Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

Abstract

This paper presents the development of stochastic models of occupants' main bedroom window operation based on measurements collected in ten UK dwellings over a period of a year. The study uses multivariate logistic regression to understand the probability of opening and closing windows based on indoor and outdoor environment factors (physical environmental drivers) and according to the time of the day and season (contextual drivers). To the authors' knowledge, these are the first models of window opening and closing behaviour developed for UK residential buildings. The work reported in this paper suggests that occupants' main bedroom window operation is influenced by a range of physical environmental (i.e. indoor and outdoor air temperature and relative humidity, wind speed, solar radiation and rainfall) and contextual variables (i.e. time of day and season). In addition, the effects of the physical environmental variables were observed to vary in relation to the contextual factors. The models provided in this work can be used to calculate the probability that the main bedroom window will be opened or closed in the next 10 minutes. These models could be used in building performance simulation applications to improve the inputs for occupants' window opening and closing behaviour and thus the predictions of energy use and indoor environmental conditions of residential buildings.

Lessons from in-home air filtration intervention trials to reduce urban ultrafine particle number concentrations

“…Window opening reduces the effectiveness of filtration by allowing ambient UFP into the home [44]. This may have affected our ability to reduce PNC indoors to our goal of 80–90% reduction.…”

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

“…The occupants' window opening behaviors play a decisive role on the natural ventilation performance and significantly influence the airborne infection risk and indoor air quality (IAQ) [8], as well as the energy consumption for air-conditioning systems [9][10][11]. Many factors are known to influence the window opening behaviors, including thermal-driven factors, time-related factors, environmentdriven factors, psychological factors and other uncertain factors [12][13][14][15][16][17][18][19][20][21][22][23][24][25]34,35]. Thermal-driven factors, including outdoor climate conditions, indoor air temperature and relative humidity, occupants' number, clothing condition, gender, etc., are recognized as the most important driving factors in many previous studies [12][13][14][15][16][17].…”

“…Windows were opened and closed at certain times of the day (morning and late afternoon hours) regardless of the different day in a week. Jones et al [20] investigated the impact of season on residential buildings for window opening/closing behaviors, finding that seasonality affected both frequency and drivers of window operation in bedrooms. Same simulation models to predict window operation behavior could be used in spring and autumn.…”

Physical environmental and contextual drivers of occupants’ manual space heating override behaviour in UK residential buildings

“…Internet. The data used in this study were collected as part of a larger Post Occupancy Evaluation (POE) to assess 141 the actual operational performance of the dwellings [33,41,42].…”

“…The multivariate logistic regression method has been used for modelling occupant behaviour in a range of previous 248 studies, (e.g. [27,28,33,44]).…”