improve in accuracy over time as more operational data is collected.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Model calibration and parameter estimation for each individual building, plus data quality and sensor coverage constraints, can limit scalability across large heterogeneous building portfolios.
Early Majority
Focus on detailed thermal behavior modeling explicitly for integration into energy management systems, rather than generic building analytics, enabling more precise predictive control of HVAC and energy flows.