Window Opening Behavior Research Articles

Research on occupants’ window opening behavior in residential buildings based on the survival model

Natural ventilation can reduce a building’s energy consumption while improving indoor thermal comfort and air quality. However, window opening behavior is prominently random and highly dependent on meteorological conditions. It is of considerable significance to explore indicators influencing window opening behavior and establish a model to predict the window opening probability. In this study, four environmental indicators significantly influencing window opening behavior (indoor air temperature, the concentration of CO2, outdoor air temperature, and the concentration of PM2.5) were screened out from the measurement. With the selected indicators as input variables, the Cox model for survival analysis was established. For comparison, the logistic model was also established, coupled with the Cox model, to simulate the window opening probability under the interaction of environmental indicators. According to the comparison results, although slightly less accurate than the existing logistic model in predicting the window opening probability throughout whole survival lifetime, the accuracy of the Cox model was 1.3% more than that of the logistic model, which will provide a foundation for further revision of the human behavior model.

Investigation of window-opening behaviour and indoor air quality in dwellings situated in the temperate zone in China

Indoor air quality (IAQ) is a key factor that could affect the indoor environment and people's health. In this study, we report a comprehensive study on IAQ in 30 dwellings in the temperate zone in China. Thirty naturally ventilated dwellings were selected in the urban area of Kunming, which is the largest city in temperate zone in China. In four seasons, short-term measurement of formaldehyde and benzene, toluene, xylenes-volatile organic compounds and air infiltration rate were conducted in these dwellings. A long-term 1-year monitoring of temperature, relative humidity, fine particulate matter (PM2.5), carbon dioxide (CO2), window-opening behaviour and night-time air change rate (ACR) were conducted in 13 of these dwellings by remote sensors. Short-term measurements showed the mean formaldehyde concentration in summer (100.76 μg/m3), benzene concentration in winter (8.46 µg/m3), PM2.5 concentration (80.4 µg/m3) in spring of 2017 exceeded the Chinese standard, the US Office of Environmental Health Hazard Assessment value and the World Health Organization guidelines. The night-time ACR was the highest in summer (2.1 h−1) and the lowest in winter (1.6 h−1) in bedrooms of 13 long-term monitored dwellings due to seasonal time variation in window opening in bedrooms, which was mainly driven by CO2 concentration, indoor temperature and outdoor PM10 concentration.

Verification of behavioural models of window opening: The accuracy of window-use pattern, indoor temperature and indoor PM2.5 concentration prediction

Occupant-controlled window opening plays a significant role in exposure to indoor environmental hazards such as high temperatures and PM2.5. Both the indoor and outdoor environments can affect window opening behaviour of building occupants, while variation in this behaviour is often poorly defined in building simulations. This study examines the robustness of six existing behavioural models of window opening in predicting window-use patterns in Hong Kong flats, and the impact of behavioural models on the accuracy of indoor temperature and indoor PM2.5 concentration prediction. The behavioural models were implemented in the EMS application of EnergyPlus. The predicted window-use patterns were compared to those of filed measurements, whilst the predicted indoor temperatures and indoor PM2.5 concentrations were benchmarked against those predicted by the EnergyPlus model with the actual occupant window opening behaviour. Results indicate that the ability of individual behavioural models to accurately predict occupant window opening behaviour varies depending on modelling approach, outdoor environmental variable and simulation time-step size. The model providing the best estimate of the duration of window-opening is able to best contribute to accurate predictions about indoor temperatures and indoor PM2.5 concentrations. Results also show that a static temperature threshold for window-opening (a common method of modelling occupant window opening behaviour) can lead to a significant difference between the actual and predicted window-use patterns (and therefore indoor temperatures or indoor PM2.5 concentrations), underlining the importance of improving model inputs for occupant window opening behaviour.

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control.

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R2 of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.

Investigation of thermal comfort and behavioral adjustments of older people in residential environments in Beijing

Aged population is growing rapidly and aging in place is the major endowment pattern for older people. This study focused on the thermal living environments and thermal comfort of older people in their own homes. Special attention was paid to their behavioral adjustment characteristics. The longitudinal investigation was conducted from March 2018 to March 2019 in a residential community in Beijing, China, and the participants involved 5 households with 10 older respondents. The investigation included a yearlong continuous environmental measurement, behavioral monitoring, and a monthly collection of subjective evaluations. Face-to-face interviews on behavioral adjustments were also conducted with 102 older respondents in different communities in Beijing. The field investigation involved the observation of seasonal variation in the indoor environments and subjective evaluations. The prediction accuracy of the predicted mean vote (PMV) model was found to be inconsistent for thermal sensations of older people between the cold side and hot side. With regard to the behavioral adjustments of older people, this study described the effect of the outdoor temperature on window opening behavior and air conditioner use, and the effect of indoor temperature on clothing behavior. The preference for the adjustment methods were studied, and the personal characteristics of older people (including sex, age, educational background, and time spent at home) were found to influence their preference for different methods. The differences in behavioral adjustments between older and non-older people and between older people in different climate zones were discussed by comparing this study to previous field studies.

Probabilistic window opening model considering occupant behavior diversity: A data-driven case study of Canadian residential buildings

It was found from monitored data from eight dwellings in a case study building in Quebec City (Canada) that there are clear differences in the window opening behavior between different households. This paper aims to develop from data a probabilistic window opening model that accounts for occupant behavior. Logit regression is employed to predict the state (opened/closed) of windows according to indoor and outdoor temperatures environmental and temporal parameters. To replicate the diversity of behavior, normal distribution functions applied to the logit regression coefficients are used so that simulated occupants respond differently to environmental stimuli. It was found that the model offers good prediction for the monitoring by only using the outdoor and indoor temperatures as predictors. The proposed methodology was tested by simulating 10,000 times a full validation year of the case study building and comparing the results with measured data. The agreement was good. The model overestimated slightly the total frequency of window opening in the dwellings and the number of window changes-of-state. A vast range of window opening behavior was generated by the model, showing its ability to reproduce both the aggregated window opening behavior and the diversity of behaviors of the case study building.

Effects of household features on residential window opening behaviors: A multilevel logistic regression study

Window opening behavior is an important factor influencing building performance, including building energy consumption, indoor thermal environment and indoor air quality. Traditionally utilized linear logistic regression model, which describes the correlations between the window opening probability and the environmental driven factors, fails to include the variety among different residences. This study utilized multilevel logistic regression model to analyze the effect of household features on the residential probability of window opening. Here the environmental factors were set as lower-level predictors while the household features were set as higher-level predictors. The studied household features include the area, the located floor, the home renovation condition and the special type of residents (i.e., smoker and old people). The analysis was conducted on a measured dataset monitored in 10 residential apartments in Nanjing. According to the results, 84% of the difference in the probability of window opening was attributable to the residence-level variety, which cannot be ignored. Although the higher-level household features show no direct effect on the window opening probability, all of them significantly affect the correlations between the probability of window opening and the lower-level environmental predictors. Together with future field measurements of representative households, this research will be a crucial basis to build up the multilevel structure of predictors for the residential window opening behaviors, which will further make an improvement to represent the residence-level variety in building performance simulations.

Radon exposure is rising steadily within the modern North American residential environment, and is increasingly uniform across seasons

Human-made buildings can artificially concentrate radioactive radon gas of geologic origin, exposing occupants to harmful alpha particle radiation emissions that damage DNA and increase lung cancer risk. We examined how North American residential radon exposure varies by modern environmental design, occupant behaviour and season. 11,727 residential buildings were radon-tested using multiple approaches coupled to geologic, geographic, architectural, seasonal and behavioural data with quality controls. Regional residences contained 108 Bq/m3 geometric mean radon (min < 15 Bq/m3; max 7,199 Bq/m3), with 17.8% ≥ 200 Bq/m3. Pairwise analysis reveals that short term radon tests, despite wide usage, display limited value for establishing dosimetry, with precision being strongly influenced by time of year. Regression analyses indicates that the modern North American Prairie residential environment displays exceptionally high and worsening radon exposure, with more recent construction year, greater square footage, fewer storeys, greater ceiling height, and reduced window opening behaviour all associated with increased radon. Remarkably, multiple test approaches reveal minimal winter-to-summer radon variation in almost half of properties, with the remainder having either higher winter or higher summer radon. This challenges the utility of seasonal correction values for establishing dosimetry in risk estimations, and suggests that radon-attributable cancers are being underestimated.

Effects of building parameters on occupant’s window opening behaviour

Nowadays a large portion of energy consumption is depends on Residential Building. For these reasons Mechanical ventilation, colling, heating such things also be improved. But Occupants take action to control their indoor environments to adjust their individual flavour. Window Opening & closing If this result is universally applicable most preferred behaviour to adjust their indoor environment. However these behaviour causes an increase in energy consumption in the building. Therefore, if the window opening and closing behavior can be predicted, it will lead to an increase the comfort in the building and a decrease in the energy consumption. Previous studies have confirmed that window opening and closing behavior is significantly affected by ambient temperature. This also indicates that the season also affects window opening. It can be seen that the machine learning algorithm has better prediction than the traditional regression model. The purpose of this study is to investigate the effects of building information (location, number of floors, area, completion year) and human characteristic for indoor environmental control(heating setting temperature), not on the environmental parameters(temperature, humidity, fine dust, etc.) applied to previous machine learning models in the prediction of window opening and closing behavior. If this result is normally applicable, it will make more realistic predictions possible than when predicting with existing environmental parameters alone. This will give a plausibility to the results of simulations on actual buildings.

Non-intrusive measurement method for the window opening behavior

The occupant windows opening behavior has a great impact on indoor air quality and building energy consumption. Therefore, measuring the window opening behavior and factors that affect it are important for the occupant behavior modeling and architectural design. In this study, we proposed non-intrusive measurement methods which can achieve large-scale sampling for the window state. An image recognition code based on MATLAB was used to conduct projective transform of the building elevation maps, identify the window positions and determine their opening proportions. The method can recognize most of window open states with the error of 8%. Based on this method, the window opening states for a hospital building from August to December in 2018 (about 6000 samples) was collected. Then the significance of the influencing factors and window opening distributions under different factors were analyzed. The results showed the outdoor temperature had the most significant effect, and the frequency of the window opening proportion in the range of 0.3–0.5 within 20–30 °C is significantly higher than other temperature ranges. This large-scale sampling method proposed in this paper provided a powerful tool for building modeling and energy analysis.

Investigation and Evaluation of Winter Indoor Air Quality of Primary Schools in Severe Cold Weather Areas of China

The indoor air quality (IAQ) in classrooms has attracted more and more attention. Unfortunately, there is limited information relating to IAQ in the primary schools in severe cold weather areas of China. In this study, a field investigation on the IAQ of a primary school of Shenyang in northeast China was carried out by physical measurements and questionnaire surveys. The carbon dioxide (CO2) concentration in selected classrooms was continuously measured for a week, and the corresponding ventilation rate was calculated. Meanwhile, the perceptions of the IAQ, the purpose and the comfort degree of window opening have also been recorded from 106 pupils, aged 9–12. The results indicate the ventilation rate is considerably inadequate in about 99% of the class time due to the low frequency of window opening. The average daily CO2 concentration in these classrooms is 1510–3863 ppm, which is far higher than the recommended value of 1000 ppm. Most pupils understand that the purpose of opening windows in winter is to improve air quality. However, there are big differences between the measurement results and subjective judgments of indoor air quality. Contrary to the high measured CO2 concentration, around 70% pupils consider the air fresh, and only 3.7% pupils are dissatisfied and even very dissatisfied with IAQ in their classroom. It is necessary to change the existing manual window opening mode, because the pupils’ subjective judgment affects the window opening behavior.

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

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.

Critical review and illustrative examples of office occupant modelling formalisms

It is widely understood that occupants can have a significant impact on building performance. Accordingly, the field has benefited extensively from research efforts in the past decade. However, the methods and terminology involved in modelling occupants in buildings remains fragmented across a large number of studies. This fragmentation represents a major obstacle to those who intend to join in this research endeavor as well as for the convergence and standardization of methods. To address this issue, this paper investigates occupant modelling methods for the key domains of electric lighting, blinds, operable windows, thermostats, plug loads, and occupancy. In the reviewed literature, five broad categories of occupant model formalisms were identified: schedules, Bernoulli models, discrete-time Markov models, discrete-event Markov models, and survival models. Illustrative examples were provided from two independent datasets to demonstrate the strengths and weaknesses of these model forms. It was shown that Markov models are suitable to represent occupants' adaptive behaviors, while survival models are suitable to represent occupancy, non-adaptive behaviors, and infrequently executed adaptive behaviors, such as the blinds opening behavior. Practical application: The engineering application of the occupant modelling formalisms that are critically reviewed in this paper is that these models are highly beneficial for incorporating occupants' presence and behaviors into building design and control. Building design can be improved significantly regarding energy use and occupant comfort when the most suitable occupant models are implemented in simulation-aided building design process. Ultimately, like for any modelling domain, the most suitable model is dependent on the modelling objective (e.g. optimizing passive design, equipment sizing), building type and size, occupant-related domain (e.g. occupancy, window-opening behavior), and climate zones. Furthermore, there is great potential in improving occupant comfort and energy savings of existing buildings when occupants' presence and interactions with buildings' systems and components are predicted accurately using occupant models.

Formula omitted] concentrations in a multifamily building in Porto, Portugal: Occupants' exposure and differential performance of mechanical ventilation control strategies

Adequate supply of fresh outdoor air is important to create healthy and comfortable indoor building environments. Currently, most of the residential buildings in mild climate European countries, such as Portugal or Spain, remain naturally ventilated. Distinct concerns have emerged in the building community regarding the indoor air quality (IAQ) in these buildings since several studies have shown that they are often poorly ventilated. The main cause has been identified as occupants' different window opening behavior, which can vary notably from one apartment to the next. This work presents a comparative method for evaluating occupants' exposure to CO2 concentration levels in existing buildings based on the comparison of cumulative frequency distribution curves. Indoor CO2 concentration levels recorded over 17 months in the bedrooms and living rooms of eight apartments of a naturally ventilated residential building located in Porto (Portugal) were used as experimental input data for developing the method and to calibrate the IAQ performance of an EnergyPlus building model. The calibrated building model exhibited a good agreement with the recorded data on the apartments, reporting maximum Root Mean Square Errors (RMSE) for the majority of the apartments below 5% and 10% for the living rooms and bedrooms, respectively.

Inclusion of window opening habits in a window model based on activity and occupancy patterns

The occupants’ window opening behaviour can have a substantial influence on the indoor climate and the energy use in low energy dwellings. In literature, most window opening models are based on outdoor and/or indoor climate variables. However a study of Verbruggen et al. [1] revealed that these models are not able to predict the window opening behaviour accurately in wintertime, which may be attributed to the presence of window opening habits. The occupants perform the habits not according to a fixed time step but rather to the performance of a reoccurring activity or an occupancy change. Therefore, a window opening model is generated based on the occupancy and activity patterns of the inhabitants. The model links certain behaviours to specific activities or moments in an occupant’s day without relating it to an exact time-step or specific weather conditions. Data on these habits and the links with occupancy are acquired from a survey conducted in a NZEB case-study project in Belgium. This paper includes the results of the habit-survey and explains how the window use model based on habits is generated. Based on the answers from the survey the window use in bedrooms and bathrooms could be fully defined for 93% of the households, only in the living room no complete window use profile could be defined. The developed model is able to predict the window use in a more realistic way compared to weather-models, with window opening actions linked to specific moments in the occupant’s day.

Thermal comfort, occupant control behaviour and performance gap – A study of office buildings in north-east China using data mining

Simulation techniques have been increasingly applied to building performance evaluation and building environmental design. However, uncertain and random factors, such as occupant behaviour, can generate a performance gap between the results from computer simulations and real buildings. This study involved a longitudinal questionnaire survey conducted for one year, along with a continuous recording of environmental parameters and behaviour state changes, in ten offices located in the severe cold region of north-east China. The offices varied from private rooms to open-plan spaces. The thermal comfort experiences of the office workers and their environmental control behaviours were tracked and analysed during summer and winter seasons. The interaction of the thermal comfort experiences of the occupants and behaviour changes were analysed, and window-opening behaviour patterns were defined by applying data mining techniques. The results also generated window-opening behaviour working profiles to link to building performance simulation software. The aim was to apply these profiles to further study the discrepancies between simulation and monitored results that arise from real-world occupant behaviour patterns.

Estimation of PM2.5 infiltration factors and personal exposure factors in two megacities, China

This study estimates infiltration factors (Finf) and ambient personal exposure factors (Fpex) for fine particulate matter (PM2.5) in two Chinese megacities, and constructs predictive models to explore their determinants. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days from 33 residences (of retired adults) in Nanjing (NJ) and Beijing (BJ), China, in both the non-heating season (NHS) and the heating season (HS). Elemental sulfur in filter deposits was determined by energy-dispersive X-ray fluorescence for PM2.5Finf and Fpex estimations. Season-specific models developed by stepwise multiple linear regression were evaluated using R2 and root mean square error (RMSE). The median [interquartile range (IQR)] of Finf varied from 0.76 (0.15) in the HS to 0.93 (0.11) in the NHS in NJ; and from 0.67 (0.16) to 0.86 (0.12) in BJ. Similarly, Fpex was significantly higher during the NHS [NJ: 0.95 (0.07); BJ: 0.89 (0.14)] than during the HS [NJ: 0.76 (0.17); BJ: 0.67 (0.11); p < 0.0001]. Common predictors of Finf and Fpex included window opening behaviors, meteorological variables, and building age. Moreover, air conditioning and distance to the nearest major road had an influence on Finf, while predictors of Fpex were more related to human behavior and activity (e.g., time spent outdoors and transportation). The models accounted for 35.4%–68.1% (RMSE: 0.065–0.101) and 41.6%–77.0% (RMSE: 0.033–0.103) of the variance in Finf and Fpex, respectively. By indicating the determinants of Finf and Fpex, these models can improve ambient PM2.5 exposure assessment and reduce exposure misclassification.

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

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.

Preliminary study on heating energy consumption distribution of dwellings in hot summer and cold winter climate region of China

Investigating the total heating energy consumption over the entire heating season and its distribution data can help policymakers to adequately understand the current status and development trend of heating energy consumption, as well as to formulate effective policies to improve the indoor comfort and energy performance of residential buildings. This paper aims to explore a method for obtaining the space heating energy consumption demand of dwellings to guide energy conservation and energy supply with heating. A survey questionnaire was carried out to obtain detailed information on the occupant behaviour on space heating in a multi-unit high-rise residential community in Nantong. The statistical results of some factors were obtained from the questionnaire, such as occupants’ space heating behaviour, window-opening behaviour, and indoor temperature setting. Then, energy consumption by space heating was simulated and analysed based on the survey data, and the reliability of results was verified by comparing modelled results with measured energy consumption from 1625 households. The results reveal that the heating energy consumption simulation method based on occupant behaviour survey can be used to predict the distribution of the energy consumption over the entire heating season in this region and areas with similar climate.

Development of a multimedia model (IIAQ-CC) to assess climate change influences on volatile and semi-volatile organic compounds in indoor environments

An indoor air quality model (IIAQ-CC) was developed to assess climate change (CC) influences on indoor level of volatile or semi-volatile organic compounds. With IIAQ-CC, the CC influences is predicted in integrated manners by factors including fate/transport properties of pollutants, building characteristics, occupant behaviors, indoor emission sources, and outdoor air quality. IIAQ-CC was evaluated for the temperature sensitivity of indoor formaldehyde (FA) concentration at outdoor temperature below 20 °C. No significant difference (t-test, p < 0.05) was found between the predicted (3.9 ± 1.0%/oC) and the measured (5.0%/oC with a 95% confidence interval (3.7%/oC to 6.4%/oC)), demonstrating that IIAQ-CC can successfully predict CC influences driven by temperature change on VOCs of indoor environmental concern. IIAQ-CC predicts that the long-term FA concentration may slowly rise due to increase in the outdoor concentration or may decrease in the presence of strong indoor sources in RCP8.5 conditions of South Korea. In either case, the long-term change does not exceed 12% of the average FA concentration of the base period. Over a shorter period (e.g., a month), the FA level may noticeably rise (by about 4 times) when windows are closed due to exceedingly hot or cold weather. The FA concentration change is primarily governed by duration of windows kept open, which depends on outdoor temperature. That is, temperature manifests its effects on the indoor air quality essentially by change in window opening behavior rather than by changes in kinetics or equilibrium processes such as photo-degradation and sorption/desorption onto/from walls and floor.