Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method. Huang, P., Augenbroe, G., Huang, G., & Sun, Y. Journal of Building Performance Simulation, 12(2):117–132, March, 2019. ![Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{huang_investigation_2019,
title = {Investigation of maximum cooling loss in a piping network using {Bayesian} {Markov} {Chain} {Monte} {Carlo} method},
volume = {12},
issn = {1940-1493},
url = {https://doi.org/10.1080/19401493.2018.1487998},
doi = {10.1080/19401493.2018.1487998},
abstract = {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.},
number = {2},
urldate = {2019-01-06},
journal = {Journal of Building Performance Simulation},
author = {Huang, Pei and Augenbroe, Godfried and Huang, Gongsheng and Sun, Yongjun},
month = mar,
year = {2019},
keywords = {Bayesian inference, HVAC, Markov Chain Monte Carlo Sampling, capacity loss, uncertainty},
pages = {117--132},
}
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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. 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