Sustainable Human–Building Ecosystems

Buildings represent a major class of interventions by people in the environment, resulting in resource depletion, environmental emissions, and waste generation. To date, building-related interventions, together with those involving industry and transportation, have comprised the planet’s capacity to support the sustenance of future generations. This circumstance has raised the awareness regarding the importance of sustainability in the building sector. However, as in many other environmentally relevant domains, the theory (how the built environment should be) and practice (how the built environment actually is) have not converged. If we are to take the idea of sustainable buildings seriously, we need to approach it critically in the context of the complex and consequential relationships involving people, buildings, and environment. Why do we erect buildings? How can we measure their effectiveness in meeting people's requirements? How can we assess their ecological implications?

Many high-performance building strategies, such as daylighting and natural ventilation, offer a wealth of potential benefits to both occupants and owners (i.e., increased productivity, satisfaction, cost savings, etc.). However, these benefits may be compromised by patterns of occupation, especially if the end-users do not understand how to interact with buildings as intended. Furthermore, social influences and resulting occupant behaviors may further complicate energy efficiency goals. A sequential mixed-methods study was conducted to better understand occupant behaviors, environmental satisfaction, and learning in high-performance buildings. Results indicated a significant difference between groups; individuals who had reported effective training were significantly more likely to be satisfied with their office environment (Day and Gunderson, 2014). Additional qualitative findings offer further insight into occupant behaviors and interactions. The primary objective of this paper is to identify motivators and characteristics of occupant behaviors, as they relate to building energy use.

Research on delivering high quality energy-related information on users’ activities and consumption rates signify the effectiveness of such information for inspiring and motivating users to change their behavior towards more energy saving ones. However, the issue of making these behavior changes durable and integrated to one’s lifestyle is still remaining a topic for further investigation. This paper attempts to encourage new ways of thinking about users’ engagement in the energy management system of their community-based microgrid by combining computational means of feedback delivery with an incentive program, which requires users’ self-organized collaboration and participation in the shared-energy community endeavor.

As buildings become more efficient and automated, occupants’ role become more significant to help buildings reach their full energy efficiency potential. There are several approaches to promote occupant energy efficiency in buildings such as education, interventions, and eco-feedback programs. Eco-feedback systems have proven to be more effective than other methods in establishing energy efficient behavior among peers. Despite the effectiveness of eco-feedback systems, a question still remains how best to target specific occupants. In this paper, we develop new metrics to analyze occupant behavior and perform hypothesis tests in an eco-feedback study conducted in Denver, CO with more than 100 occupants. The results demonstrate an inconsistent response to eco-feedback programs. Therefore, we suggest a need for further research to investigate the possibility and potential of targeted and personalized eco-feedback programs which guide, motivate, and engage the occupants based on their behaviors, characteristics, and constraints.

Occupancy related energy-use behavior has a significant influence on building energy consumption. Variations and uncertainties in occupants’ energy behavior provide the main obstacle for researchers to analyze and predict the impact of occupant behavior on building energy consumption since commercial buildings often have such a large number of residents with unique energy-use patterns. However, this paper hypothesized that individual occupants have their own individual energy consumption patterns and will typically follow such patterns consistently over time. Thus, this research studies occupant behavior in an office environment to examine whether commercial building’s occupant’s energy-use behaviors are consistent over time. In particular, this research focuses on delay intervals between the occupancy entry/departure events and the beginning/end of the occupant’s energy-consuming behaviors. Occupants’ entry and departure events were detected by passively capturing Wi-Fi packets from occupants’ smartphones while plug-load monitoring detected the beginning/end and quantity of energy use. Results from a four-week long period of tracking individual occupants confirm that occupants use a consistent pattern of starting and ending their energy-use behaviors. Based on these results, this research supports a framework of non-intrusive occupant load monitoring (NIOLM) for tracking occupant-specific energy consuming behaviors in commercial buildings. In the NIOLM framework, the process of tracking each occupant leverages existing Wi-Fi networks, and building energy-monitoring data aggregates energy-consumption data for occupants. Thanks to this study’s findings, NIOLM provides a new opportunity for current industry and research efforts to track occupants’ energy-consuming behaviors at a minimal cost.

Increasing the use of natural light in commercial buildings could save up to 50 percent in energy consumption. Previous research has shown that there is a direct connection between occupant behavior and the energy consumed in a building. Experimental studies suggest occupants are less likely to adjust the shades if the available lighting is within their preferred levels and enough to perform their daily tasks. Therefore, in order to be able to increase daylighting in an office space, it is important to understand the effect of default lighting settings on occupants’ rate of lighting adjustments as well as their performance. In this paper, through the use of immersive virtual environments, the authors have analyzed 64 participants’ interactions and performance in a virtual office space when put in a room with default light setting set at either (1) only natural light available or (2) only artificial light available. The preliminary results indicate that people are significantly more likely to keep the lighting setting if the default has only natural light available than only artificial light.

In the recent years, surveys and studies have established the importance of occupant’s behaviour on energy consumption in buildings. Therefore, inclusion of inhabitants’ behaviours is compulsory for the assessment of building energy management system’s (BEMS) strategies, which highly depends on human behaviour. The purpose of modelling the inhabitants’ behaviour is to see how their choices and control of household appliances can impact the energy consumption. In this paper, a co-simulation approach is presented where the inhabitants’ behaviours are co-simulated with the SIMBAD-MOZART thermal model of a reference house and BEMS. The realization of all the different kinds of inhabitant behaviours into energy co-simulations will help to improve the smart grid technology and hence provide inhabitants with better services to save energy and cost while maintaining their comfort levels.

A general approach is proposed to estimate the number of occupants in a zone using different kinds of measurements such as motion detection, power consumption or CO₂ concentration. The proposed approach is inspired from machine learning. It starts by determining among different measurements those that are the most useful by calculating the information gains. Then, an estimation algorithm is proposed. It relies on a C4.5 learning algorithm that yields human readable decision trees using measurements to estimate the number of occupants. It has been applied to an office setting.

The information of occupant distribution in the whole building scale is important for the evacuation in emergency and the energy conservation of HVAC systems. This information is usually estimated by a sensor network system. However, the evaluation of the performance of the sensor network system is usually hard to be achieved beforehand. Furthermore, the optimization of the sensing system design is much more difficult. For this reason, a simulation platform is of great practical interest for the purpose of the sensing system design and optimization. To this end, we proposed an event based simulation platform which different human movement models, sensor models and estimation algorithms could be input into. For the purpose of simulation efficiency, parallel simulation is applied in our platform. A serious of experiments show the high efficiency and generality of our simulation platform.

In commercial buildings, more than 40% of the energy is consumed by HVAC systems. There is usually a significant difference between energy demand and energy consumption in buildings. It has been widely recognized that occupancy is one of the most important factors impacting actual demands for HVAC systems. However, it is still not clear how and when occupancy should be integrated with HVAC systems at the building level for a long period of time. This is a complex problem as occupancy is variant in nature, and each day occupants have different presences. This paper systematically identifies the significance of variant transitions of occupant presence on the absolute energy efficiency and the relative performance of building level setpoint/setback control policies. An actual office building is used for implementing the proposed methodology. The results showed that the variant transitions of occupant presence have significant influences on the absolute energy efficiency but not on the relative performance of different setpoint/setback control policies.

The current practice of defining operational settings for HVAC systems is to use fixed set points, which assumes static and same comfort requirements for building occupants. The majority of the research efforts in the literature study thermal comfort through time-invariant learning algorithms. However, thermal comfort has been shown to vary from person to person, and change over time due to climatic variations or acclimation. In this paper, we present thermal comfort variation results by studying the data from 33 human subjects and statistically evaluate and study the variations to learn similarities and differences among these individuals. In order to quantify the variations, we briefly describe our adaptive stochastic modeling technique. The technique uses a systematic approach for detecting time dependent thermal comfort variations for an individual. The results confirm that personal comfort vary over time (average: 0.061 °C per day). In addition, we observed a high standard deviation (0.159 °C) across the subjects’ preference variations.

Occupant thermal comfort is a key performance metric for green buildings. Although numerous studies have used thermal comfort as a criterion to control mechanical systems and optimize their energy consumptions, few studies examined individual thermal comfort preferences and their implications. This study aims to investigate the individual thermal comfort preferences in an office space. An online dashboard is developed to collect subjective thermal comfort data from office workers. The data then is compared and correlated with the measured key environmental data, such as air temperature and relative humidity. The data analysis results suggested that different occupants have various preferences about thermal comfort yet each individual has fair consistent thermal preference over time. In future study, the dashboard could be linked to the real-time control system to provide individual thermal control for office workers.

The human body has a biological thermoregulation mechanism (homeostasis), which enables it to maintain a stable and constant body temperature via changing physiological signals such as skin temperature and heart rate. These signal patterns, that are generated based on the human autonomic nervous system, have been validated as a potential variable to provide information about an individual’s current thermal sensations. Among the numerous body segments and parts generating skin temperature, facial skin is five times more sensitive to ambient thermal conditions than other skin surfaces. Therefore, based on the use of facial skin temperatures, this research will establish an adaptive thermal sensation model which will be applicable to automatic (individual) building mechanical system controls within the principle of Human–Building interactive strategy.

Integration of the human dimension in product and process modeling, as related to the integrated design process, requires the accounting of the impact of the factors that determine indoor environmental quality (IEQ). Their influences upon occupant comfort remain the primary measures for predicting productivity and satisfaction within the workplace. The aim of this proposal is to present the outcomes of a yearlong literature review summary of four IEQ elements that most influence occupant health, comfort and wellbeing (i.e., thermal, ventilative, luminous, and acoustic comfort). The study’s results presented provide evidence to modify office environmental design guidelines to improve economic, health, and environmental benefits. Our work postulates that case study data in co-relational analysis is critical to the development of new metrics for simulation predictability in new and repurposed design. Our assessment concludes that two critical steps are necessary to develop predictive measures to insure comfort sufficiency in new and especially retrofit buildings: 1) Because occupants are exposed to multiple factors simultaneously we speculate that the weighted value of the interrelationships of these influences are the critically important factor required to develop articulate sensitivity in simulation schedules; and 2) Because most of the decisions regarding economic investments (especially in repurposing and retrofit design) continue to be made based on the first cost systems replacement rather than on a cohesive model of comfort sufficiency that inclusively embraces both energy efficiency and attaining a high level of environmental quality.

Providing conditioned and fully controlled indoor environment is one of goals of building design. But on the other hand, smart building controllers are providing the required cooling and heating load depending on occupants’ real time feelings. This study focuses on evaluating meaningful and steady state relationship between human thermal perception and body physiological behavior and express comfort feeling perception as a measurable and sensible variable in general occupants through their ambient. For this purpose different body part reaction in the same ambient situation has been studied and two body parts which have the biggest linear temperature changes to ambient condition, has been selected to evaluate this research hypothesis. For these tests, wrist and palm temperatures on different people have been measured accurately with thermal camera to follow the temperature trend on various comfort levels which has been caused by different ambient condition. It has been founded that each person has his own unique temperature on wrist and palm when he feels comfort in the ambient he is in. In the other words each person’s body temperature at comfort level is a precise indicator for comfort feeling of his own. So in the future by having this unique comfort temperature for each person, controlling the indoor condition with the occupants’ body temperature to approach them to their unique hand comfort temperature would be a rational choice to bring convenience while energy has been saved more.

This paper presents a novel modeling methodology that integrates the near building environmental conditions (or microclimate), whole-building design, and occupant behavior. Accurate predictions of the future building operating conditions lead to designs that serve the building’s purpose – to support occupants’ tasks. This study bridges the gap between human factors and architecture to include physical, cognitive, and organizational systems into building information modeling using future typical meteorological year climate data, canyon air temperature microclimate model, and a whole-building energy simulation to investigate the impact of future microclimate conditions on a “typical” single-occupant office. Additionally, to capture the effects of building occupant decision-making and adaptive behaviors, an agent-based model is proposed. Model inputs are task-based, which aim to produce a more robust model to investigate a variety of Human–Building control interactions to ensure high building performance and occupant comfort and satisfaction.

The main purpose of this paper is to determine a procedure or methodology for validating the effectiveness of immersive virtual environment (IVE) applications to studies of occupant behavior. In recent decades, IVE experiments have become increasingly popular in various fields of study such as healthcare, education, science and engineering. They have been effectively utilized to replicate field experiences that are costly or risky in research contexts. Furthermore, this paper proposes to use IVE in initial building designs to identify the most significant predictors of users’ lighting-use behavior. The authors are interested in exploring the extent to which occupants’ responses in IVE accurately reflect those in-situ. In fact, the ultimate goal of this study is to enhance IVE research designs for measuring occupants’ energy behaviors and integrate the results with the existing energy simulation models and reduce the uncertainties in estimates.

These days, the design and construction industry is challenged with the question “How are architects, engineers, contractors, building owners using information technology to further automate their practices, building operation and benchmarking?” The following case study with interviews will critically evaluate the ‘Integrated Project Delivery’ process of the ‘Q1 ThyssenKrupp Quarter’ in Essen, Germany built from 2009 to 2010, and decommissioned in 2012. The Q1-Energy Concept of the ThyssenKrupp Headquarters’ primary energy consumption is measured at 139.6 kWh/m²/yr and its ecological footprint has by 27% less CO₂ emissions than similar buildings in this category. One of the major innovative systems are the Solar Facade controlled system and the Q1-Total Green Building Automation (DESIGO). Siemens specifically developed a Building Automation System (BAS) called Green Building Monitor™ to supervise the building consumption data of Q1 continuously for expert analysis. DESIGO includes a specially developed energy-efficient operating control concept for 1,133 rooms.

Sustainability and energy efficiency are crucial to many sectors and all stakeholders at local, municipal, national, and global scales. According to the city of Vancouver, buildings contribute more than 50 percent of the city’s total GHG emissions. With the introduction of standards such as LEED the hope has been significant reductions in the footprint; however, change has been much lower than predicted. Much of this can be attributed to how people actually use the buildings they inhabit. Preliminary results of semi-structured interviews with residents of a Vancouver multi-residential LEED Gold building show lack of feedback the most common barrier among inhabitants who intend to save energy. Lack of appropriate affordances and constraints follow respectively.

Ever since the first global oil crisis in the seventies, the shortage of fossil fuels, which is considered as the number one resource for energy carrier, has emphasized the need of modern society on cheap energy and resources. Over short or long term, that circumstance is compelling people to consider global energy strategies and accordingly take proper measures. Along with a limited supply of carbon based fuels worldwide, the impact of the carbon footprint— the effect of an increasing carbon dioxide (CO₂) concentration in the earth’s atmosphere and its consequence on the global climate — has become indubitably clear. Recent studies show that building sector consumes significant amounts of energy and is responsible for a considerable amount of greenhouse gas (GHG) emissions. Studies show that amongst several factors contributing to the building energy performance, occupant behavior is one of the driving factors. According to these studies building characteristics and the quality of indoor environment significantly influence the level of satisfaction and efficiency of occupants in buildings. Therefore, a proper design is needed to increase building occupant’s satisfaction and efficiency. To determine the best design strategy, several building envelope materials as well as occupant behaviors should be considered as design variables in the design process. Therefore, the objective of this study is to develop a proper weighting system for different strategies to secure occupants’ satisfaction as well as constructors’ consent. To achieve this objective, a quality function deployment (QFD) method is utilized to identify, translate, and implement the end-user requirements into the design process. In addition, the utility theory is used to reflect the constructors’ perception on usefulness and practicality of end user requirements. The insights from this study will aid in developing better decision making system and more viable alternatives.

A key factor to promote energy efficiency across campus and buildings is through education and clarity of information. Energy data is complex to understand and difficult to comprehend for the majority of users. Higher education institutions consists of building groups that usually operate according to strategies pre-set by the operation teams that respond to a specific energy demand condition. The purpose of this research is to design a data friendly interface to simplify and assist in the implementation of energy literacy across campus. The research method utilized available energy metered and simulated data at the campus level provided by the university operations. A web based data visualization interface is developed and an energy map is created to inform the campus community on the status of energy consumption. In addition to visualization, the proposed interface helps operators to benchmark campus buildings, promote energy efficiency across campus and disseminate the resulting information in a simple, clear and concise way to the campus community. The interface is a great step towards change of behavior through education, a questionnaire to measure user actions and understanding is distributed to measure the effectiveness of this new approach and the results are compiled to advance the interface design and user experience.

The production of sustainable housing requires the cooperation of a variety of participants with different goals, needs, levels of commitment and cultures. To achieve mainstream net zero energy housing objectives, there is arguably a need for a non-linear network of collaboration between all the stakeholders. In order to create and improve such collaborative networks between stakeholders, we first need to map stakeholders’ relationships, processes, and practices. This paper discusses compares and contrasts maps of the sustainable housing production life-cycle in Australia, developed from different perspectives. The paper highlights the strengths and weaknesses of each visualisation, clarifying where gaps in connectivity exist within existing industry networks. Understanding these gaps will help researchers and practitioners identify how to improve the collaboration between participants in the housing industry. This in turn may improve decision making across all stakeholder groups, leading to mainstream implementation of sustainability into the housing industry.

Low-income populations spend a larger proportion of their income on energy usage and tend to live in housing stocks that consume more energy. Thus, energy efficiency is cross-cutting goal, connected to both social and energy-related concerns. One case in point is the Weatherization Assistance Program (WAP): a federal program that assists low-income families through education, energy audits and retrofits. While some states have exerted considerable effort to achieve program goals, others have had lackluster results. The study results here suggest that ambitious program – measured as the proportion of home weatherizations planned that were completed – is influenced by whether states view the program as a social policy-oriented program.

Architectural design can be seen as a multi-objective optimization process, aimed at finding optimal solutions for multiple, often conflicting objectives. Despite the significant advancements of multi-objective optimization methods and building modeling technologies, multi-objective optimization has not been enabled for building design in practice. The reasons include: 1) the complexity of fitness evaluation for quantifiable objectives, such as structural, economical, and sustainable performance objectives, in which a large number of building design decision variables are discrete, nonlinear, and stochastic; and 2) the missing of non-quantifiable objectives of overriding significance to architects, e.g. aesthetic, social, and cultural objectives in optimization. The research objective of this research is to discover how to develop a parametric BIM-based, multi-objective optimization framework with both quantifiable and non-quantifiable objectives, and how it can search for Pareto Optimal design solutions with diverse non-quantifiable objective measures to assist architects’ decision-making.

How might social inequality influence local government decisions to adopt green building incentives or mandates? Extant research has considered the influence that political homophily, environmental activism, and pressure groups play in the spread of sustainability policies. But to what extent do concerns over environmental justice motivate local government adoption of green building, development, and remodeling programs? This study begins to disentangle these questions by asking: do cities within regions with higher levels of social inequality adopt green building policies less frequently than those in areas of lower inequality? Using national survey data, we find measures of wealth and “reformed” government positively influence the use of green-building tools, while regional income inequality dampens these effects.