Pei Huang

Investigation of the ageing effect on chiller plant maximum cooling capacity using Bayesian Markov Chain Monte Carlo method

Ageing inevitably leads to capacity degradation in a chiller plant. Hence in the life-cycle performance analysis of a chiller plant, ageing always represents a crucial consideration for designers. Ageing is normally quantified using maintenance factor. A conventional analysis recommends that the maintenance factor should be 0.01 for systems that undergo annual professional maintenance, and 0.02 for those that are seldom maintained. However, this recommendation is mainly based on a rule of thumb, and may not be accurate enough to describe the ageing for a given chiller plant. This research therefore proposes a method of identifying the chiller maintenance factor using a Bayesian Markov Chain Monte Carlo method, which can take account of the uncertainties that exist in the estimation of the ageing. Details of the identification will be provided by applying the proposed method to a real chiller plant, and results will be compared with that of the conventional analysis.

Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty

Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-model-based system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally selected. Cases studies with 1331 design options have validated the proposed method for 10,000 randomly produced decision scenarios (i.e., users’ preferences to the design criteria). The results show that the established response surface models reasonably predict the design criteria with errors no greater than 3.5% at a cumulative probability of 95%. The proposed method reduces the number of Monte Carlos simulations by 97.8%, and robustly sorts out top 1.1% design options in expectation. With the largely reduced Monte Carlo simulations and high overall performance of the selected design option, the proposed method provides a practical and efficient means for system sizing of nearly/net ZEBs under uncertainty.

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

Cooling loss during transmission from cooling sources (chillers) to cooling end-users (conditioned zones) is prevalent in HVAC systems. At the HVAC design stage, incomplete understanding of the cooling loss may lead to improper sizing of HVAC systems, which in turn may result in additional energy consumption and economic cost (if oversized) or lead to inadequate thermal comfort (if under-sized). For HVAC system sizing or retrofit, there is a lack of study of uncertainties associated with the maximum cooling loss of HVAC systems although uncertainties in predicting building maximum cooling load have been studied by many researchers. This paper, therefore, proposes a study to investigate the uncertainties associated with the major parameters in predicting the maximum cooling loss in HVAC piping networks using the Bayesian Markov Chain Monte Carlo method. Prior information of those uncertainties combined with available in-situ data, is implemented to produce more informative posterior descriptions of the uncertainties. To facilitate the application, uncertain parameters are categorized into specific and generic types. The posterior information gathered for the specific parameters can be used in retrofit analysis, whereas that acquired for the generic parameters can be referred to in new HVAC system design. Details of the proposed methodology are illustrated by applying it to a real HVAC system.