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IEA EBC Annex 79: Occupant-centric building design and operation

Goal: As building envelopes and mechanical and electrical equipment become more efficient, the impact of occupants on building energy increases. Meanwhile trends in teleworking, co-working, and home-sharing mean vastly different occupancy than the standard occupancy schedules. Finally, global expectations for comfort are increasing, while a variety of new technologies may or may not succeed in meeting this demand. The convergence of these trends has necessitated a new look at how occupants are incorporated into building design and operation practice throughout the building life-cycle.

The field of occupant modelling emerged over four decades ago; however, it has surged in the past decade – particularly as a result of IEA EBC Annex 66 – “Simulation and Definition of Occupant Behaviour in Buildings”. Annex 66 played an important role in formalizing experimental research methods, modeling and model validation, and occupant simulation. Given the number of unanswered questions about occupant comfort and behaviour and minimal penetration of advanced occupant modelling into practice, this follow-up Annex 79 - “Occupant-centric building design and operation”. The IEA EBC Annex 79 term is from 2018-2023. More details here: http://annex79.iea-ebc.org/

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Tianzhen Hong
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This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants' schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.
Jakob Hahn
added a research item
Differences in building operator strategies can significantly affect building energy use and occupant comfort. However, it seems that the daily work of building operators and facility managers is still largely based on heuristics and individual experiences. In this work, we have investigated the current data collection methods during the operation and its daily use in buildings as well as the handling of occupant behavior, comfort, and user complaints based on interviews with ten building operators in Germany. These interviews were conducted as part of an international study of building operator OCC (Occupant-Centric Control) strategies, under the auspices of the IEA EBC Annex 79. The results of the interviews clearly reflect, that until now, communication between building operators and building occupants plays a more important role in optimizing or adjusting building operations to meet occupant needs than the data collected by BAS, which is mainly used to detect faults and check the system status of key HVAC components when faults occur. In some cases, the real-time data are applied for the adjustment of set points and schedules depending on measured conditions; however, customization of set points considering the user’s preferred temperature or ventilation rate or building operation based on occupancy detection has not yet been implemented in the considered buildings. The overall objective of this contribution to building operation research is to highlight best practices and identify white spaces that fulfill occupant requirements and achieve a high level of energy-efficiency. The presented findings identify current gaps between science and practice in the field of sustainable optimization of building operation, but also point out real-world starting points for future research and development.
Tianzhen Hong
added a research item
Energy-related behaviors of occupants constitute a key factor influencing building performance; accordingly, the measured occupant data can support the objective assessment of the indoor environment and energy performance of buildings, which can inform building design and operational decisions. Existing data schemas focus on metadata of sensors, meters, physical equipment, and IoT devices in buildings; however, they are limited in representing the metadata of occupant data, including occupants’ presence in spaces, movement between spaces, interactions with building systems or IoT devices, and preference of indoor environmental needs. To address this gap, an extension to the widely adopted metadata schema, Brick, is proposed to represent the contextual, behavioral, and demographic information of occupants. The proposed extension includes four parts: (1) a new “Occupant” class to represent occupants’ demography and energy related behavioral patterns, (2) new subclasses under the Equipment class to represent envelope system and personal thermal comfort devices, (3) new subclasses under the Point class to represent occupant sensing and status, and (4) new auxiliary properties for occupant interactable equipment to represent the level of controllability for each piece of equipment by occupants. The extension is implemented in the Brick schema and has been tested using multiple occupant datasets from the ASHRAE Global Occupant Database. The extension enables Brick schema to capture diverse types of occupant sensing data and their metadata for FAIR data research and applications.
Clayton Miller
added 2 research items
Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant’s preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with fewer data. This method uses BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios that use conventional zoning and a generic 4x4 square meter grid method from two field-based data sets. The results show that the proposed Build2Vec method has an 18%–23% higher overall sampling quality than the spaces-based and square-grid-based sampling methods. The Build2Vec method also performs similar to the baselines when removing redundant occupant feedback points but with better scalability potential.
This paper describes the adaptation of an open-source ecological momentary assessment smart-watch platform with three sets of micro-survey wellness-related questions focused on i) infectious disease (COVID-19) risk perception, ii) privacy and distraction in an office context, and iii) triggers of various movement-related behaviors in buildings. This platform was previously used to collect data for thermal comfort, and this work extends its use to other domains. Several research participants took part in a proof-of-concept experiment by wearing a smartwatch to collect their micro-survey question preferences and perception responses for two of the question sets. Participants were also asked to install an indoor localization app on their phone to detect where precisely in the building they completed the survey. The experiment identified occupant information such as the tendencies for the research participants to prefer privacy in certain spaces and the difference between infectious disease risk perception in naturally versus mechanically ventilated spaces.
Tianzhen Hong
added a research item
Agent-based modeling is an advanced computational technique capable of representing complex and dynamic processes of human behavior in building performance simulation. Though the agent-based approach supports diverse applications concerning human behavior modeling within the built environment, there is no consensus on the optimal amount of information or level of granularity needed for occupant information representation. This paper attempts to formalize the level of details (LoD) needed for occupant behavior representation in agent-based environments. A novel framework, grounded on the concept of LoD, is proposed to select the required details in representing occupants in agent-based models. Ten attributes related to occupants’ presence, movement, behavioral processes, and repertoire are considered to define the LoD. The framework identifies use case parameters as the guiding principle and allows a hybrid approach for selecting varying degrees of occupant attributes to serve the purpose of simulation. A discussion on the pertinence of different occupant behavior LoDs in relation to the desired objective and simulation context is also presented. The study intends to support the occupant behavior research by advancing agent-based occupant modeling in building performance simulation.
Tianzhen Hong
added a research item
The complexity of occupant behavior is one of the major contributors to uncertainty in building performance simulation. Agent-based modeling (ABM), a computational simulation technique, has gained attention in the occupant modeling field due to its capability and flexibility to capture the heterogeneity and dynamics of human behavior and the emergent effects. While multiple efforts in the past decade have demonstrated the usefulness of the ABM approach for simulating occupants and their impacts on building performance, several crucial matters in the ABM research still remain unexplored. This paper presents ten questions that highlight the most important issues regarding ABM research and applications for occupant behavior in the context of building performance simulation. The questions and answers aim to provide insights into current and future ABM research, and more importantly to inspire new significant questions from young researchers in the field. This research is part of the IEA EBC Annex 79 project, occupant-centric building design and operation.
Marcel Schweiker
added a research item
To design and operate energy efficient and comfortable buildings it is important to know what the occupants’ preferences for indoor environmental quality would be. These preferences are related to a range of personal characteristics that occupants may or may not be willing to share. Preparing materials for a forthcoming stated preference discrete choice experiment (SPDCE) investigating willingness of building occupants to share information, we conducted cognitive-interview pretesting with 12 participants to find out whether these materials were interpretable and meaningful. Qualitative analysis identified seven important limitations, including misinterpretations and uncertainties arising from language and difficulties imagining the situation and options being described. Most participants expressed some desire for a deeper understanding and were not satisfied with the choices they were asked to make. We discuss how identifying these limitations assisted in refining these SPDCE materials, the potential cognitive interviewing has for enhancing the validity of study materials and the importance of better understanding when researching occupant behaviours.
Adam Rysanek
added a research item
Current codes are making buildings more reliant on air-conditioning at the expense of natural ventilation and other cooling solutions. Adam Rysanek (University of British Columbia) explains why this should be countered. Revolutionising codes in a manner that widens the responsibility of architects and engineers to deliver IEQ is urgently needed in advance of future public health crises and the climate emergency. Full article: https://www.buildingsandcities.org/insights/commentaries/rethinking-ieq-standards.html
Adam Rysanek
added a research item
In the pursuit of mitigating the performance gap between model-predicted thermal comfort and measurements of perceived thermal comfort, recent studies have investigated the multi-domain nature of thermal comfort and IEQ. This paper presents an update to a recent work that applied Bayesian logistic regression to examine possible independent correlations between perceived thermal comfort of building occupants and metrics of indoor environmental quality (IEQ) such as CO 2 concentrations. The prior work made use of the COPE dataset, a 20-year-old field database of objective and subjective IEQ measurements collected from approximately 800 occupants of open-plan offices in large Canadian and US cities. This study updates that work with a new IEQ field study of 141 workstations carried out at the University of British Columbia across 2019 and 2020. In addition, a new, hierarchical (multi-level) Bayesian logistic regression model is formulated and applied. The new results not only increase the credibility of observing measurements of CO 2 concentrations to improve predictions of thermal satisfaction in the original COPE dataset, but that the credibility of this evidence is strengthened on the addition of the new data. At indoor temperatures of 23.5 • C, the probability of an occupant feeling thermally satisfied at measured CO 2 levels of 550 ppm was 0.62 [0.54-0.69, 95% CrI]. This decreased to 0.28 [0.17-0.42, 95% CrI] at 750 ppm. This study does not suggest these observations are generalizable outside the COPE and UBC datasets, nor does it suggest that CO 2 must be affecting occupants physiologically or psychologically to cause these observations. However, it is suggested that CO 2 levels may remain overlooked as a contextual, or latent variable for the real-time prediction of perceived thermal comfort in buildings. For instance, prior research has observed a relationship between indoor CO 2 levels and perceived air quality as well as a relationship between multiple perceptual factors of IEQ, such as air quality and thermal comfort. If the findings of this paper continue to be found in future studies, measurements of CO 2 concentrations may be used to improve the accuracy of thermal comfort prediction models. The core recommendation for future work is therefore to expand measurements in future thermal comfort field studies to include, at least, measurements of indoor CO 2 levels. A greater dataset is needed to determine whether the findings of this study are possibly universally applicable, and/or whether knowledge of indoor CO 2 concentrations may improve personalized models of thermal comfort that are building-and context-specific.
Clayton Miller
added a research item
Internet-of-Things (IoT) devices in buildings and wearable technologies for occupants are quickly becoming widespread. These technologies provide copious amounts of high-quality temporal data pertaining to indoor and outdoor environmental quality, comfort, and energy consumption. However, a barrier to their use in many applications is the lack of spatial context in the built environment. Adding Building Information Models (BIM) and Geographic Information Systems (GIS) to these temporal sources unleashes potential. We call this data convergence the Internet-of-Buildings or IoB. In this paper, a digital twin case study of data intersection from various systems is outlined. Initial insights are discussed for an experiment with 17 participants that focused on the collection of occupant subjective feedback to characterize indoor comfort. The results illustrate the ability to capture data from wearables in the context of a BIM data environment.
Gianmarco Fajilla
added a research item
In European efficient buildings, the share of energy use for DHW is a significant percentage of the heat requirement. The evaluation of DHW energy demand and consumption patterns has been neglected in the past and studies are located in Northern countries. This paper presents a survey conducted in Southern Italy. Descriptive statistics were performed to characterize DHW production by climatic zone. Inferential statistics were conducted to discover significant contextual and personal variables and identify usage groups. The most used energy source was biomass (49.3%), followed by methane gas (37.7%). Fireplaces were installed in the coldest zone, in detached houses, more in small municipalities (36%), and low-income families (32%). Solar systems were in 8% of dwellings having income higher than 28000€. Four clusters of dwellings were identified considering the daily usage hours. The eldest and less numerous households fell into the cluster with the lowest usage (3.5 hours). Households with a percentage less than 25% of unemployed components had more probability to belong to clusters with more DHW usages. The paper contributes to fill the investigation in the Mediterranean area and provides a systematic approach to expand the limited knowledge about the influencing factors on the DHW production and usage.
Clayton Miller
added 2 research items
In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each building. This study proposes an approach that utilizes the search volume of topics (e.g., education} or Microsoft Excel) on the Google Trends platform as a proxy of occupant behavior and use of buildings. Linear correlations were first examined to explore the relationship between energy meter data and Google Trends search terms to infer building occupancy. Prediction errors before and after the inclusion of the trends of these terms were compared and analyzed based on the ASHRAE Great Energy Predictor III (GEPIII) competition dataset. The results show that highly correlated Google Trends data can effectively reduce the overall RMSLE error for a subset of the buildings to the level of the GEPIII competition's top five winning teams' performance. In particular, the RMSLE error reduction during public holidays and days with site-specific schedules are respectively reduced by 20-30% and 2-5%. These results show the potential of using Google Trends to improve energy prediction for a portion of the building stock by automatically identifying site-specific and holiday schedules.
Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person’s thermal preference. The spatial context of a building can provide information to models about the windows, walls, heating and cooling sources, air diffusers, and other factors that create micro-environments that influence thermal comfort. Due to spatial heterogeneity, it is impractical to position sensors at a high enough resolution to capture all conditions. This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting spatial–temporal occupants’ indoor environmental preferences. Build2Vec utilizes the spatial data from the Building Information Model (BIM) and indoor localization in a real-world setting. This framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA). The aggregation of these data is combined into a graph network structure (i.e., objects and relations) and used as input for a classification model to predict occupant thermal preference. The results of a test implementation show 14%–28% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.
Clayton Miller
added a research item
Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's thermal preference. The spatial context of a building can provide information to models about the windows, walls, heating and cooling sources, air diffusers, and other factors that create micro-environments that influence thermal comfort. Due to spatial heterogeneity, it is impractical to position sensors at a high enough resolution to capture all conditions. This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting spatial-temporal occupants' indoor environmental preferences. Build2Vec utilizes the spatial data from the Building Information Model (BIM) and indoor localization in a real-world setting. This framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA). The aggregation of these data is combined into a graph network structure (i.e., objects and relations) and used as input for a classification model to predict occupant thermal preference. The results of a test implementation show 14-28% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.
Tianzhen Hong
added a research item
Occupant behavior simulation frameworks can employ synthetic populations to characterize occupancy and behavioral patterns in buildings based on observed demographic data at a certain geographical location. Multiple methods are available to generate a synthetic population, with pros- and cons- for different applications and contexts. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing case study. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. We draw upon the extended version of Drivers-Needs-Actions-Systems (DNAS) framework to guide the selection of variables and data imputation. The resulting synthetic occupant data is evaluated by comparing the joint distributions between the actual and synthetic data sets, % difference, and Standardized-Root-Mean-Squared-Error (SRMSE). Our results show that the BN approach is poweful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on multi-family housing particularly show better performance than the office and senior housing cases. Future work includes testing and demonstration of the synthetic data set as an input to the occupant behavior module of a co-simulation with a building performance modeling tool such as EnergyPlus.
Tareq Abuimara
added a research item
This paper aims to document the status and challenges of communicating occupant-related assumptions among design stakeholders during the building design process. In this study, the findings of interviews with ten building design professionals are presented. To this end, architects, engineers, and energy consultants from different countries were interviewed using a set of twenty questions that assess interviewees backgrounds, roles, awareness of the impact of occupant-related assumptions, and the level and means of sharing occupant-related assumptions. The interviews indicated the need for improvement in three areas: developing improved occupant-related assumptions sharing mechanisms, promoting the integrated design process (IDP), and updating relevant codes and standards with more comprehensive and detailed requirements. Key Innovations • This paper documents the status and challenges of communicating occupant-related assumptions which is linked to design uncertainty. • The findings of this study underscores practices that are linked to the widely recognized performance gap. Practical Implications • This paper provides insights on how to better design for occupants in a simulation-aided design process, considering the uncertainty of occupants. • The paper highlights the benefits of adopting and implementing the integrated design process (IDP) in bridging the building performance gap.
Tianzhen Hong
added an update
From Liam -
Another great Annex 79 meeting - thanks Julia Day, PhD, LEED AP and Shelby Ruiz for hosting! https://lnkd.in/gQdHiG3. Next one in London in the spring of 2022.
 
Marcel Schweiker
added a research item
Occupants’ comfort perception affects building energy consumptions. To improve the understanding of human comfort, which is crucial to reduce energy demand, laboratory experiments with humans in controlled environments (test rooms) are fundamental, but their potential also depends on the characteristic of each research facility. Nowadays, there is no common understanding for definitions, concepts, and procedures related to human comfort studies performed in test rooms. Identifying common features would allow standardising test procedures, reproducing the same experiments in different contexts, and sharing knowledge and test possibilities. This review identifies 187 existing test rooms worldwide: 396 papers were systematically selected, thoroughly reviewed, and analysed in terms of performed experiments and related test room details. The review highlights a rising interest in the topic during the last years, since 46% of related papers has been published between 2016 and 2020. A growing interest in non-thermal sensory domains (such as visual and air quality) and multi-domain studies about occupant's whole comfort emerged from the results. These research trends have entailed a change in the way test rooms are designed, equipped and controlled, progressively becoming more realistic inhabitable environments. Nevertheless, some lacks in comfort investigation are highlighted: some continents (like Africa and South America) and climate zones are found to be underrepresented, while involved subjects are mainly students performing office tasks. This review aspires to guide scientists and professionals toward the improved design or the audit of test room experimental facilities, especially in countries and climate zones where human comfort indoors is under-studied.
Tianzhen Hong
added a research item
Occupants’ adaptive strategies play an important role in office buildings' energy consumption. Previous research has mostly focused on the adaptive strategies triggered by occupants’ indoor discomfort; however, it is crucial to understand if specific adaptive strategies are linked to occupants’ energy-saving intentions. This study explores the relationships among employees’ heuristic decision-making in their first choice of adaptive strategies (technological solutions or personal adjustments) when facing extreme discomfort conditions, and their energy-saving intentions, then links these patterns with building design, workplace contextual factors and demographics. A cross-sectional survey was collected among university employees from China, Brazil, Italy, Poland, Switzerland, and the US. Our results demonstrated that the accessibility to indoor environmental controls (IECs) and office type were significant factors for adaptive strategies. There was a positive relationship between the number of IEC features and percentage of employees choosing a technological solution. When feeling too hot, occupants in private offices are more likely to adopt a technological solution, whereas occupants in cubicles are more likely to choose a personal adjustment. Occupants with energy-saving intentions are less likely to choose thermostat adjustments or use portable devices as adaptive strategies than their counterparts. Finally, the cluster analysis suggests females were more likely to use adaptive strategies for energy-saving purposes than males. The majority of occupants would turn on/off lighting to save energy. The study provides contributions in the connection between heuristic decision-making process, and energy-saving intentions and recommendations on design strategies for building architects, engineers, and managers.
Clayton Miller
added 2 research items
Internet-of-Things (IoT) devices in buildings and wearable technologies for occupants are quickly becoming widespread. These technologies provide copious amounts of high-quality temporal data pertaining to indoor and outdoor environmental quality, comfort, and energy consumption. However, a barrier to their use in many applications is the lack of spatial context in the built environment. Adding Building Information Models (BIM) and Geographic Information Systems (GIS) to these temporal sources unleashes potential. We call this data convergence the Internet-of-Buildings or IoB. In this paper, a digital twin case study of data intersection from various systems is outlined. Initial insights are discussed for an experiment with 17 participants that focused on the collection of occupant subjective feedback to characterize indoor comfort. The results illustrate the ability to capture data from wearables in the context of a BIM data environment.
Gianmarco Fajilla
added a research item
Occupancy modeling in office buildings is still in progress and needs to be developed by using observable data. In this paper, an office building under the Mediterranean climate was instrumental in the collection of both indoor environmental parameters (air temperature and relative humidity, CO2, VOC) and user action-related variables (electricity power, window, door state, and air conditioning use). Each parameter was monitored along with the occupancy state at a one-minute time step for two years. The purpose of the investigation was to evaluate the potential application of three straightforward models, such as the Law of Total Probability (LTP), Naïve Bayes classifier (NB), and Classification and Regression Tree (CART), to estimate the occupancy state using the indoor measurements. Thirty-four (34) different combinations of parameters were applied on the developed models; the true positive rate (TPR), true negative rate (TNR), and accuracy were used as evaluation metrics. The results confirmed that the performances of the models were influenced by both the number and the typology of the used parameters. In particular, the CART model was found to be the least affected by them; almost half of the parameters’ combinations provided accuracies higher than 93% and TNR higher than TPR. Accuracies of the order of 90% were obtained with NB and LTP.
Marcel Schweiker
added a research item
Occupants' satisfaction had been researched independently related to thermal and visual stimuli for many decades showing among others the influence of self-perceived control. Few studies revealed interactions between thermal and visual stimuli affecting occupant satisfaction. In addition, studies including interactions between thermal and visual stimuli are lacking different control scenarios. This study focused on the effects of thermal and visual factors, their interaction, seasonal influences, and the degree of self-perceived control on overall, thermal, and visual satisfaction. A repeated-measures laboratory study with 61 participants running over two years and a total of 986 participant sessions was conducted. Mixed model analyses with overall satisfaction as outcome variable revealed that thermal satisfaction and visual satisfaction are the most important predictors for overall satisfaction with the indoor environment. Self-perceived thermal control served as moderator between thermal satisfaction and overall satisfaction. Season had slight influence on overall satisfaction. Random effects explained the highest amount of variance, indicating that intra- and interindividual differences in the ratings of satisfaction are more prevalent than study condition. Future building design and operation plans aiming at a high level of occupant satisfaction should consider personal control opportunities and take into account the moderating effect of control opportunities in multimodal interactions.
Christiane Berger
added a research item
The comfort requirements of occupants influence indoor-environmental factors and energy performance of buildings. Occupants are typically exposed to a multitude of indoor-environmental factors, including a variety of different thermal, auditory, visual, and air quality conditions. However, the bulk of past research and derivative indoor-environmental codes and standards concerning the comfort of occupants address the multiple indoor-environmental stimuli in isolation. Starting from a brief review of past research on multi-perceptual indoor-environmental assessments of occupants, the present study pursues an experimental approach to explore the potential cross-modal effects on the evaluation of indoor-environmental thermal, visual, and acoustic aspects. In this context, a laboratory space including two adjacent identical mock-up office rooms was used to conduct multi-aspect parametric studies with human participants. Different thermal, visual, and auditory conditions were maintained in these two units. In the course of the present study, 296 participants were exposed, on a short-term basis, to different combinations of thermal, visual, and auditory conditions. The experiments were intended to explore if the evaluation of one aspect of the indoor environment could be influenced by differences in the values pertaining to the other aspects. The experimental results are presented and discussed, including their limitations.
Tianzhen Hong
added an update
Annex 79 completed the online 6th experts meeting and the 7th international occupant behavior symposium during April 19-21, 2021. It is great to see more than 120 participants from many time zones across the globe. So much progress and achievements despite the COVID-19 challenges. Many thanks to the operating agents Andreas and Liam for leading Annex 79 and Voja of NTNU for hosting the meetings and symposium.
 
Elie Azar
added a research item
Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.
Clayton Miller
added a research item
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use.
Andrew Sonta
added 2 research items
COVID-19 has touched almost all facets of modern life. As part of this global shift, many employers have recommended employees work from home in an effort to curb the spread of infection. When organizations bring workers back to the office, the specific policies for personnel reintroduction will shape both productivity and the spread of disease. This study explores the secondary social and energy impacts of potential reintroduction policies. Using a socio-organizational network inferred from an office in Redwood City, California, we define social, epidemic resistance, and energy metrics which are used to compare the character of personnel rein-troduction plans. Our notable findings are, first, that the choice of which occupants return has a large effect on modeled network-level epidemic resistance. Second, where the occupants are located can significantly impact overlap in space-use within smaller spatial zones-a concept related to social distancing. In summary, this work is a critical first step in demonstrating the value of intelligent occupant network topology based reintroduction schemes in offices that can minimize: disease spread, socio-organizational disruptions and building energy use impacts.
One of the primary driving factors in building energy performance is occupant behavioral dynamics. As a result, the layout of building occupant workstations is likely to influence energy consumption. In this paper, we introduce methods for relating lighting zone energy to zone-level occupant dynamics, simulating energy consumption of a lighting system based on this relationship, and optimizing the layout of buildings through the use of both a clustering-based approach and a genetic algorithm in order to reduce energy consumption. We find in a case study that nonhomogeneous behavior (i.e., high diversity) among occupant schedules positively correlates with the energy consumption of a highly controllable lighting system. We additionally find through data-driven simulation that the naïve clustering-based optimization and the genetic algorithm (which makes use of the energy simulation engine) produce layouts that reduce energy consumption by roughly 5% compared to the existing layout of a real office space comprised of 151 occupants. Overall, this study demonstrates the merits of utilizing low-cost dynamic design of existing building layouts as a means to reduce energy usage. Our work provides an additional path to reach our sustainable energy goals in the built environment through new non-capital-intensive interventions.
Tianzhen Hong
added a research item
Since the introduction of the occupant behavior Drivers-Needs-Actions-Systems (DNAS) framework in 2013, researchers have used the framework or further developed it based on their case studies, which include efforts to collect new data on occupant behaviors. The effort is often costly for the relatively few new data points added. Problems emerge when the already collected data do not meet the modelers’ interoperability requirements. Previous studies addressed this issue by developing more sophisticated ontologies that enable integration with other datasets and synthetic data methodologies that would meet unique research applications. This paper presents an extension of the DNAS framework for the representation of synthetic occupant data to support various applications and use cases across the building life cycle. An agent-based modeling application is one of our motivations that requires more elaborate characteristics of an occupant-agent or a group-of-agent. The extension, built upon a review of the literature, introduces new elements to the framework that fall into five categories, including socio-economic, geographical location, activities, subjective values, and individual and collective adaptive actions. On-going research includes identifying occupant datasets and developing data fusion methods to generate synthetic occupants, as well as to demonstrate its applications in agent-based modeling coupled with building performance simulation.
Romana Markovic
added 2 research items
As the number of installed meters in buildings increases, there is a growing number of data time-series that could be used to develop data-driven models to support and optimize building operation. However, building data sets are often characterized by errors and missing values, which are considered, by the recent research, among the main limiting factors on the performance of the proposed models. Motivated by the need to address the problem of missing data in building operation, this work presents a data-driven approach to fill these gaps. In this study, three different autoencoder neural networks are trained to reconstruct missing short-term indoor environment data time-series in a data set collected in an office building in Aachen, Germany. This consisted of a four year-long monitoring campaign in and between the years 2014 and 2017, of 84 different rooms. The models are applicable for different time-series obtained from room automation, such as indoor air temperature, relative humidity and CO2 data streams. The results prove that the proposed methods outperform classic numerical approaches and they result in reconstructing the corresponding variables with average RMSEs of 0.42 °C, 1.30% and 78.41 ppm, respectively.
The aim of this work is to develop and validate a miscellaneous electric loads (MEL) predictive model that does not require occupant-wise or building-wise model training nor model adaptation while achieving competitive accuracy. For that purpose, a long-short-term memory (LSTM) model was developed using monitored data from a research building located in Abu Dhabi, United Arab Emirates (UAE). In order to test the generalization capabilities of the proposed method, the model was evaluated using data from two additional buildings, a bank office building located in Frankfurt, Germany, and a university building in Ottawa, Canada. The results showed that the developed LSTM is applicable to the tested buildings without the need for occupant-wise or building-wise calibration, hence, addressing an important gap in the existing literature. In addition, a set of MEL predictive models from the literature, that are based on a Weibull distribution and Gaussian mixture models (GMM) are implemented and evaluated using the three identical data sets. The round-robin evaluation of existing MEL predictive models showed that the proposed LSTM model outperformed them especially when a combination of MEL and occupancy information was used as inputs. Finally, the neural network saturation was identified as the key challenge when developing an LSTM-based model for MEL prediction.
Tianzhen Hong
added a research item
Energy-related occupant behaviour in buildings has demonstrated considerable energy-saving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models. This article proposes a holistic survey-and-simulation-based framework for estimating the energysaving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement program with effective and efficient measures.
Tareq Abuimara
added 2 research items
Designing high-performance buildings is a complex process that involves several stakeholders at different stages of design development. Design stakeholders need to work together to achieve design objectives and overcome the challenge that arises from inefficient collaboration and coordination. Among these challenges are occupant-related assumptions which are made throughout the design process, including schematic design, energy modelling, construction, and even operation. Accuracy of these assumptions is highly dependent on the design stakeholders' objectives and the time that they are engaged in the design process. Differences in occupant-related assumptions can lead to a considerable level of uncertainty, which probably leads to suboptimal design decisions. To this end, the current practice, including the challenges and the barriers, needs to be documented and understood in order to develop an improved occupant modelling approach during building design. Therefore, this paper highlights the current practices of communicating occupant-related assumptions in the building design process. In this paper, we also argue the need for in-depth consideration of communication among design stakeholders.
Designers typically follow a simplistic approach regarding occupant distribution in building simulation models, such as a uniform distribution. However, this approach rarely reflects reality, as occupants are often distributed heterogeneously in building spaces. To this end, this study develops a methodology to examine the impact of occupant distribution on energy and comfort performance; it then applies the methodology to an office building model in Toronto, Canada. The building energy model was simulated under a set of seventy-five plausible occupants’ distribution scenarios (ODSs). The results indicate that ODSs can have a significant impact on occupants’ comfort as densely populated zones experienced significantly higher discomfort hours per occupant compared to the homogeneous distribution. On the other hand, the variable ODSs had a modest impact on energy performance as the highest difference in energy use intensity was observed to be about 9%, given that the model heating, ventilation, and air conditioning was hard-sized for all simulations. Finally, the benefits of deploying adaptive technologies such as demand-controlled ventilation were assessed in terms of mitigating the impact of variable ODSs.
Astrid Roetzel
added a research item
The dynamic nature of daylight requires a responsive strategy by utilizing adaptive facades (AFs) as shading systems to control the indoor environment. Controlling an adaptive façade's performance is feasible either through an automatic control strategy without any user interventions or occupant-centric control strategy allowing user interactions. Lack of occupant-centric control is experienced when a fully-automatic control is operating the shading system. In addition, limiting or disabling users to overrule their local environment showed negative impacts on their overall satisfaction and comfort. To this end, this paper reviews the current state-of-the-art with respect to occupant-centric control strategies in the literature and identifying the research gaps for future investigations. As a major finding, occupant-centric control is limited to certain applications, which imposes thinking of a new innovative shading control strategy that counterbalances antagonistic phenomena and can enhance both user's comfort desires and building's energy efficiency especially in a shared working environment.
Astrid Roetzel
added a project reference
Marcel Schweiker
added a research item
A discussion of sustainability in architecture cannot be meaningfully carried out without the inclusion of most buildings’ central purpose, namely the provision of indoor environments that are accommodating of occupants’ needs and requirements. To this end, building designers and operators are expected to demonstrate compliance with codes and standards pertaining to indoor environmental quality (IEQ). However, the majority of conventional IEQ standards, codes, and guidelines have a single-domain character, in that they address IEQ in terms of a number of isolated domains (i.e., thermal, visual, acoustic, air quality). In this context, the present contribution explores the current state of multi-domain IEQ evaluation approaches and the necessary conditions for their further development and application. Toward this end, a number of common building rating schemes were selected and analyzed in detail. The results of this assessment imply the necessity of both short-term improvements of the existing schemes in terms of the transparency and plausibility of the applied point allocation and weighting strategies and the fundamental need for a deeper empirically grounded understanding of the nature of occupants’ perception of and behavior in the built environments
Andrew Sonta
added a research item
We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations.
Clayton Miller
added a research item
Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect occupant comfort preference. Human perception could be helpful to capture these disparate phenomena and interpreting their impact; the challenge is collecting spatially and temporally diverse subjective feedback in a scalable way. This paper presents a methodology to collect intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An experiment with 30 occupants over two weeks produced 4378 field-based surveys for thermal, noise, and acoustic preference. The occupants and the spaces in which they left feedback were then clustered according to these preference tendencies. These groups were used to create different feature sets with combinations of environmental and physiological variables, for use in a multi-class classification task. These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned. The most accurate model had multi-class classification F1 micro scores of 64%, 80% and 86% for thermal, light, and noise preference, respectively. The discussion outlines how these models can enhance comfort preference prediction when supplementing data from installed sensors. The approach presented prompts reflection on how the building analysis community evaluates, controls, and designs indoor environments through balancing the measurement of variables with occupant preferences in an intensive longitudinal way.
Astrid Roetzel
added a research item
The aim of this research was to assess the reliability of occupants’ verbalised thoughts and experiences of environmental conditions for objective environmental condition assessment. This research was based on the Integral Sustainable Design approach and included concurrent think-aloud method and environmental measurements at participants indicated most intense temperature, air movement, sound, lighting and preferred place to study (PPS) locations. The results showed that parameters of the most intense environmental places were significantly higher or lower than PPS. Participants’ experiences of the most intense environmental places as intensely high and low, compared to PPS, was generally mirrored by differences in measured environmental parameters between the two sets of places. Findings show that, in this case, human sensors of environmental conditions are reliable. The think-aloud method revealed that participants considered multiple environmental parameters of PPS concurrently and made trade-offs that prioritised the studying task. Future studies might consider integrating first-person reporting with building operation and maintenance functions to improve efficiency without compromising occupants’ preferences, satisfaction and comfort.
Philip Agee
added 2 research items
Smart buildings are complex systems, yet architecture, engineering, and construction (AEC) professionals often perform their work without considering the human factors of building occupants. Traditionally, the AEC industry has employed a linear design and delivery approach. As buildings become smarter, the AEC industry must adapt. To maximize human well-being and the operational performance of smart buildings, an iterative, human-centred approach must be employed. The omission of human factors in the design and delivery of smart building systems risks misalignment between occupant-user needs and the AEC industry's perception of occupant-user needs. This research proposes a human-centred approach to smart housing. The study employed a multi-phase, mixed-methods research design. Data were collected from 309 high performance housing units in the United States. Longitudinal energy use data, occupant surveys, and semi-structured interviews are the primary data inputs. Affinity diagramming was leveraged to categorize the qualitative data. The output of the affinity diagramming analysis and energy analysis led to the development of data-driven Personas that communicate smart housing user needs. While these data were gathered in the United States, researchers, practitioners, and policy-makers can leverage the human-centred approach presented in this paper toward the design of other human-centred buildings and infrastructure.
Clayton Miller
added a research item
The activity-based workspace (ABW) paradigm is becoming more popular in commercial office spaces. In this strategy, occupants are given a choice of spaces to do their work and personal activities on a day-to-day basis. This paper shows the implementation and testing of the Spacematch platform that was designed to improve the allocation and management of ABW. An experiment was implemented to test the ability to characterize the preferences of occupants to match them with suitable environmentally-comfortable and spatially-efficient flexible workspaces. This approach connects occupants with a catalog of available work desks using a web-based mobile application and enables them to provide real-time environmental feedback. In this work, we tested the ability for this feedback data to be merged with indoor environmental values from Internet-of-Things (IoT) sensors to optimize space and energy use by grouping occupants with similar preferences. This paper outlines a case study implementation of this platform on two office buildings. This deployment collected 1,182 responses from 25 field-based research participants over a 30-day study. From this initial data set, the results show that the ABW occupants can be segmented into specific types of users based on their accumulated preference data, and matching preferences can be derived to build a recommendation platform.
Astrid Roetzel
added a research item
The current development of shading systems which respond to both environmental and human inputs; leads to envisioning an envelope that is multifunctional, responsive and dynamic, named as Adaptive Facades (AFs). The key aspect of any external shading system is to control solar radiation that might cause unwanted thermal and visual stresses for occupants which can lead to higher cooling loads significantly. Recent studies attempted to develop non-conventional AFs such as folding structures to fulfill the incapability of conventional systems (e.g. venetian blinds) for delivering multi-objective user demands due to their different design features. Therefore, in this paper motorized venetian blinds were initially used as conventional AFs in Energy Management System (EMS) of EnergyPlus to develop a reference case study. Furthermore, an automatic control was assigned to venetian blinds based on two indoor sensors to control slat angles on hourly basis, Daylight Glare Index (DGI) and task horizontal illuminance. Then, two new control algorithm were derived as hourly transmittance schedule (hTs) and shade factor through extended calculation methods that were used to modify the properties of a roller shade as to take into account the same adjustment of venetian blinds based on indoor sensors. Finally, a comparison was conducted based on annual energy consumption including heating and cooling loads, in which results showed that both methods have discrepancy lower than 6% and can be alternative solutions to evaluate the performance of non-conventional adaptive systems. However, there are several limitations and applicability of the suggested methodologies that were also discussed.
Tianzhen Hong
added a research item
Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Future research directions include the need for large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, the need for improved metrics of measuring building performance, as well as the need for industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.
Jakob Hahn
added a research item
The building sector is responsible for about 40 % of the EU primary energy consumption. For this reason, the EU and its member states have constantly strengthened energy legislation, such as implementing the Energy Building Performance Directive (EPBD) and the German Energy Saving Ordinance (EnEV). While on the one hand, the energy efficiency of buildings is continuously improving, on the other hand, there is often a significant gap in energy performance between the targeted values according to code calculations and simulations from design stages and real operation. In addition to inefficient operation or poor construction of the building and building systems, this is mainly attributed to occupant behavior (OB). The aim of this research paper is to present a broad overview of the different types of energy performance gaps in the literature. The focus beyond this is on the energy performance gap (EPG) of low-energy and "nearly zero energy buildings (nZEB)", in connection with occupant behavior and the underlying causes of the observed deviations in residential buildings. To this end, one high-efficiency residential building with eight apartments is examined and analyzed as a case study. Comparisons are made between the code compliance calculations, simulations with different occupant behaviors, and real measurements from Technical Monitoring (TMon). Several factors are eliminated in order to be able to evaluate the actual deviation by occupant behavior more precisely. The study considers thermal energy for space heating and domestic hot water, as well as electricity usage.
Elie Azar
added a research item
Building occupants are continuously exposed to multiple indoor environmental stimuli, including thermal, visual, acoustic, and air quality related factors. Moreover, personal and contextual aspects can be regarded as additional domains influencing occupants’ perception and behaviour. The scientific literature in this area typically deals with these multiple stimuli in isolation. In contrast to single-domain research, multi-domain research analyses at least two different domains, for example, visual and thermal. The relatively few literature reviews that have considered multi-domain approaches to indoor-environmental perception and behaviour covered only a few dozen articles each. The present contribution addresses this paucity by reviewing 219 scientific papers on interactions and cross-domain effects that influence occupants’ indoor environmental perception and behaviour. The objective of the present review is to highlight motivational backgrounds, key methodologies, and major findings of multi-domain investigations of human perception and behaviour in indoor environments. The in-depth review of these papers provides not only an overview of the state of the art, but also contributes to the identification of existing knowledge gaps in this area and the corresponding need for future research. In particular, many studies use “convenience” variables and samples, there is often a lack of theoretical foundation to studies, and there is little research linking perception to action.
Tianzhen Hong
added a research item
Despite the fact that buildings are designed for occupants in principle, evidence suggests buildings are often uncomfortable compared to the requirements of standards; difficult to control by occupants; and, operated inefficiently with regards to occupants’ preferences and presence. Meanwhile, practitioners –architects, engineers, technology companies, building managers and operators, and policymakers – lack the knowledge, tools, and precedent to design and operate buildings optimally considering the complex and diverse nature of occupants. Building on the success of IEA EBC Annex 66 (“Definition and simulation of occupant behavior in buildings”; 2013-2017), a follow-up IEA EBC Annex 79 (“Occupant-centric building design and operation”; 2018-2023) has been developed to address gaps in knowledge, practice, and technology. Annex 79 involves international researchers from diverse disciplines like engineering, architecture, computer science, psychology, and sociology. Annex 79 and this review paper have four main areas of focus: (1) multi-domain environmental exposure, building interfaces, and human behavior; (2) data-driven occupant modeling strategies and digital tools; (3) occupant-centric building design; and (4) occupant-centric building operation. The objective of this paper is to succinctly report on the leading research of the above topics and articulate the most pressing research needs – planned to be addressed by Annex 79 and beyond.
Tianzhen Hong
added a research item
In light of recent research, it is evident that occupants are playing an increasingly important role in building energy performance. Despite the important role of building energy codes and standards in design, the occupant-related aspects are typically simple and have not kept up with the leading research. This paper reviews 23 regions’ building energy codes and standards by first comparing their quantitative aspects and then analyzing their mandated rules and approaches. While the present paper focuses on offices, general recommendations are applicable to other building types as well. The review revealed a wide range of occupant-related values, approaches, and attitudes. For example, code-specified occupant density varies by nearly a factor of three between different codes. This underlines the need for development of advancement in occupant behavior modeling approaches for future occupant-centric building performance codes and standards. Moreover, occupants are often referred to only implicitly; underlying expectations about energy-saving occupant behavior from building occupants varies greatly; and, only a few codes address occupant feedback and system usability. Based on the findings of the review, a set of initial recommendations for future building energy codes is proposed.
Tianzhen Hong
added a research item
The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.
Jakob Hahn
added a research item
In recent years, research has emerged to quantitatively and qualitatively understand occupants' interactions with buildings. However, there has been surprisingly little research on building interfaces and how their design, context (e.g., location), and underlying logic impact their usability and occupants’ perceived control, as well as the resulting comfort and energy performance. Research is needed to better understand how occupants interact with building interfaces in both commercial and residential applications; both applications are important to address as there are many differences in interface types, level of control and understanding, and even expectations of engagement. This paper provides a cursory review and discussion of select common building interfaces: windows, window shades/blinds, thermostats, and lighting controls. The goal of this paper is to review literature related to these human-building interfaces to explore interface characteristics, current design and use challenges, and relationships between building interfaces and occupants. Human-building interface interactions are complex, more research is needed to understand design, use, and characteristics. Common themes emerged throughout the literature review to explain occupant interactions (or lack of interactions) with building interfaces, which included thermal and visual comfort, ease and access of control, interface/control placement, poor interface/control design, lack of understanding, and social-behavioral dynamics.
Marcel Schweiker
added a research item
The usage of "resilient" increased over the last decade reflecting anticipated changes in our climate and resulting necessary changes in our energy system. While resilience is popular, its usage varies to such extent that opposing consequences for the building design are promoted: robustness or flexibility. This paper questions whether resilient buildings support the resilience of their occupants and presents a framework for human-building resilience, pointing to distinctive aspects like "toughness", "ability to cope", or "capacity to recover". In addition, this framework includes a wider definition of thermal comfort, which considers not only thermal relief as provided by thermoneutral conditions, but thermal encouragement, related to adaptation, and thermal enjoyment, pointing to thermal alliesthesia. Based on previously published and new data from laboratory studies, the presentation of first attempts analysing individual parameters of human resilience is followed by a discussion of the consequences of the new framework for future pathways of design and operation. Should we continue searching for ways to predict "optimal" conditions or shift our focus towards those design and operation concepts, which optimise encouragement and enjoyment and consequently lead to a higher human resilience and a lower dependence on building resilience or intensive energy use?
Tianzhen Hong
added an update
Annex 79 just completed its first virtual meetings -a huge success and record number of participants. Thanks to operating agents Liam and Andreas as well as the host University of Southampton.
 
Tianzhen Hong
added 2 research items
In light of recent research, it is evident that occupants are playing an increasingly important role in building energy performance. Despite the important role of building energy codes and standards in design, the occupant-related aspects are typically simple and have not kept up with the leading research. This paper reviews 23 regions’ building energy codes and standards by first comparing their quantitative aspects and then analyzing their mandated rules and approaches. While the present paper focuses on offices, general recommendations are applicable to other building types as well. The review revealed a wide range of occupant-related values, approaches, and attitudes. For example, code-specified occupant density varies by nearly a factor of three between different codes. This underlines the need for development of advancement in occupant behavior modeling approaches for future occupant-centric building performance codes and standards. Moreover, occupants are often referred to only implicitly; underlying expectations about energy-saving occupant behavior from building occupants varies greatly; and, only a few codes address occupant feedback and system usability. Based on the findings of the review, a set of initial recommendations for future building energy codes is proposed.
Despite the fact that buildings are designed for occupants in principle, evidence suggests buildings are often uncomfortable compared to the requirements of standards; difficult to control by occupants; and, operated inefficiently with regards to occupants’ preferences and presence. Meanwhile, practitioners –architects, engineers, technology companies, building managers and operators, and policymakers – lack the knowledge, tools, and precedent to design and operate buildings optimally considering the complex and diverse nature of occupants. Building on the success of IEA EBC Annex 66 (“Definition and simulation of occupant behavior in buildings”; 2013-2017), a follow-up IEA EBC Annex 79 (“Occupant-centric building design and operation”; 2018-2023) has been developed to address gaps in knowledge, practice, and technology. Annex 79 involves international researchers from diverse disciplines like engineering, architecture, computer science, psychology, and sociology. Annex 79 and this review paper have four main areas of focus: (1) multi-domain environmental exposure, building interfaces, and human behavior; (2) data-driven occupant modeling strategies and digital tools; (3) occupant-centric building design; and (4) occupant-centric building operation. The objective of this paper is to succinctly report on the leading research of the above topics and articulate the most pressing research needs – planned to be addressed by Annex 79 and beyond.
Shen Wei
added 12 research items
In order to gain a good understanding of residential building energy consumption in China, a case study about occupants' use of electricity for lighting, appliances and room air conditioners was carried out in 44 identical apartments in Beijing. Additionally, two apartments with significantly different energy consumption levels were chosen for a detailed study about the impact of occupants' daily behavior in using lighting, appliances and room air conditioners. The results of this study demonstrate the important contribution of occupant behavior to the significant variation of electricity consumption among residential buildings. The detailed measurement of occupant behavior showed a potential in better understanding how energy is consumed in buildings and relevant information gathered from this process can help occupants change their behavior for energy saving.
Isabella Gaetani
added a research item
Occupant behaviour (OB) is recognized as a leading source of uncertainty in building performance predictions. Neglecting the potential influence of uncertainties on building performance could result in erroneous decision-making during the design phase. Therefore, it is essential that uncertainties are appropriately considered within building performance simulation (BPS) models. As for OB, there are various approaches to model occupant presence and actions in BPS tools. Literature shows that the appropriate modelling approach depends on the object and purpose of the simulation, which makes it difficult to favour a method over another a priori. Moreover, there is very little support for selecting the most appropriate modelling approach. As a result, OB is modelled in practice in various ways, mostly dictated by intuition and habit. This study builds on previous literature to introduce and test a complete approach for appropriate OB modelling. The method can be generalized and readily applied to design questions.
Shen Wei
added a research item
Lighting control in office buildings is driven by occupant's demand for indoor light environment. The control behavior not only has a direct impact on occupants’ visual comfort, but also relates with the building lighting energy consumption. However, due to the effect of glare, lighting control is often associated with shading adjustment. In this regard, this paper proposed a prediction model which can accurately describe the lighting and shading coupling control behavior by fully considering the difference and diversity of occupants. The light environment preferences and the usage habits of lighting and shading system of occupants was firstly investigated and classified by means of questionnaire. Markov model and log-logistic survival model were introduced to quantitatively describe the probability distribution of various shading and lighting control behaviors. On this basis, combined with the indoor workplane illumination prediction model, the behavior of occupant's lighting and shading coupling control can be predicted. By comparing the four models considering or not considering the diversity and coupling effect, it is found that the proposed coupling prediction models show better performance, the maxium error rate is only 13.04% for the lighting energy consumption prediction.
Astrid Roetzel
added a research item
Adaptive facades (AFs) are building envelopes that can control occupant’s visual and thermal comfort along enhancing energy savings. However, to achieve this purpose, an appropriate control strategy is needed, in which automatic control strategies facilitate effective utilization of daylight penetration in indoor spaces. In addition, these control strategies are potentially responsible for improving an occupant’s productivity and well-being by preventing discomfort risks while keeping energy in control. This paper reviews simulation-based studies which employed automatic shading control methods for balancing human comfort and energy savings. The main aim of this research is to review the existing literature and identify research gaps in controlling AFs as a pilot study for future investigations. The review basically focuses on simulation approaches towards evaluating the performance of an automatic shading control that employs either open-loop or closed-loop control algorithm. The review concludes that existing studies only investigated automatic shading controls for typical AFs such as roller shades or venetian blinds that could not deliver multi-objective control over diverse human comfort perspectives along reducing energy consumption simultaneously.
Elie Azar
added a research item
Buildings play a dominant role in global efforts towards energy consumption reduction, greenhouse gas (GHG) emission mitigation, as well as global clean energy transition. Building Energy Policies (BEP) improved globally and quickly with a growing number of building codes implemented over the past decade. Occupant Behavior (OB) has significant impacts on building energy performance and occupant comfort, despite often being not well understood and oversimplified in BEPs. This paper highlighted the research needs of properly integrating OB in building energy polices by presenting a literature review to identify the key questions and challenges related to building technical standards and regulations, building information policies, building energy incentives, and policy evaluations and way forward. Challenges and opportunities of OB in BEP are also discussed with respect to technical innovation and digitalization, as well as concerns related to energy efficiency and fairness. There has been growing interests, research and applications in this field, but significant challenges and opportunities still lie ahead.
Jakob Hahn
added a research item
In the last four decades several methods have been used to model occupants’ presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants’ actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.
Yuzhen Peng
added a research item
Outdoor and indoor temperature prediction of local buildings is important for optimal building operation and energy-demand management. This study collects data from a commercial building, covering outdoor and indoor climate, and variables of occupants and building system operation. Based on the selected data, two different data-driven methodologies using machine learning techniques are proposed to predict local outdoor and indoor temperatures at a high resolution. The proposed data-driven models with learning capabilities are based on k-nearest neighbor and artificial neural networks, showing good prediction performance for the case study building.
Yuzhen Peng
added a research item
Demand-driven building control is an emerging approach to mitigate the increasing pressures on buildings and facilities for requirements of energy and comfort services. This study proposes a framework that integrates online learning capabilities to make building systems adapt to occupants' actual energy and comfort demand. Based on the framework, two types of control strategies are developed: occupancy-based and thermal-preference-based demand-driven controls. Both of them have been implemented in an office building, keeping occupants in the loop of building operation under realistic conditions. This paper first introduces the proposed framework, and then presents two types of controls applied in for a case study. Lastly, lessons learnt from conducting them in the field tests are discussed.
Clayton Miller