A study on influential factors of occupant window-opening behavior in an office building in China

Pan, Song; Xiong, Yingzi; Han, Yiye; Zhang, Xingxing; Liang, Xin; Wei, Shen; Wu, Jinshun; Han, Mengjie

doi:10.1016/j.buildenv.2018.02.008

Cited by 95 publications

(62 citation statements)

References 24 publications

(46 reference statements)

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“…Many field studies also observed that the changing of window status mostly occurred on arrival of the occupants in office buildings. 12,29 humidity, and CO 2 concentration, described the window opening behavior much better than did the outdoor variables, as shown in Table 3.…”

Section: Discussionsmentioning

confidence: 89%

“…The changes in window states mainly occurred in the morning, and the outdoor air temperature significantly influenced the hours of windows opened in the home. Pan et al 12 also showed that the non-environment factors, such as seasonal changes, time of day, personal preference, affect the occupant behavior. For the prediction of the occupant window opening behavior in homes, Yao and Zhao had developed the multivariate linear logistic regression models.…”

Section: Introductionmentioning

confidence: 99%

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Modeling occupant behavior of the manual control of windows in residential buildings

Park

Choi

2018

Indoor Air

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Window opening and closing is the most preferred behavior for occupants to control their indoor environment in homes. This study aims to identify driving forces for window opening and closing behavior in the home. The additional field survey was carried out for the cooling period after following the previous study. The state of windows and environmental variables for outdoor and indoor were continuously monitored in 23 sample homes over one year. The monitored data provide evidence that there is a statistically significant relationship between window opening behavior and outdoor temperature. The behavior of the occupant's manual control of windows can be described by seasonal effects, occupancy, and time of day. Indoor stimuli, such as such as temperature, humidity, and CO2, can better account for the window opening behavior than can outdoor stimuli. There are clear differences in driving variables between window opening and closing behavior. The closing behavior is better described when the outdoor and indoor variables are combined. Finally, multivariate logistic regression models were developed to predict typical patterns of window opening and closing as a function of indoor and outdoor variables.

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Section: Discussionsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Modeling occupant behavior of the manual control of windows in residential buildings

Park

Choi

2018

Indoor Air

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“…Moghadam et al [60] attempted to simulate the impact of individual behavior on the window state, by implementing Logistic regression models to describe three distinct types of window behavior from active, passive and generalized window users. In 2018, Pan et al [61] analyzed the effects of both environmental and non-environmental factors on window opening/closing behaviors and developed window models using Logistic regression. The study was based on a 9.5-month field measurement for a total of 5 windows in 5 offices on the second floor in Beijing, China.…”

Section: State Of the Artmentioning

confidence: 99%

A model based on Gauss Distribution for predicting window behavior in building

Pan

Han

Wei

et al. 2019

Building and Environment

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Modeling of window behavior is a key component for building performance simulation, due to the significant impact of opening/closing windows on indoor environment and energy consumption. The predictions of existing models cannot well reflect actual window behavior, the prediction accuracy still needs to be improved. The Gauss distribution model is a new machine-learning technique which has achieved successful applications in many fields because of its special advantages (i.e. simple structure, strong operability and flexible nonparametric inference ability) compared to existing models. This paper presents results from a study using the Gauss distribution model to predict window behavior in office building. The data used in this study were from a real building located in Beijing, China, and covered two transitional seasons (from October 1 to November 15, 2014 and from March 15 to May 16, 2015), when natural ventilation was fully applied. When modeling, three types of input variables, i.e., indoor temperature, outdoor temperature and their combination were used. This work validates the importance of selecting suitable input variables when developing Gauss distribution model. This study also compared the prediction performance between the Gauss distribution modeling approach and the Logistic regression modeling approach, which is the most popular method used to model occupant window behavior in buildings. The results showed that Gauss distribution models could provide higher prediction accuracy, with 9.5% higher than Logistic regression model when using suitable inputs. This paper provided a novel modeling method that can be used to predict window states more accurately in office buildings.

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“…Window opening and closing actions are mainly driven by thermal comfort preferences. In naturally ventilated buildings (as for the case study), ventilation patterns are strictly connected to indoor and outdoor temperature trends [43][44][45]. Light-switching behaviours are related both to visual comfort and habits.…”

Section: Behavioural Modelsmentioning

confidence: 99%

Measuring Occupants’ Behaviour for Buildings’ Dynamic Cosimulation

et al. 2018

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Measuring and identifying human behaviours are key aspects to support the simulation processes that have a significant role in buildings’ (and cities’) design and management. In fact, layout assessments and control strategies are deeply influenced by the prediction of building performance. However, the missing inclusion of the human component within the building-related processes leads to large discrepancies between actual and simulated outcomes. This paper presents a methodology for measuring specific human behaviours in buildings and developing human-in-the-loop design applied to retrofit and renovation interventions. The framework concerns the detailed building monitoring and the development of stochastic and data-driven behavioural models and their coupling within energy simulation software using a cosimulation approach. The methodology has been applied to a real case study to illustrate its applicability. A one-year monitoring has been carried out through a dedicated sensor network for the data recording and to identify the triggers of users’ actions. Then, two stochastic behavioural models (i.e., one for predicting light switching and one for window opening) have been developed (using the measured data) and coupled within the IESVE simulation software. A simplified energy model of the case study has been created to test the behavioural approach. The outcomes highlight that the behavioural approach provides more accurate results than a standard one when compared to real profiles. The adoption of behavioural profiles leads to a reduction of the discrepancy with respect to real profiles up to 58% and 26% when simulating light switching and ventilation, respectively, in comparison to standard profiles. Using data-driven techniques to include the human component in the simulation processes would lead to better predictions both in terms of energy use and occupants’ comfort sensations. These aspects can be also included in building control processes (e.g., building management systems) to enhance the environmental and system management.

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A study on influential factors of occupant window-opening behavior in an office building in China

Cited by 95 publications

References 24 publications

Modeling occupant behavior of the manual control of windows in residential buildings

Modeling occupant behavior of the manual control of windows in residential buildings

A model based on Gauss Distribution for predicting window behavior in building

Measuring Occupants’ Behaviour for Buildings’ Dynamic Cosimulation

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