From Static to Adaptive: A Physics and Data Driven Virtual Testing Framework for Retro-Commissioning of HVAC Systems

Traditional one-time handover commissioning often fails to identify operational inefficiencies that emerge during long-term operation of heating, ventilation, and air-conditioning (HVAC) systems, while on-site commissioning remains labor-intensive and less adaptable to changing operating conditions. This study proposes a physics- and data-driven virtual testing framework to enable virtual retro-commissioning and adaptive control optimization. A calibrated Modelica-based system model is developed as a virtual testbed, where data-driven/physics-informed predictors are used to support unit operation optimization and preheating control. Physics-based representation provides scenario-rich simulations and intermediate physical variables, while the data-driven components translate these insights into fast, control-oriented predictors. The framework is demonstrated on an underperforming ground source heat pump (GSHP) system. Results show that the optimized control strategy reduces energy consumption by 16.1% while maintaining indoor thermal conditions comparable to those under the existing strategy, and increases the operational coefficient of performance (COP) from 3.0 to 4.4. Virtual evaluation over a representative 14-day winter period indicates a 14.6% reduction in electricity costs without hardware modifications. The proposed framework enables low-risk, model-based retro-commissioning and provides a practical pathway for adaptive performance optimization of existing HVAC systems.