Seasonal variation of window opening behaviors in two naturally ventilated hospital wards

Shi, Zhenni; Qian, Hua; Zheng, Xiaohong; Lv, Zhengfei; Li, Yuguo; Liu, Li; Nielsen, Peter

doi:10.1016/j.buildenv.2017.12.019

Cited by 67 publications

(26 citation statements)

References 38 publications

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“…However, it should be noted that no significant literature was found about motorized windows surrounding human-building interactions. As evidenced in the following sections, the majority of existing studies were focused on office buildings and few studies were found about the identification of key variables influencing window control behavior in residential [46]- [49] educational [50], and healthcare [51] buildings.…”

Section: Operable Windowsmentioning

confidence: 99%

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A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort

Day

McIlvennie

Brackley

et al. 2020

Building and Environment

102

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In recent years, research has emerged to quantitatively and qualitatively understand occupants' interactions with buildings. However, there has been surprisingly little research on building interfaces and how their design, context (e.g., location), and underlying logic impact their usability and occupants' perceived control, as well as the resulting comfort and energy performance. Research is needed to better understand how occupants interact with building interfaces in both commercial and residential applications; both applications are important to address as there are many differences in interface types, level of control and understanding, and even expectations of engagement. This paper provides a cursory review and discussion of select common building interfaces: windows, window shades/blinds, thermostats, and lighting controls. The goal of this paper is to review literature related to these human-building interfaces to explore interface characteristics, current design and use challenges, and relationships between building interfaces and occupants. Human-building interface interactions are complex, more research is needed to understand design, use, and characteristics. Common themes emerged throughout the literature review to explain occupant interactions (or lack of interactions) with building interfaces, which included thermal and visual comfort, ease and access of control, interface/control placement, poor interface/control design, lack of understanding, and social-behavioral dynamics.

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Section: Operable Windowsmentioning

confidence: 99%

“…Personal control of windows differs significantly between different building typologies [51] and climate type [52], according to function, usability and living habits. Patterns of interaction between the occupants and windows vary according to building end-use and, in particular, between office and residential buildings.…”

Section: Rq1: What Influences Behavior?mentioning

confidence: 99%

A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort

Day

McIlvennie

Brackley

et al. 2020

Building and Environment

102

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“…This study was designed to improve the understanding of the interactions among indoor and outdoor environmental parameters and the roles they play in influencing people's WOB. Many quantitative methods have been used to model WOB such as logistic regression [5,16,18,19,28,29], Markov chains [30], Gauss distribution [30], and deep learning [31]. No studies have been found to apply SEM in the WOB field.…”

Section: Discussionmentioning

confidence: 99%

“…Logistic regression remains a popular method among researchers for modeling WOB [5,16,18,19,28,29]. However, other modeling approaches have also been used in the literature.…”

Section: Introductionmentioning

confidence: 99%

Indoor/Outdoor Environmental Parameters and Window-Opening Behavior: A Structural Equation Modeling Analysis

et al. 2019

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In moderate climates, the operation of windows is the most common way to control for thermal comfort. Window-opening behavior (WOB) is a complex process influenced by multiple factors, yet only simple bi-variate analyses between variables obtained from longitudinal datasets have been examined. The goal of this study is to investigate the effects of indoor and outdoor environmental parameters on WOB using a statistical modeling approach called “structural equation modeling.” The results show that the indoor environmental parameters, such as operative temperature and air velocity, mediated the relationship between the outdoor environmental parameters, such as outdoor air temperature and wind gust, and the WOB. The indoor wet-bulb globe temperature rose as the solar radiation increased, and subsequently, both parameters affected the WOB. Also, an increase in outdoor wind gust led to higher indoor air velocity, which in turn resulted in a lower chance of occupants opening the window. By enhancing our understanding of the relationship between these theoretical parameters, improved design strategies on the mediating parameters can be prioritized and communicated early in the building design phase leading to more informed design decisions.

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“…Yun et al [28] have used this method and developed a model predicting window behavior of occupants in an office building in the UK. Using data collected from two hospital wards in Nanjing in China, Shi et al [29] have developed a logistic window behavior model, and got a prediction accuracy of 70%.…”

Section: Introductionmentioning

confidence: 99%

Developing window behavior models for residential buildings using XGBoost algorithm

Hao

Sun

Liu

et al. 2019

Energy and Buildings

120

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Buildings account for over 32% of total society energy consumption, and to make buildings more energy efficient dynamic building performance simulation has been widely adopted during the buildings' design to help select most appropriate HVAC (Heating Ventilation and Air Conditioning) systems. Due to the lack of good behavioral models in current simulation packages, many researchers have tried to develop useful behavioral models to improve simulation accuracy, including window behavior models, using field data collected from real buildings. During this work, many mathematical and machine learning methods have been used, and some level of prediction accuracy has been achieved. XGBoost is a recently introduced machine learning algorithm, which has been proven as very powerful in modeling complicated processes in other research fields. In this study, this algorithm has been adopted to model and predict occupant window behavior, aiming to further improve the modeling accuracy from a globally accepted modeling approach, namely, Logistic Regression Analysis. Field data in terms of both occupant window behavior and relevant influential factors were collected from real residential buildings during transitional seasons. Both XGBoost and Logistic Regression Analysis were used to build window behavior models, after a feature selection work, and their prediction performances on an independent dataset were compared. The comparison revealed that XGBoost has solid advantages in modeling occupant window behavior, over Logistic Regression Analysis, and it is expecting that the same finding would be obtained for other behavioral types, such as blind control and air-conditioner operation.

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Seasonal variation of window opening behaviors in two naturally ventilated hospital wards

Cited by 67 publications

References 38 publications

A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort

A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort

Indoor/Outdoor Environmental Parameters and Window-Opening Behavior: A Structural Equation Modeling Analysis

Developing window behavior models for residential buildings using XGBoost algorithm

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