Investigation of maximum cooling loss uncertainty in piping network using Bayesian Markov Chain Monte Carlo method

Heating, Ventilation, and air-conditioning (HVAC) systems have been widely equipped in modern buildings to provide thermal comfort and acceptable indoor air quality, and always represent the largest primary energy end-use. As reported by many researchers, the cooling loss is prevalent in HVAC systems during cooling transmission from cooling sources (chillers) to cooling end-users (conditioning zones), and in some cases, it may even account for as high as 55% of the system total heat flow. At the design stage of an HVAC system, incomplete understanding of the cooling loss may lead to improper sizing of the HVAC system, which may result in additional energy consumption/economic cost (if oversized) or cause insufficient thermal comfort problems (if undersized). Therefore, the cooling loss in a typical HVAC system is significant, and it should be considered in the HVAC system sizing. For HVAC system sizing or retrofit, although there are many studies in the uncertainty in predicting the building peak cooling load, the uncertainty associated with the maximum cooling loss of the HVAC systems are still neglected. Therefore, this study proposes a study to investigate the uncertainty associated with the key parameters in predicting the maximum cooling loss in the HVAC systems using the Bayesian Markov Chain Monte Carlo method. The prior information of the uncertainty together with the available in-situ data is integrated to infer more informative posterior description of the uncertainty. The studied uncertain parameters can either be used for retrofit analysis or be used for prediction of the HVAC system performance. Details of the proposed methodology are illustrated by applying it to a real HVAC system.