An explanatory parametric model to predict comprehensive post-commissioning building performances

Existing building commissioning (EBCx) is a promising approach to save energy and improve indoor environmental quality (IEQ) without sufficient attention yet. However, the uncertainty of building performance post-retrofit leads to a low adoption rate of EBCx, as the commissioning outcomes are dependent on various factors and difficult to model. To facilitate decision-making on EBCx, this paper introduces PECCO, a parametric mathematical model enriched with years of commissioning engineering experience to predict comprehensive EBCx outcomes for various types of public buildings, along with a mobile-based tool to automate the prediction process. An 8-min questionnaire is used to collect building parameters regarding general, management, HVAC, smart system, IEQ, electrical, water supply, and building envelope. Then, a mathematical model predicts commissioning outcomes including energy-saving potential, annual cost saving, asset value increase, comfort-improvement potential, operation risk, commissioning cost, simple payback period, and difficulty in commissioning, and provides overall commissioning strategies, specific commissioning suggestions, and benchmark figures compared with other buildings. Three case studies suggested that PECCO's prediction on the simple payback period achieved a mean absolute percentage error (MAPE) of 15.47%, which is acceptable and comparable to other related studies. By providing various perspectives of information to building owners and managers, PECCO is helpful to foster the industry to undertake EBCx, leading to a more energy-efficient, low-carbon building industry. •Proposed a mathematical model to predict building commissioning outcomes.•Predicted results include performances on energy, cost, comfort, strategies, etc.•Developed a user-friendly mobile-based tool PECCO to facilitate industrial uptake.•PECCO provides a quick diagnosis to help decision-making on building commissioning.•Three case studies showed a mean absolute percentage error of 15.67%.