Behavior In Residential Buildings Research Articles

An interdisciplinary model for behaviour in residential buildings: Bridging social sciences and engineering approaches

In this paper, we develop a comprehensive behaviour model for residential buildings that considers the diversity among households. While existing behavioural theories from social and psychological sciences have been used in building research, they often lack technical detail, contextual aspects, and focus primarily on behavioural change. The authors propose an interdisciplinary theoretical model that integrates insights from behavioural science and engineering dimensions. This model aims to link measurable drivers directly to energy outcomes, consider building-related contexts, and reflect the complexity of high-performance buildings.The research consisted of the quantitative analysis of building monitoring data and the analysis of interviews using thematic analysis. The mix-methods approach allowed to obtain new insights into the relationship between the aspects that affect occupants' behaviour. An interdisciplinary model is developed based on the results from the analysis, existing theoretical models used in building research, and previous studies on occupants' behaviour. The model is intended to support the identification of occupants' behaviour drivers, inform user-centric and energy-efficient building design, enhance decision-making for building monitoring and simulations, and aid in various practical applications such as performance assessment and energy contracting.

Modelling occupant behaviour in residential buildings: A systematic literature review

This systematic literature review examines Occupant Behaviour (OB) modelling in residential buildings, focusing on its role in narrowing the energy performance gap and enhancing occupant comfort. It provides an overview of methodologies from simple statistical models to advanced machine learning algorithms. We conducted a chronological literature review on OB modelling in residential buildings, identifying the algorithms and the main remarks and limitations of each study. We also present sample populations for each study, offering a comprehensive view of OB modelling's evolution. Our study shows a trend towards incorporating intelligent technologies like IoT and Artificial Intelligence, highlighting an evolution towards more advanced and precise techniques. The review evaluates studies utilizing monitoring, diary, or survey data to improve behavioural model accuracy, covering aspects such as occupant presence, window manipulation, lighting and shading regulation, thermostat adjustment, and usage patterns of domestic hot water and appliances. The review underscores the importance of standardized data collection and integrating OB into building standards. It advocates for adaptive OB models leveraging IoT data with privacy considerations and calls for comparative analysis of modelling techniques. Future research should develop adaptable occupancy profile repositories and integrated modelling frameworks to address OB complexities.

Analysis and modeling of fresh air systems usage behavior in residential buildings in Beijing during heating season

Abstract Fresh air systems (FAS) represent an excellent solution for improving indoor air quality. Because FAS has a significant impact on buildings’ energy consumption, it is vital to investigate FAS usage behavior. At present, there is a research gap with respect to individualized FAS usage behavior and prediction models for residential buildings. From November 15th, 2018 to March 15th, 2019, this paper conducted a field measurement and modeling study of FAS usage behavior in thirteen Beijing households. During heating season, the FAS is used intermittently, with an average daily opening duration of 17.5 hours. Based on the “average daily opening duration”, two FAS usage behaviors were identified: economical households and conventional households. The average daily indoor particles matter 2.5 (PM2.5) concentration was observed to be lower in FAS-using households (16.19 μg/m3, 12.30 μg/m3) than in non-FAS-using households (46.1 μg/m3). Outdoor PM2.5 concentration is a motivating factor for FAS opening. The paper used binary logistic regression to predict the FAS status of both economical and conventional households (two classified models), as well as for all tested households (unclassified model). These results suggest that classified models (84.1%, 83.5%) have greater accuracy than the unclassified model (75.9%), opening the possibility of using the average daily opening duration to classify and examine FAS usage behavior. The study provides accurate field data for evaluating FAS usage behavior, as well as a reference for further simulating the energy consumption of FAS-using households.

A/C usage intensity and adaptive comfort behaviour in residential buildings in South East Queensland, Australia

Occupants’ interaction with building controls has been recognised as a major contributing factor to the often-reported wide discrepancies between actual and predicted energy consumption in buildings. Accordingly, research into actual patterns of occupant behaviour is increasingly being incorporated into building energy performance simulation tools. However, utilizing a single behaviour pattern can also lead to differences between the actual and predicted energy use in a building. This study aims to classify patterns of occupant adaptive comfort behaviours based on longitudinal field observations made in Australian homes. A sample of 41 households was classified into ‘light’, ‘medium’ and ‘heavy’ usage types according to their annual A/C use. Statistical models describing the proportion of a specific adaptive behaviour (incl. A/C and window operation) in relation to outdoor temperature were developed for the three household classifications. The results indicate that more than 30% of the ‘heavy’ usage households operated their A/C for cooling as the outdoor temperature exceeds 30℃, in contrast to 10% for the ‘medium’ and nearly none for the ‘light’ A/C usage groups. In general, the household A/C usage and hours of windows being open in living rooms were found to be inversely proportional. This study also proposes a probability model for classifying the A/C usage levels of a given dwelling into the three groups (i.e. light, medium and heavy) based on simple background information that can be acquired at the design stage (i.e. site and construction characteristics).

Priority Weight-Based Systems to Predict Energy Consumption Behavior in Residential Buildings

Priority Weight-Based Systems to Predict Energy Consumption Behavior in Residential Buildings

A systematic review of time user surveys-questionnaires and monitoring variables used to track occupant behaviour in residential buildings

In recent years, a growing body of literature has revealed that occupants and their interactions with the building systems and controls (window opening, thermostats, solar shading, etc.) have become a key factor influencing the building’s energy performance and their Indoor Environment Quality. Occupant behaviour can lead to major disparities between the predicted-simulated and the actual energy use in buildings, described by the term Energy Performance Gap, affecting possible strategies to reduce energy consumption. A potential solution to minimize the differences between the simulated and the real performance is by designing realistic models of occupants, which can be incorporated in a simulation code providing useful information for the design and/or assessment of new or existing buildings. Usually, occupant behaviour models are treated as static-deterministic schedules in building simulations, disregarding their stochastic and diverse nature. Accurate configuration of occupant models leads to fine-tuned simulations, enhancing the operation of building control systems. An occupant-centric building design results in building systems capable to meet occupants’ health and comfort requirements, minimizing at the same time the energy use. Although a number of techniques have been used to model occupancy behaviour, such as probabilistic and statistical models, in the present work, we systematically review the most recent techniques based on questionnaires and monitoring variables. These techniques are quite common and their application could help to better understand what drives occupants to interact with a building and its systems, making possible the design of more robust and realistic occupant models.

A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.

Window Opening Behavior of Residential Buildings during the Transitional Season in China’s Xi’an

Window opening behavior in residential buildings has important theoretical significance and practical value for improving energy conservation, indoor thermal comfort, and indoor air quality. Climate and cultural differences may lead to different window opening behavior by residents. Currently, research on residential window opening behavior in northwest China has focused on indoor air quality, and few probabilistic models of residential window behaviors have been established. Therefore, in this study, we focused on an analysis of factors influencing window opening behavior and the establishment of a predictive model for window opening behavior. Four typical residential buildings in different locations and building types in Xi’an were selected. The indoor and outdoor environments and window opening states were measured. Subsequently, a multivariate analysis of variance was used to determine the factors that had a significant effect on window opening behavior. Single‐ and multiparameter logistic regression models for window opening behavior were established. Of all the measured factors, we found that indoor temperature and CO2 concentration, outdoor temperature, and relative humidity had significant effects on window opening behavior, and indoor relative humidity and noise did not. Meanwhile, the temperature was positively correlated with the window opening probability, whereas indoor CO2 concentration and outdoor relative humidity were negatively correlated. The prediction accuracy of the multiparameter model was promising, at almost 75%, and the model can provide theoretical support for modelling residential buildings in Xi’an.

The window opening behavior of infant families: A case study during transition season in the cold region of China

Window opening behavior is a common and effective way for occupants to improve indoor thermal comfort and air quality. There are many studies on the window opening behavior in residential buildings, but research on infant families is still lacking. This paper examines the window opening behavior of four infant families in Beijing and Tianjin that are located in a cold region of China during the transition season. The Kendall Tau coefficient is used to measure the correlation between behavior and environmental factors for the first time. Two types of windows opening behavior were found: i) related to environmental factors and ii) independent of environmental factors. The study reveals that the window opening probability in the living room of infant families is 70.6%, and in the bedroom is 57.2%, which is obviously higher than the existing research on ordinary residents (24.3 ∼ 43.2%). The window opening probability of infant families shows a more obvious downward trend with increasing outdoor PM2.5 concentration compared to ordinary families. Non-environmental factors, such as time of day and age of infant, also affect behavior. Based on the proposed concept of indoor CO2 driving strength (DSCO2_In), it was concluded that the variation of the indoor CO2 concentration is the result of the window opening behavior rather than a driving factor. The logistic regression model shows that outdoor temperature and PM2.5 concentration are the most important factors. This study can help us gain a thorough understanding of the window opening behavior in infant families and it can also provide data and theoretical support for of the simulation of energy consumption in infant families.

Habitual window opening behaviour in residential buildings

Recent studies indicated that window opening behaviour is related to environmental and contextual factors, as well as to practices of everyday life, such as morning routines and daily activities. To the authors’ knowledge no studies have been conducted that focus on the habitual window opening behaviour in residential buildings. The aim of this study is to determine the extent of the habitual behaviour and to identify the types of window use habits. Additionally, a window use model is created that includes habitual behaviour. Results of the online survey conducted with Belgian households revealed that almost all occupants habitually interact with at least one window. Some household and building characteristics are related to the presence of certain habits. However, this impact is rather small in comparison to the impact of the habits of other rooms. These insights are utilized for the creation of a window use habit model. The model is explained in detail as well as the validation procedure. While further research is necessary regarding the cultural and seasonal dependency of the window use habits, this model is promising as it links the window use to the everyday life, resulting in the prediction of realistic and justifiable window use.

Analysis and modeling of air conditioner usage behavior in residential buildings using monitoring data during hot and humid season

With split air conditioner (AC) becoming one of the most popular thermal comfort-related appliances in residential buildings in China, many issues, such as high energy consumption, have arisen. One crucial way to address these issues is to study the usage of split AC models, which can be greatly affected by occupant behavior. In this study, we conducted a large-scale field measurement among 34 residential buildings in eight cities distributed in three climate zones using wireless sensors during summer. The AC usage patterns in the three climate zones were analyzed, and models based on artificial neural networks and gradient boosting decision tree algorithms were developed for predicting AC on/off behavior. The results showed that the average usage duration and energy consumption of AC per day in the three climate zones were 7–10 h and 2.78–3.00 kWh, respectively, and that there was a large difference among different households. The AC usage patterns of households in the three climate zones had certain similarities. The models developed in this study showed higher performance and accuracy than the logistic regression-based model, and indoor relative humidity and CO2 concentration were found to significantly improve predicting accuracy. The models can generate an AC operation schedule as an input boundary condition to improve the accuracy of residential building energy consumption simulations in future studies.

Examining the attitude-behavior gap in residential energy use: Empirical evidence from a large-scale survey in Beijing, China

Occupant behavior in residential buildings has great energy-saving potential and offers an important opportunity for realizing the targets of energy conservation and carbon emission reduction. Traditionally, it is believed that occupant behavior aligns with people’s energy-related attitude. However, an attitude-behavior gap has been observed in many recent studies, which rarely focused on specific attitudes and behaviors in urban residential buildings. Therefore, based on a large-scale empirical survey (N = 1003) in Beijing, China, we explore the relationship between energy-related attitudes and behaviors. Specifically, purchase and habitual behaviors are investigated along with relevant attitudes, including the willingness to pay (WTP) more for energy efficient products, whether paying attention (WPA) to energy efficiency parameters/indicators of products when making a purchase, and the willingness to change behavior (WTC) for further residential energy reduction. The results show a mixed picture. There is a consistency between the occupants’ WPA and purchase behavior. In contrast, the occupants generally don’t behave consistently with their reported WTP and WTC, suggesting the existence of attitude-behavior gaps. Different reasons may lead to these gaps, demonstrating the necessity of a more comprehensive and flexible strategy for energy policymaking. Furthermore, a closer look at the factors underlying different attitudes shows the significant effects that perceived building thermal insulation performance and past experiences of changing behavior may have, suggesting the effectiveness and importance of building thermal retrofits and more targeted awareness campaigns. A self-reinforcing feedback loop may exist between attitude and behavior, demonstrating the great potential of social interactions and peer influence for behavioral change.

Extracting energy-related knowledge from mining occupants’ behavioral data in residential buildings

In this study, a data mining-based framework is developed to provide practical insight into occupants' energy behavior in residential buildings. Occupant Activity Indicator (OAI) and Residential Energy Intensity Indicator (REII) are introduced as two new indicators. The proposed data-mining framework aims to evaluate the energy-related behavior of the buildings' residents based on the difference between the target REII and actual REII. The dissimilarity, which is found between the target and the actual REII, is used to identify when occupants behave wasteful/efficient in different zones of the building. The practicality of the proposed methodology is demonstrated by applying it to a three-bedroom apartment. As a result, the time and zones for potential energy savings in the considered building are identified. The extracted knowledge identifies the zones that occupants need to be more cautious about their energy consumption. Besides, location and time for the improvement in energy-related behavior of the occupants are defined. Further, the variations of occupants' energy-related behavior in the apartment are identified by the time of day, day of the week, and months.

Using pattern recognition to characterise heating behaviour in residential buildings

ABSTRACT The understanding of energy-related occupant behaviour and its better reproduction in building energy analysis has recently become a primary field of interest. The combination of computing ease and the availability of big streams of high resolution building performance data enhance the ability to study this behaviour. In this context, data mining methods are increasingly employed. The aim of this study is to propose a data mining methodology for the characterization of heating behaviour in residential buildings. The methodology consists of two multivariate statistical analysis methods, namely Principal Component Analysis (PCA) followed by cluster analysis. The methods were applied on monitored gas consumption data of five dwellings in Italy. Findings support literature indicating that people heat their homes in different ways. It was found that households do not always follow a different heating schedule on weekends, have very different temperature preferences and operate the heating system at different hours during the day. In fact, some households may change heating practices over the heating season. The highlight of the proposed methodology is the insightful and simple way that PCA can extract succinct information about the heating behaviour of the user.

A holistic investigation into the seasonal and temporal variations of window opening behavior in residential buildings in Chongqing, China

Window operation behavior has been recognized as one of the pivotal factors affecting building energy efficiency and indoor environments. Such behavior could vary from one region to another. A holistic understanding of the temporal and seasonal variations in the operation of residential windows in the Yangtze River region remains insufficiently explored. This paper presents a multi-scale approach by combining cross-sectional surveys, photographic image observations, and longitudinal onsite measurements to investigate the window opening characteristics of residents. The advantage is being able to perform qualitative and quantitative analyses systematically upon the completion of each step. The methodology has been applied to a case study in Chongqing, China. A year-long study revealed the window operation habits of residents in this region who rely greatly on windows for natural ventilation in transient seasons and for fresh air in summer/winter, especially when heating and cooling systems are in operation. Logistic models were developed and differentiated by seasons: transient, winter, summer with natural ventilation, and summer with air conditioning. The models quantified the driving factors and their corresponding variations affecting window opening probabilities in different periods of a year. It can be integrated into building simulations with realistic profiles for more accurate prediction of energy consumption in buildings. The proposed research methodology is applicable to other regions. The gained knowledge of residents’ window operation characteristics in this region provides evidence for window operations and building designs/retrofit strategies.

Factors Driving Consumer Involvement in Energy Consumption and Energy-Efficient Purchasing Behavior: Evidence from Korean Residential Buildings

The recent rapid transition in energy markets and technological advances in demand-side interventions has renewed attention on consumer behavior. A rich literature on potential factors affecting residential energy use or green technology adoption has highlighted the need to better understand the fundamental causes of consumer heterogeneity in buildings’ energy-related behavior. Unresolved questions such as which consumers are most likely to opt into demand-side management programs and what factors might explain the wide variation in behavioral responses to such programs make it difficult for policy-makers to develop cost-effective energy efficiency or demand response programs for residential buildings. This study extends the literature on involvement theory and energy-related behavior by proposing a holistic construct of household energy involvement (HEI) to represent consumers’ personal level of interest in energy services. Based on a survey of 5487 Korean households, it finds that HEI has a stronger association with consumer values, such as preferences for indoor thermal comfort and automation, than with socioeconomic or housing characteristics and demonstrates HEI’s potential as a reliable, integrated predictor of both energy consumption and energy-efficient purchases. The study illuminates the multifaceted influences that shape energy-related behavior in residential buildings and offers new tools to help utility regulators identify and profile viable market segments, improve the cost-effectiveness of their programs, and eventually promote urban sustainability.

Occupant-based energy upgrades selection for Canadian residential buildings based on field energy data and calibrated simulations

Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings’ carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles. This framework has been demonstrated through a case study of two single-family detached homes in Canada, which were highly instrumented with sensors for monitoring energy input and output. The case studies represented two common occupancy profiles-(1) a family of four (consisting of 2 working adults and 2 teenagers); and (2) a retired couple. Firstly, calibrated energy models were developed by using one-year energy use data collected through an intrusive load monitoring technique. Secondly, energy upgrade combinations were considered for each profile and tested for additional investment, payback period and greenhouse gas (GHG) emissions. Lastly, the most suitable combination of energy upgrade for each profile was ranked using a multi-criteria decision-making method (e.g., TOPSIS). Results indicated that the retired couple used more energy than the family of four and required energy upgrades with usually higher payback periods to achieve the same level of GHG emission reduction. The results of this research are timely for energy policymaking and developing best management practices, which need to be implemented along with the deployment of more stringent building standards and codes.

Appliance use behavior modelling and evaluation in residential buildings: A case study of television energy use

Aside from the consumption for domestic cooling and heating, the energy consumption of appliances in a household is a large amount. This energy use by appliances is highly dependent on the activities of the occupants in a building. Occupant behavior in residential buildings possesses the characteristic of randomness, variety, and complexity. More specifically, the profiles of energy consumption and appliance use are highly dependent on the timing of residents’ activities. Thus, it is necessary to model the domestic energy use profile with high temporal resolution. For example, in the context of energy demand response analysis, it is crucial to take occupant behavior into consideration. This article presents a thorough and detailed method for generating an appliance use pattern based on measured power data in real households, taking the television as a case study. The study develops a stochastic model based on appliance use data at one-minute temporal resolution. The proposed model firstly extracts feature parameters to depict the use behavior of the appliance. Then, one stochastic simulation model is established with the input of feature parameters. An evaluation method for this appliance behavior model is also proposed. As a result of one case study simulating appliance consumption at a variety of scales of households, it was concluded that the model can enable applicability to further household energy profile simulation.

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.

Characterizing energy-related occupant behavior in residential buildings: Evidence from a survey in Beijing, China

Despite progress in energy efficiency technologies and energy intensity reduction, the buildings sector's final energy consumption and associated CO2 emissions both continue to grow, pointing to the necessity of understanding real occupant behavior. This study, therefore, explores occupants' energy-related behavior in residential buildings with empirical data from a large-scale survey in Beijing, China, aiming to present an in-depth and comprehensive picture of occupant behavior. We obtained a total of 1003 valid responses to an online questionnaire concentrating on occupant behaviors with respect to space heating and cooling, water heating, cooking, lighting, appliances and other equipment. Our results show that, typically, purchase behavior is overall energy-efficient, as most appliances purchased are energy efficient. For both purchase and usage behaviors, air conditioning (AC), which is used for both space heating and cooling, plays a critical role as residents usually have less energy efficient AC in their homes and use it more often and for a longer period of time than other devices. Their habitual reactions are consistent across all habitual categories, but show no significant correlation with their purchase behavior. There is no single, straightforward and coherent pattern that can be explained by any single socio-demographic factor. These findings offer a behavioral insight into efforts to improve residential energy efficiency at the city scale. For instance, more policy attention should be directed to occupants’ purchase and usage behavior of ACs, such as by promoting the energy rating program in a more effective way. Moreover, given the lack of apparent linkage between purchase and habitual behaviors, a comprehensive policy framework incorporating different measures for different types of behavior is necessary. The complexity underlying behavioral choices also suggests the necessity of better understanding the behavioral patterns and their determining factors, as well as avoiding any simplistic assumption in policy making that aims to take behavioral factors into account.