Zonal model for predicting contaminant distribution in stratum ventilated rooms

Lu, Yalin; Zhang, Yuchun

doi:10.1111/ina.13061

Cited by 5 publications

(3 citation statements)

References 62 publications

(126 reference statements)

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“…It gives BEMS with a set of boundary targets within which the building's energy usage should ideally fall (Deb et al , 2017). Fumo (2014) classified the forecasting models into categories such as physical, simulation models (Lu et al , 2022), grey box and data-driven models. The two approaches for IRT forecasting are broadly classified as: Forward/Deterministic approach : It depends on equations explaining the physical and thermal behavior of buildings. Data driven approach : It depends on historical data of building (Wei et al , 2018).…”

Section: Resultsmentioning

confidence: 99%

“…It gives BEMS with a set of boundary targets within which the building's energy usage should ideally fall (Deb et al, 2017). Fumo (2014) classified the forecasting models into categories such as physical, simulation models (Lu et al, 2022) (1) Forward/Deterministic approach: It depends on equations explaining the physical and thermal behavior of buildings.…”

Section: Findings 41 Application Of Indoor Room Temperature Forecasti...mentioning

confidence: 99%

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Indoor room temperature forecasting in buildings: a literature review (2001-21)

Pandey

Basu²

2022

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Purpose Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a complex and challenging task, especially when energy demands are exponentially rising. The purpose of this paper is to review the relevant literature on indoor temperature forecasting in the past two decades and draw inferences on important methodologies with influencing variables and offer future directions. Design/methodology/approach The motivation for this work is based on the research work done in the field of intelligent buildings and energy related sector. The focus of this study is based on past literature on forecasting models and methodologies related to IRT forecasting for building energy management, with an emphasis on data-driven models (statistical and machine learning models). The methodology adopted here includes review of several journals, conference papers, reference books and PhD theses. Selected forecasting methodologies have been reviewed for indoor temperature forecasting contributing to building energy consumption. The models reviewed here have been earmarked for their benefits, limitations, location of study, accuracy along with the identification of influencing variables. Findings The findings are based on 62 studies where certain accuracy metrics and influencing explanatory variables have been reviewed. Linear models have been found to show explanatory relationships between the variables. Nonlinear models are found to have better accuracy than linear models. Moreover, IRT profiles can be modeled with enhanced accuracy and generalizability through hybrid models. Although deep learning models are found to have better performance for this study. Research limitations/implications This is accuracy-based study of data-driven models. Their run-time performance and cost implications review and review of physical, thermal and simulation models is future scope. Originality/value Despite the earlier work conducted in this field, there is a lack of organized and comprehensive evaluation of peer reviewed forecasting methodologies. Indoor temperature depends on various influencing explanatory variables which poses a research challenge for researchers to develop suitable predictive model. This paper presents a critical review of selected forecasting methodologies and provides a list of important methodologies along with influencing variables, which can help future researchers in the field of building energy management sector. The forecasting methods presented here can help to determine appropriate heating, ventilation and air-conditioning systems for buildings.

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Section: Resultsmentioning

confidence: 99%

“…It gives BEMS with a set of boundary targets within which the building's energy usage should ideally fall (Deb et al, 2017). Fumo (2014) classified the forecasting models into categories such as physical, simulation models (Lu et al, 2022) (1) Forward/Deterministic approach: It depends on equations explaining the physical and thermal behavior of buildings.…”

Section: Findings 41 Application Of Indoor Room Temperature Forecasti...mentioning

confidence: 99%

Indoor room temperature forecasting in buildings: a literature review (2001-21)

Pandey

Basu²

2022

View full text Add to dashboard Cite

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“…19,22,23 A highly accurate and robust numerical simulation model would greatly benefit the design of an effective ventilation strategy to mitigate infection transmission risk. Three methods, namely, multi-zone models, 24 zonal models 25 and computational fluid dynamics (CFD), 26 are commonly employed to predict the air distribution within a building. Multi-zone models are a low-cost computational modelling solution, but it has limited applicability due to low accuracy and the treatment of each room as a single node.…”

Section: Introductionmentioning

confidence: 99%

Optimal ventilation strategy for multi-bed hospital inpatient wards: CFD simulations using a genetic algorithm

Satheesan,

Tsang,

Mui

et al. 2023

Indoor and Built Environment

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Optimising ventilation strategy for an indoor environment necessitates systematically evaluating the influence of a diverse combination of physical and operational parameters in the design space. This study proposes a methodology that couples an evolutionary algorithm (genetic algorithm) with an evaluation mechanism (computational fluid dynamics) to determine the optimal ventilation strategy for an inpatient ward. The traditional approach would exhaustively simulate numerous scenarios to identify the optimal combination of parameters meeting the design objective. The proposed methodology would iteratively evaluate diverse design solutions with fewer CFD simulations than the traditional approach. The results of design space exploration suggest that design parameters, namely, location of the infected patient; air change rate; flow rate through local exhaust grille; and number, location and size of supply air diffuser and local air exhaust grille, are critical in minimising the risk of cross-infection caused through contact transmission in a ward.

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Impact of different supply modes of stratum ventilation on airflow and contaminant distribution characteristics

Liu

2023

Building and Environment

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Zonal model for predicting contaminant distribution in stratum ventilated rooms

Cited by 5 publications

References 62 publications

Indoor room temperature forecasting in buildings: a literature review (2001-21)

Indoor room temperature forecasting in buildings: a literature review (2001-21)

Optimal ventilation strategy for multi-bed hospital inpatient wards: CFD simulations using a genetic algorithm

Impact of different supply modes of stratum ventilation on airflow and contaminant distribution characteristics

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