Building Occupants' Comfort Levels Identified with POE and Visualized by BIM

Creating and maintaining a comfortable indoor environment is crucial for energy-efficient building operation. However, there is often a disparity between defined comfort conditions and occupants' perceived comfort. To address this, collecting occupants' feedback and evaluating building performance through post-occupancy evaluation (POE) surveys are essential. Building Information Modeling (BIM) can enhance the visualization of survey results by providing a digital representation of the building. This study aimed to utilize POE surveys, Dynamo, and Revit add-ins to identify, visualize, and communicate factors contributing to discomfort for building occupants. A POE survey was conducted with 51 respondents, assessing aspects such as temperature comfort, indoor air quality, visual comfort, acoustic comfort, and space adequacy. Results indicated that occupants were most dissatisfied with indoor air quality in summer and most satisfied with space adequacy. Dynamo, a visual programming tool, was employed to create a script that imported the survey results into each room, colorizing them based on the survey aspects. Additionally, Revit add-ins were developed using Microsoft Visual Studio and the C# programming language to import and present data from Excel files within the Revit model. This facilitated the visualization of sensor data in the same BIM environment. By conducting the POE survey, comfort levels were identified, and Dynamo scripts colorized the rooms in Revit to represent the comfort levels. The Revit add-ins further enhanced BIM's role as a unified and digital database, allowing the import and reading of sensor data. In summary, this research aimed to use POE surveys, Dynamo, and Revit add-ins to identify, visualize, and communicate factors contributing to discomfort for building occupants. The combination of these tools provided valuable insights into comfort levels and facilitated efficient visualization and communication of survey results within the digital building model

Improving building occupant comfort through a digital twin approach:A Bayesian network model and predictive maintenance method

This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems’ behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study’s results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings.publishedVersio

Sick building syndrome: are we doing enough?

Health and well-being are vitally important aspects of people centric building design and are the roots of productivity. Sick building syndrome (SBS) is a collection of factors that can negatively affect physical health in several ways. Besides physical health is also related to psychological well-being because the human body is one interactive biological system. This paper focuses on reviewing the current state of knowledge on building sickness syndrome which has been prevalent as a building illness since the 1970s especially in offices and schools. While the concepts of intelligent, smart and sustainable buildings have gained considerable attention during recent decades, there is now increasing attention being given to designing healthy buildings. This study provides a review about SBS symptoms. Several negative effects of SBS are identified and potential solutions are advocated. Finally, the study stresses the role of built environment and concludes that ongoing research towards tackling SBS and developing healthy indoor environments should not be limited to a single formula as any health-related building design approach is dependent on several interacting factors

Building information modeling for facility managers

A Decision Support System (DSS) can help facility managers to improve building performance, occupants’ comfort, and energy efficiency during the Operation and Maintenance (O&M) phase. These DSSs are normally data-intensive and have specific data requirements. Building Information Modeling (BIM) has the potential to advance and transform facilities O&M by providing facility managers with a digitalized virtual environment that allows them to retrieve, analyze, and process such data. However, the implementation of BIM in O&M phases is still limited. The majority of issues in the BIM-O&M context lie in the interoperability between different software that requires different data structures and formats. In a BIM environment, there are issues associated with extracting, storing, managing, integrating, and disseminating data so that interoperability is assured. Considering the aforementioned aspects, the aim of this thesis is to enable interoperability between BIM models and the DSSs for building performance aspects such as building condition, maintenance, and occupants’ comfort. This integration automatizes the data transfer process which can assist Facility Management (FM) team in properly establishing the necessary measurements to moderate the negative consequences on buildings and thereby improve their performance and occupants’ comfort. The approach can also provide FM teams with an effective platform for data visualization in a user-friendly manner that can assist in integrating digital insights into FM decision-making processes and converting them into positive strategic actions. The proposed approach is validated in existing software as a case study. It is possible to demonstrate the applicability of this approach by ensuring that its interactions and outcomes are feasible using case studies. Case studies also identify how much the task efficiencies are in comparison with the manual method, helping facility managers to optimize operation strategies of buildings in order to enhance their performance. Verification tests are also performed on the information exported from a software program. The results demonstrate an efficiency increase in high-quality FM data collection for different kinds of DSS, reducing the time and effort that the FM team spends on searching for information and entering data. A Dynamo script is designed to allow administrators to include as much information as they wish in BIM models. Moreover, a novel approach is proposed to create a new category in BIM to assist public and business administrations with managing assets efficiently. In addition, building performance aspects can also be analyzed using the proposed method of integrating occupants' feedback into BIM models. By implementing the proposed approach, FM teams are able to correctly establish measurements which can be applied to mitigate the negative effects on buildings, thus improving their performance and enhancing their occupants’ comfort. Besides, the proposed approach enables BIM to be a more useful tool for visualization by using the most appropriate charts and formatting.Un Sistema de Soporte de decisiones (SSD) puede ayudar a los gestores de edificios a mejorar su rendimiento, su eficiencia energética y el confort de sus ocupantes. Para el buen funcionamiento de los SSD se requieren muchos datos. El Building Information Modeling (BIM) permite mejorar la gestión de las operaciones y el mantenimiento de los edificios al proporcionar un entorno virtual digitalizado que permite recuperar, analizar y procesar los datos requeridos por los SSD. Sin embargo, la implementación de BIM en las fases de Operación y Mantenimimento (O&M) aún es escasa. La mayoría de los problemas en el contexto de BIM-O&M radican en la interoperabilidad entre diferentes programas que requieren diferentes estructuras y formatos de datos. En un entorno BIM, existen problemas asociados a la extracción, el almacenamiento, la gestión, la integración y la difusión de datos para garantizar la interoperabilidad. Teniendo en cuenta los aspectos antes mencionados, el objetivo de esta tesis es facilitar la interoperabilidad entre los modelos BIM y los SSD relacionados con el rendimiento de los edificios, su estado de conservación y el confort de los ocupantes. Esta integración automatiza el proceso de transferencia de datos que puede ayudar a los gestores de edificios a establecer correctamente las medidas necesarias para mejorar su rendimiento y el confort de sus ocupantes. Esta integración también va a proporcionar a los gestores de edificios una plataforma eficaz para la visualización de datos de una manera fácil de usar que puede ayudar a integrar resultados de los SSD y convertirlos en acciones estratégicas. Para demostrar la aplicabilidad y la eficiencia de este integración, ésta se valida a través de casos de estudio. También se realizan pruebas de verificación sobre la información exportada en los diferentes sistemas. Los resultados demuestran un aumento de la eficiencia en la recopilación de datos de alta calidad para diferentes tipos de DSS, lo que reduce el tiempo y el esfuerzo que los gestores de edificios dedican a buscar información e introducir datos en la diferentes aplicaciones. Un script de Dynamo está diseñado para permitir que los gestores incluyan tanta información como deseen en los modelos BIM. Además, se propone un enfoque novedoso para crear una nueva categoría en BIM para ayudar a las administraciones públicas y empresariales a gestionar los activos de manera eficiente. Además, los aspectos del rendimiento del edificio también se pueden analizar utilizando el método propuesto de integrar los comentarios de los ocupantes en los modelos BIM. Al implementar el enfoque propuesto, los gestores de edificios pueden establecer correctamente las medidas que se pueden aplicar para mitigar los efectos negativos en los edificios, mejorando así su rendimiento y el confort de sus ocupantes. Además, la integración propuesta permite que BIM sea una herramienta más útil para la visualización mediante el uso de los gráficos y las opciones de formato más apropiados, guiando a la toma de decisiones para gestionar los edificiosPostprint (published version

Post-Occupancy Evaluation and IEQ Measurements from 64 Office Buildings: Critical Factors and Thresholds for User Satisfaction on Thermal Quality

The indoor environmental quality (IEQ) of buildings can have a strong influence on occupants’ comfort, productivity, and health. Post-occupancy evaluation (POE) is necessary in assessing the IEQ of the built environment, and it typically relies on the subjective surveys of thermal quality, air quality, visual quality, and acoustic quality. In this research, we expanded POE to include both objective IEQ measurements and the technical attributes of building systems (TABS) that may affect indoor environment and user satisfaction. The suite of three tools, including user satisfaction survey, workstation IEQ measurements, and TABS in the National Environmental Assessment Toolkit (NEAT) has been deployed in 1601 workstations in 64 office buildings, generating a rich database for statistical evaluation of possible correlations between the physical attributes of workstations, environmental conditions, and user satisfaction. Multivariate regression and multiple correlation coefficient statistical analysis revealed the relationship between measured and perceived IEQ indices, interdependencies between IEQ indices, and other satisfaction variables of significance. The results showed that overall, 55% of occupants responded as “satisfied” or “neutral”, and 45% reported being “dissatisfied” in their thermal quality. Given the dataset, air temperature in work area, size of thermal zone, window quality, level of temperature control, and radiant temperature asymmetry with façade are the critical factors for thermal quality satisfaction in the field. As a result, the outcome of this research contributes to identifying correlations between occupant satisfaction, measured data, and technical attributes of building systems. The presented integrated IEQ assessment method can further afford robust predictions of building performance against metrics and guidelines for IEQ standards to capture revised IEQ thresholds that impact building occupants’ satisfaction.</jats:p

The ambivalence of personal control over indoor climate - how much personal control is adequate?

Literature sets personal control over indoor environmental conditions in relation to the gap between predicted and actual energy use, the gap between predicted and observed user satisfaction, and health aspects. A focus on building energy performance often leads to the proposal of more automated and less occupant control of the indoor environment. However, a high degree of personal control is desirable because research shows that a low degree (or no) personal control highly correlates with indoor environmental dissatisfaction and sick building syndrome symptoms. These two tendencies seem contradictory and optimisation almost impossible. Based on current efficiency classes describing the effect of room automation systems on building energy use during operation, fundamental thoughts related to thermophysiology and control, recent laboratory experiments, important lessons learnt from post-occupancy studies, and documented conceptual frameworks on the level of control perceived, we discuss the ambivalence of personal control and how much personal control is adequate. Often-proposed solutions ranging from fully automated controls, over manual controls to dummy controls are discussed according to their effect on a) building energy use during operation and b) occupants perceived control. The discussion points to the importance of adequate personal control. In order to meet the goals for nearly zero energy buildings and for a human-centric design, there is the need to establish design procedures for adequate personal control as part of the design process

Human factors in the design of sustainable built environments

Scientific research provides convincing evidence that climate change is having significant impacts on many aspects of life. In the built-environment domain, regulatory requirements are pushing the challenges of environmental, economic, and social sustainability at the core of the professional agenda, although the aims of carbon reduction and energy conservation are frequently given a priority over occupants' comfort, well-being, and satisfaction. While most practitioners declare to embrace sustainability as a driver of their professional approach, a general lack of integrated creative and technical skills hinders the design of buildings centred on articulate and comprehensive sustainability goals, encompassing, other than energy criteria, also human-centred and ethical values founded on competent and informed consideration of the requirements of the site, the programme, and the occupants. Built environments are designed by humans to host a range of human activities. In response, this article aims to endorse a sustainable approach to design founded on the knowledge arising from scholarly and evidence-based research, exploring principles and criteria for the creation and operation of human habitats that can respond to energy and legislative demands, mitigate their environmental impacts, and adapt to new climate scenarios, while elevating the quality of experience and delight to those occupying them

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces

A multidisciplinary research approach to energy-related behavior in buildings

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the “performance gap” in the building sector requires a deeper understanding and consideration of the “human factor” in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants’ interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency – to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research