Window Opening Behavior Research Articles

Occupants’ interactions with windows in 8 residential apartments in Beijing and Nanjing, China

Occupants’ interactions with windows influence both building energy consumption and exposure to airborne pollutants indoors. Occupants’ window opening behavior varies from region to region due to physical environmental factors and social reasons. China is now confronting severe atmospheric pollution, which may affect occupants’ window opening behaviors. A field study was conducted in 8 naturally ventilated residential apartments in Beijing and Nanjing, China. This involved periodically monitoring window states of eight residential apartments within each season from October 2013 to December 2014 by magnetic induction devices (TJHY, CKJM-1). Relationships between the probability of window opening (p) and explanatory variables, including outdoor air temperature (t o), outdoor relative humidity (RH), outdoor wind speed (V s), and ambient PM2.5 (particles with aerodynamic diameter less than 2.5 microns) concentrations (C p), were analyzed. Stochastic models of occupants’ interactions with windows in monitored residences were established via univariate and multivariate linear logistic regression for both cities. According to the results, t o is the most important explanatory variable affecting occupants’ interactions with windows in monitored residences. The best multivariate linear logistic model result from the “backward selection” procedure based on “Akaike Information Criterion” (AIC) includes t o, RH, V s and C p as explanatory variables, which implied that outdoor air quality, represented by C p, has become a concern affecting Chinese residents’ interactions with windows.

A generalized probabilistic formula relating occupant behavior to environmental conditions

The occupant behavior (use of heating, use of air conditioning, use of light, opening of windows, adjustment of curtains and blinds, etc.) in a building is usually closely related to the environmental conditions. This paper presents a novel general mathematical formula for describing the relationships between such behavior and the environmental conditions based on a memoryless hypothesis and psychophysical function (Stevens' power law). Compared to existing formulas, the proposed formula is simple and harmonizes both the threshold and stochastic characteristics of an occupant's response to environmental stimuli. Preliminary test showed that the formula has good applicability and reliability. The proposed formula can serve as a basic reference for detailed modeling of an occupant's heating, air conditioning, lighting, window opening, and shading adjustment behavior in the future.

Verification of stochastic behavioural models of occupants' interactions with windows in residential buildings

Realistic characterisation of occupants' window opening behaviour is crucial for reliable prediction of building performance by means of building energy performance simulations. Window opening behaviour has been investigated by several researchers, leading to a variety of logistic regression models expressing the probability with which actions will be performed. But only very few attempts have been made to investigate the reliability of the models. In this paper, data from a measurement campaign in 15 apartments was used to estimate the predictive accuracy of four sets of models of window opening. Initially three models from literature were investigated by comparison of predicted probabilities and the actual measured state of the windows.Data from one of the papers was reanalysed to create new models, based on measurements from single dwellings. These models were used to predict window transition probabilities using data from the field survey. The output was then compared to the measured transitions.Results showed that the models which most accurately predicted both the state of the window (open or closed) and the number of actions on windows had certain characteristics in common: A positive correlation between the probability of opening and CO2 concentration and illumination values and a negative correlation with sun hours and illumination level for closing windows.

Numerical Study on the Effect of Natural Ventilation and Optimal Orientation of Residential Buildings in Changsha, China

Indoor environment affects human's health directly. In residential buildings, natural ventilation can be applied as an effective way of ventilation. Different window-opening behavior results in different indoor environment. Five window-opening behavior of a common residential building in Changsha were compared and analyzed by using Airpak. Energy consumption of building with different orientation was calculated by DesignBuilder according to the Changsha typical meteorological data. A new evaluation index was proposed to compare the design schemes with consideration of the performance of natural ventilation and the building energy consumption. The optimal scheme combination of the building orientation and the opening's position was obtained by the index.

Drivers and barriers to occupant adaptation in offices in India

Occupant window-opening behaviour in Indian offices is a nascent field. This paper relies on the thermal comfort field study data from 28 Indian offices in Hyderabad and Chennai. Occupants in naturally ventilated buildings used the windows and doors adaptively as the seasons changed and the temperature varied. We found that 50% of the windows would be opened at an indoor air temperature of 30 °C, using logistic regression. We noted some non-thermal factors possibly affecting the adaptive operation of controls as well, including: design and construction, operation and maintenance, environmental, sociocultural, attitudinal and behavioural factors. A window's potential for modifying the comfort temperature hinges on the effective handling of these hurdles. We further categorized the barriers into those in the occupant's realm and beyond. Each category is further identified with the extent to which the barrier interferes with the control as an adaptive opportunity.

A data-mining approach to discover patterns of window opening and closing behavior in offices

Understanding the relationship between occupant behaviors and building energy consumption is one of the most effective ways to bridge the gap between predicted and actual energy consumption in buildings. However effective methodologies to remove the impact of other variables on building energy consumption and isolate the leverage of the human factor precisely are still poorly investigated. Moreover, the effectiveness of statistical and data mining approaches in finding meaningful correlations in data is largely undiscussed in literature. This study develops a framework combining statistical analysis with two data-mining techniques, cluster analysis and association rules mining, to identify valid window operational patterns in measured data. Analyses are performed on a data set with measured indoor and outdoor physical parameters and human interaction with operable windows in 16 offices. Logistic regression was first used to identify factors influencing window opening and closing behavior. Clustering procedures were employed to obtain distinct behavioral patterns, including motivational, opening duration, interactivity and window position patterns. Finally the clustered patterns constituted a base for association rules segmenting the window opening behaviors into two archetypal office user profiles for which different natural ventilation strategies as well as robust building design recommendations that may be appropriate. Moreover, discerned working user profiles represent more accurate input to building energy modeling programs, to investigate the impacts of typical window opening behavior scenarios on energy use, thermal comfort and productivity in office buildings.

Determinants of ventilation behavior in naturally ventilated dwellings: Identification and quantification of relationships

BackgroundVentilation in dwellings is essential for well-being and health. However, insight in determinants of ventilation behavior is still limited. AimIdentifying determinants of ventilation behavior and quantifying relationships. Secondly, identifying households characteristics associated with low ventilation activity. MethodsIn a cross sectional study, ordinal and binary logistic regression analyses were performed, in a large population based survey, to identify determinants of window and vent opening behavior in the main living areas during the heating season in naturally ventilated dwellings. Relationships were quantified. ResultsWindow opening behavior was associated (although not always consistently between models) positively with household size, negatively with disposable income (living- and bedroom), and with ethnicity (association different in direction between room types). Furthermore, window opening behavior was positively associated with ‘respondent or partner does not have a paid income’ (living room only), presence of females and age of the oldest occupant (bedroom only). In addition, significant associations were found between dwelling characteristics and window opening behavior, including the presence of vents (strong negative association, both room types), type of dwelling (association different in direction between room types), and the overall heat transfer coefficient by transmission. A smaller number of household and dwelling characteristics was found associated with vent opening behavior. ConclusionWindow and vent opening behavior was associated with a range of both household and dwelling characteristics. More research is needed to confirm associations.

Probability of occupant operation of windows during transition seasons in office buildings

AbstractWindow operation is not only an important method for improving the indoor thermal environment and air quality, but also a significant way to reduce energy consumption of air-conditioned rooms during off-running periods in transition seasons. The occupants' window-operation behavior is influenced by both objective factors, such as thermal comfort and indoor air quality; and objective sensation, such as psychology and physiology, introducing considerable randomness and uncertainty. A two-month field observation of occupant window-opening behaviors for natural ventilation in an office building during the transition seasons was carried out in Chongqing, China. Multi-factor analysis of variance was conducted in data analysis using SPSS statistical software. The results showed that outdoor air temperature significantly affected window opening among other factors such as outdoor relative humidity, indoor air temperature, indoor relative humidity, and indoor CO2 concentration, which have much less effect. The main trigger point for opening windows in the transition seasons is from occupants' desire to improve the indoor thermal and air quality environment. A probability model of occupants' window operation was proposed based on logistic regression analysis. Meanwhile, the Monte Carlo simulation results indicate that during transition seasons (when outdoor temperature varied from 15 to 30 °C), the probability of window opening in office buildings follows a normal distribution and increases linearly along with the outdoor temperature growth.

Stochastic control optimization for a mixed mode building considering occupant window opening behaviour

A methodology has been developed for optimizing building supervisory control strategies, employing building models that incorporate stochastic models of occupant behaviour and serve as the objective function evaluator in a stochastic model predictive control (SMPC) architecture. The SMPC architecture accounts for variability in building performance due to occupant behaviour and is shown to generate a sequence of automatic window opening decisions for a mixed mode building which lead to more robust building performance in the face of occupant window use than a heuristic controller. A set of receding optimization time horizons are described which enable the use of complex building models in simulated SMPC. Results of a case study show that deterministic optimization predicts a 50% increase in building performance, while stochastic optimization leads to a more conservative and more reliable 33% performance improvement, which takes into consideration the impact of occupant behaviour.

Ventilation system type, classroom environmental quality and pupils' perceptions and symptoms

The present study investigated indoor climate and window opening behaviour by pupils, as well as their perceptions and symptoms in classrooms with different types of ventilation systems. Four classrooms were selected in the same school in suburban Denmark. Classroom ventilation was achieved either by manually operable windows, or by automatically operable windows with and without an exhaust fan in operation, or by a balanced mechanical ventilation system. Indoor air temperature, relative humidity, carbon dioxide (CO2) concentration and window opening were continuously monitored for one month in non-heating and heating seasons; CO2 concentration was used to estimate average classroom ventilation rates. At the end of each measuring period, the pupils were asked to report their perceptions of the indoor environment and their acute health-related symptoms. The classroom in which ventilation was achieved by manually operable windows had the highest air temperatures and CO2 concentrations during both non-heating and heating season; the estimated average air-change rate was lowest in this classroom. The classroom with mechanical ventilation had the highest estimated average air-change rate. Windows were frequently opened in all four classrooms in the non-heating season but very seldom in the heating season. Automatic operation of the windows had a marked effect on CO2 concentration and classroom temperature in the heating season. Perceptions of the indoor environment were more positive in the classroom that was ventilated by automatically operable windows with an exhaust fan in operation: fewer symptoms were reported in this classroom compared with classrooms with other systems. Present results and approach can be used as the basis for rational selection of systems that ensure adequate classroom ventilation.

Fine and Ultrafine Particle Decay Rates in Multiple Homes

Human exposure to particles depends on particle loss mechanisms such as deposition and filtration. Fine and ultrafine particles (FP and UFP) were measured continuously over seven consecutive days during summer and winter inside 74 homes in Edmonton, Canada. Daily average air exchange rates were also measured. FP were also measured outside each home and both FP and UFP were measured at a central monitoring station. A censoring algorithm was developed to identify indoor-generated concentrations, with the remainder representing particles infiltrating from outdoors. The resulting infiltration factors were employed to determine the continuously changing background of outdoor particles infiltrating the homes. Background-corrected indoor concentrations were then used to determine rates of removal of FP and UFP following peaks due to indoor sources. About 300 FP peaks and 400 UFP peaks had high-quality (median R(2) value >98%) exponential decay rates lasting from 30 min to 10 h. Median (interquartile range (IQR)) decay rates for UFP were 1.26 (0.82-1.83) h(-1); for FP 1.08 (0.62-1.75) h(-1). These total decay rates included, on average, about a 25% contribution from air exchange, suggesting that deposition and filtration accounted for the major portion of particle loss mechanisms in these homes. Models presented here identify and quantify effects of several factors on total decay rates, such as window opening behavior, home age, use of central furnace fans and kitchen and bathroom exhaust fans, use of air cleaners, use of air conditioners, and indoor-outdoor temperature differences. These findings will help identify ways to reduce exposure and risk.

Window opening behavior and exposure in the U.S.

< Open windows dramatically alter occupant exposure to air pollutants of indoor and outdoor origin. However few studies have quantified the extent to which windows are open in North American residences. In this research, we use readily available data and survey instruments to understand when and how much windows are open by U.S. region. We reviewed a large set of images of residences, available from Google Streetview, to determine what fraction of these residences have windows open during the daytime (when images were collected). In addition, we surveyed 3000 randomly selected individuals across the US about window opening habits. During the 9 months of spring, summer and autumn, 20% of occupants open house windows 8 or more hours per day. Residents in the US Southeast are much more likely to keep windows closed than the rest of the country (2.8 hours vs 4.7 hours on average). Windows are more likely to be opened during the morning (28%) and afternoon (28%) than in the evening (18%). Implications: given regional differences in behavior, building construction and ambient air pollution, we anticipate significant differences in inhalation dose that may be greater than the differences in outdoor air pollution concentrations. For example, daily integrated inhalation dose of ozone in St Louis is estimated to be equal or greater than that in Atlanta, where ambient ozone concentrations trend higher. The tendency towards morning and afternoon window opening will increase relative exposure to ozone and some other photochemical oxidants. Lower window-opening frequency at night enhances exposure to indoor sourced pollutants while sleeping.

Window opening behaviour modelled from measurements in Danish dwellings

A method of defining occupants' window opening behaviour patterns in simulation programs, based on measurements is proposed.Occupants' window opening behaviour has a strong effect on indoor environment and the energy consumed to sustain it. Only few models of window opening behaviour exist and these are solely based on the thermal indoor/outdoor environment. Consequently, users of simulation software are often left with little or no guidance for the modelling of occupants' window opening behaviour, resulting in potentially large discrepancies between real and simulated energy consumption and indoor environment.Measurements of occupant's window opening behaviour were conducted in 15 dwellings in Denmark during eight months. Indoor and outdoor environmental conditions were monitored in an effort to relate the behaviour of the occupants to the environmental conditions. The dwellings were categorized in four groups according to ventilation type (natural/mechanical) and ownership (owner-occupied/rented) in order to investigate common patterns of behaviour. Logistic regression was used to infer the probability of opening and closing a window.The occupants' window opening behaviour was governed by different but distinct habits in each dwelling. However, common patterns were also identified in the analysis: Indoor CO2 concentration (used as indicator of indoor air quality) and outdoor temperature were the two single most important variables in determining the window opening and closing probability, respectively.The models could be implemented into most simulation programs, which would enable a better chance of mimicking the behaviour of the occupants in the building and thus simulating the indoor environment and energy consumption correctly.

A methodology for modelling energy-related human behaviour: Application to window opening behaviour in residential buildings

An energy simulation of a building is a mathematical representation of its physical behaviour considering all the thermal, lighting, acoustics aspects. However, a simulation cannot precisely replicate a real construction because all the simulations are based on a number of key assumptions that affect the results accuracy. Above all, the real energy performance can be affected by the actual behaviour of the building occupants. Thus, there are great benefits to be derived from improving models that simulate the behaviour of human beings within the context of engineered complex systems. The occupant behaviour related to the building control potentialities is a very complex process that has been studied only in the last years with some focuses related to natural ventilation (window opening behaviour), space heating energy demand (in particular the adjustments in the temperature set-point) and natural light (focusing on window blinds adjustments). In this paper, a methodology is presented to model the user behaviour in the context of real energy use and applied to a case study. The methodology, based on a medium/long-term monitoring, is aimed at shifting towards a probabilistic approach for modelling the human behaviour related to the control of indoor environment. The procedure is applied at models of occupants’ interactions with windows (opening and closing behaviour). Models of occupants’ window opening behaviour were inferred based on measurements and implemented in a simulation program. Simulation results were given as probability distributions of energy consumption and indoor environmental quality depending on user behaviour.

Investigation of comfort temperature, adaptive model and the window-opening behaviour in Japanese houses

This study described in this paper was undertaken to investigate comfort temperatures and related behaviours in Japanese homes. In it temperatures were measured in the living rooms and a thermal comfort survey of residents and related window-opening behavioural survey was completed over a full year in the Gifu region of Japan. The residents were found to be highly satisfied with the thermal environment of their houses. Significant seasonal differences were found in their comfort temperatures. The results showed that comfort temperature changes varied with changes in both the indoor and outdoor climate. The strength of the relationship between indoor and outdoor temperatures justified the adoption of the adaptive model for both prediction and design of control strategies for the provision of indoor comfort. The window-opening behaviours were shown to be related to both the indoor or outdoor air temperatures. The deadband was narrower and constraints on the window opening in the houses investigated were both considerably lower than previously found in office building studies. The adaptive model is highly supported by this study of occupant perceptions and window-opening behaviour.

Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models

Energy consumption in buildings is influenced by several factors related to the building properties and the building controls, some of them highly connected to the behaviour of their occupants.In this paper, a definition of items referring to occupant behaviour related to the building control systems is proposed, based on studies presented in literature and a general process leading to the effects on energy consumptions is identified.Existing studies on the topic of window opening behaviour are highlighted and a theoretical framework to deal with occupants' interactions with building controls, aimed at improving or maintaining the preferred indoor environmental conditions, is elaborated. This approach is used to look into the drivers for the actions taken by the occupants (windows opening and closing) and to investigate the existing models in literature of these actions for both residential and office buildings. The analysis of the literature highlights how a shared approach on identifying the driving forces for occupants' window opening and closing behaviour has not yet been reached. However, the reporting of variables found not to be drivers may reveal contradictions in the obtained results and may be a significant tool to help direct future research.

Factors influencing variability in the infiltration of PM2.5 mass and its components

The infiltration of particles into homes can vary seasonally, between homes in a community and between communities. However, few studies have examined the day to day variability across multiple homes. We used continuous data collected from a 2-year (2005–2006) personal exposure study conducted in Windsor, ON to estimate daily infiltration factors (Finf) for fine particulate matter (PM2.5), Black Carbon (BC), and ultrafine particles (UFP) as well as the ambient personal exposure factor (Fpex) for PM2.5. In addition, the daily ambient and non-ambient generated components of indoor and personal concentrations were estimated.Median daily Finf estimates ranged from 0.26 to 0.36 across seasons for PM2.5; from 0.28 to 0.59 for BC; and from 0.15 to 0.26 for UFP. Median daily Fpex estimates ranged from 0.24 to 0.31 across seasons. Daily PM2.5 and UFP Finf and Fpex estimates were higher in summer than winter, although BC showed the opposite trend. Predictors of daily infiltration were typically related to window-opening behaviours, air conditioning, meteorological variables, and home age. In addition, use of electrostatic precipitators and stand alone air cleaners was associated with significantly reduced infiltration factors, indicating that these devices may provide a cost effective mechanism of reducing human exposures to particles of ambient origin.The majority of indoor PM2.5 (median 57–73%) and indoor BC (median 90–100%) was of ambient origin across seasons, while both personal PM2.5 and indoor UFPs had significant non-ambient contributions (median 60–65%). Factors that were found to increase non-ambient particle concentrations were typically related to cooking, candle use, supplemental heating, cleaning, and number of people in the home. Factors that were found to decrease non-ambient particle concentrations were open windows, and air cleaner use.This work has several implications to both epidemiologic studies and risk management. A better understanding of the factors influencing Finf and Fpex can improve exposure assessment and contribute to reduced exposure misclassification in epidemiologic studies. Furthermore, by increasing our knowledge of non-ambient and ambient exposures, risk associated with PM exposure can be managed more effectively.

Verification of stochastic models of window opening behaviour for residential buildings

Based on the analyses of data from two distinct measurement campaigns conducted in residential indoor environments in Japan and Switzerland, we identify the specificities of occupants’ behaviour with respect to their interactions with windows, including the choice of opening angles for axial openings. As a first step, each dataset is analysed to develop separate predictive models which account for the specificities of window usage in the residential context. The predictive accuracy of these models is then challenged by validation on external data: using models inferred from data obtained from one survey, actions on windows are simulated for the other survey and the predictions are compared with observations. Dynamic models developed using data from office buildings as well as previously published models are also compared using this verification procedure. In the case of the Swiss dataset, these analyses demonstrate the ability of carefully formulated behavioural models developed from office environment data to reliably predict window usage in a residential context and vice-versa. However, we observe that the same models perform less satisfactorily in the prediction of window usage in Japan. From these results it seems that such models require specific calibration in the case of buildings equipped with an air-conditioning unit as was the case for the hot and humid summer climate of Japan.

Factors influencing the occupants’ window opening behaviour in a naturally ventilated office building

Window opening behaviour has become of a specific concern when it comes to the adaptive comfort analysis and evaluation. A field study of occupants’ window opening behaviour was carried out from Jan/05 to Apr/06 in a naturally ventilated office building in Sheffield’s, UK climate conditions. Window positions were recorded daily on academic semester weekdays. A total of 1620 windows’ positions in 329 days (16 months) were recorded. Outdoor and indoor physical parameters such as air temperature, relative humidity, wind speed etc. were collected at the same time. The results show that manual window control, as indicated by the proportion of windows opened, has a strong correlation with: outdoor air temperature, the season of year, time of a day and occupancy pattern. Window orientation is also considered as a relevant influencing factor. Moreover, the study tests a stochastic model to predict the probability of windows being open given the outdoor temperature with promising results. Also insights are reported about behaviour in non-office spaces in the building as a whole.

Case study of window opening behavior using field measurement results

Window opening behavior has significant influences on indoor environment and energy consumption in residential buildings. As a response to indoor environment, the control mechanism (window and interior door open/closed) should be studied first by comprehensive understanding of the variation of indoor environmental conditions. For this reason, a field measurement of environmental conditions was carried out in five representative apartments in Beijing from April to May in 2010. By comparison analyses of the field measurement results, major findings are as follows: (1) the concentration of room CO2 can be the best predictor of occupant behavior, also window opening behavior, in residential buildings; (2) the variation in indoor air quality mainly results from large variation in window opening behavior; (3) apart from indoor and outdoor thermal environment, indoor air quality as well as occupants’ presence at room have also considerable effect on window opening behavior; (4) while defining window opening behavior for one room in residential building, it is necessary to take the window adjustment of its connecting room and the adjustment of the connecting door into consideration due to air diffusion between the two connecting rooms.