Continuous data-streams for occupant feedback on indoor climate: Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

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ISBN 978-82-326-6946-2 (printed ver.) ISBN 978-82-326-6060-5 (electronic ver.) ISSN 1503-8181 (printed ver.) ISSN 2703-8084 (online ver.)

Doctoral theses at NTNU, 2021:129

Niels Lassen

Continuous data-streams for occupant feedback on indoor climate

Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

Doctoral thesis

Doctoral theses at NTNU, 2021:129Niels Lassen NTNU

Norwegian University of Science and Technology Thesis for the Degree of

Philosophiae Doctor Faculty of Architecture and Design Department of Architecture and Technology

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Niels Lassen

Continuous data-streams for occupant feedback on indoor climate

Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

Thesis for the Degree of Philosophiae Doctor

Trondheim, April 2021

Norwegian University of Science and Technology Faculty of Architecture and Design

Department of Architecture and Technology

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NTNUNorwegian University of Science and Technology

Thesis for the Degree of Philosophiae Doctor Faculty of Architecture and Design

Department of Architecture and Technology

ISBN 978-82-326-6946-2 (printed ver.) ISBN 978-82-326-6060-5 (electronic ver.) ISSN 1503-8181 (printed ver.)

ISSN 2703-8084 (online ver.) Doctoral theses at NTNU, 2021:129 Printed by NTNU Grafisk senter

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Continuous data-streams for occupant feedback on indoor climate

Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

Niels Lassen

Dissertation submitted for the partial fulfilment of the requirements for the joint degree of philosophiae doctor

Department of Architectural Design, History and Technology

Faculty of Architecture and Fine Arts Norwegian University of Science and Technology

Norges teknisk-

naturvitenskapelige universitet

Avdeling Klima, Energi og Bygningsfysikk

Skanska Teknikk Skanska Norge AS

January 2021

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Abstract

The “fourth industrial revolution” has brought technologies such as artificial intelligence, robotics, the Internet of Things and Big data analytics and enhances the flow of data between users, products and developers. Digital solutions bring the possibility for data harvesting close to each user, capturing personal preferences and collecting valuable information on how products are viewed, used and adopted to the benefit of individualized operation, tuning, and improved iterative design processes. Although buildings have become increasingly advanced both technically and even digitally, the benefits of a closer connection between developers, operators and users cannot yet be said to have reached its potential. The cost, emissions and complexity of such buildings is an increasing problem, and so-called “performance gaps”

between design and actual performance have been identified for user comfort and

satisfaction, energy use, and operation and maintenance costs. Enabling user control has been shown to have a positive impact on overall user satisfaction. Improved user satisfaction is associated with increased employee productivity and a decreased rate of employee turnover.

As evident in other industries, modern technology has the power to collect valuable data from subjective occupant feedback and could empower better and more informed choices in building design, research, tuning and control. A variety of systems and methods for occupant- centric data collection exist, and the data collected by them will differ in both application and quality. Many of the systems and approaches to involve and collect occupant-centric data regarding indoor climate that have been tested in recent research studies show promising results for predicting occupants wishes, creating personal comfort models or controlling the indoor climate.

This doctoral thesis is founded on the idea that one, by utilizing the latest technology for pervasive computing and sensing, can close the gap between buildings and occupants. By continuously monitoring the users’ subjective experience of the indoor climate, the building automation system can respond directly or indirectly to the wishes made by conscious users rather than to pre-set physical parameters. One will at the same time be able to collect, analyze and learn how a building is used by the occupants it was intended for, and how it may be improved to reach higher user satisfaction and reduce construction costs, energy use, and environmental impact. The main aim of the thesis is to understand howlow cost, non- intrusive methods of subjective occupant feedback regarding indoor climate can improve building design, research, tuning and control of commercial buildings.

The research activities have been 1: study the background and potential of continuous subjective feedback for design applications; 2: investigate the theoretical differences between different known modes and levels of subjective feedback in relation to human psychology and develop a taxonomy; 3: design a multi-level system for use in commercial buildings; 4: test the system and investigate how the system is used by un-informed occupants in 5 field tests in functioning (‘real’) office buildings; 5: uncover the validity of the collected data from the field tests compared to data simultaneously collected with conventional occupant surveys.

It was found that continuous subjective feedback has the potential to provide necessary knowledge and documentation to support holistic design choices and may contribute to closing the performance gaps in future buildings. A framework is presented for taxonomy of system types for occupant-centric data collection based on the theories of market- and

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environmental psychology. Results of five field tests performed in Norway and California are presented in three separate articles presenting quantitative and qualitative findings. The tests were performed with a total of 183 uninformed participants and collected a total of 5288 feedback instances from 4 different feedback methods. Deliberate changes were made to the indoor climate during the experiments, and 628 survey responses were collected from the occupants, as well as semi-structured interviews of occupants.

The thesis consists of five journal publications covering the focus areas mentioned above. A framework is presented for taxonomy of systems types for subjective feedback based on the theories of market- and environmental psychology. Results of five field tests performed in Norway and California are presented in three separate articles presenting quantitative and qualitative findings. Finally, the thesis concludes by summarizing how low cost, non-intrusive methods of subjective occupant feedback can be expected to perform for improving building design, research, tuning and control of existing and future commercial buildings. Several recommendations for future research are presented.

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Acknowledgements

This study has been performed with the financial support of the Research Council of Norway, within the project “Methods for real-time user involvement of indoor climate in smart buildings” (project number: 277048). The support provided by the Research Council of Norway, Skanska Norway, Skanska US, University of California Berkeley, Technical University of Denmark, Snøhetta AS, Avantor, Asplan Viak and the Norwegian University of Science and Technology is gratefully acknowledged.

Further I would like to thank my main supervisor Francesco Goia at NTNU for great academical guidance throughout the thesis, as well as my mother Ann Karina Lassen for critical reading and childcare. I would also like to specially thank Terje Josefsen, Rune Stene, and Tor Helge Dokka at Skanska Norway, Kimberly Loscher and Nick Cherbero at the Skanska US San Francisco office, Stefano Schiavon and Jovan Pantelic at the Center for the Built Environment, UC Berkeley, Henrik Madsen and Jan Kloppenborg Møller at DTU Compute, Anne Danielsberg at Avantor, Øyvind Halnes at Stanley Securities, Erik Amundrud at KnowIt AS, Olav Rådstoga at Asplan Viak and Michael Thompson at UC Berkeley Facility Services.

Finally, I want to thank my wife Caroline and children Oda, Heidi and Ingrid for coping with my concentrated mind in the house and for letting me sit in the basement and write for all those late nights.

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List of acronyms

AI Artificial Intelligence

API Application Programming Interface BAS Building Automation System

BREEAM Building Research Establishment Environmental Assessment Method CSOF Continuous Subjective Occupant Feedback

FM Facility Management

GHG Green House Gas

HVAC Heating, Ventilation and Air Conditioning ICT Information and Communications Technology IED Integrated Energy Design

IEQ Indoor Environmental Quality

IoT Internet of Things

MAE Mean Absolute Error

OCC Occupant Centric Control

OCD Occupant Centric Data

OVS Occupant Voting Systems

PMV Predicted Mean Vote [-]

POE Post-Occupancy Evaluation

PPD Percentage People Dissatisfied

QR Quick Response (code)

RMSE Root Mean Squared Error

SBS Sick Building Syndrome

SPS Satisfaction Polling Station

SQL Structured Query Language

TSV Thermal Sensation Vote

ZEB Zero Emission Buildings

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1 Introduction

1.1 Motivation

The aims and motivation of this PhD work was developed by the author in collaboration with Skanska Norway, who also sponsored the work. The thesis answers to a potential which was identified through several years of design and research with passive or natural climatization concepts for zero energy buildings. During our work we realized that modern high-

performance buildings are designed to achieve measures that do not necessarily improve the experience of the users.

Modern buildings are designed according to discipline specific performance criteria, but there is no feedback loop from users to designers. Unlike design processes in many other fields, there is no iterative process between designer and user regarding the qualities of the product (building). This practice limits the ability to develop holistic concepts, and to learn from our mistakes. The performance criteria are normally developed with a basis in theoretical knowledge and research. Yet the experience of practitioners in the field are often not in support of these criteria, and it is a common opinion that buildings often do not perform at the level that was expected.

In fact, performance gaps are found and documented for energy use, occupant satisfaction and building operation. In many projects this performance gap is neglected or concealed, as it is not in the interest of the actors of the project to address it. Modern buildings comply with strict criteria at great monetary and environmental expense, but relevant information from users is seldom collected or used to validate of improve the product performance.

Post-Occupancy Evaluations (POE) are today the established method for collecting subjective opinions from occupants. These spot measurements, that are distributed as voluntary questionnaires to occupants in some real buildings, have had a large impact on our knowledge about comfort and indoor climate preferences of occupants. For instance, the research and work with the Adaptive comfort model showed that the preferences of “real” occupants as reported in POEs was different from what was found for occupants in laboratory studies and used in other comfort models. This difference demonstrates the potential value of subjective data from real-life occupants in buildings. It proves that the human perception and evaluation of the environment is a highly subjective and psychological matter often influenced by factors unrelated to the physical indoor climate itself, such as for instance expectations, perceived control, architecture or culture. These are factors that may be individual to each building, tenant or occupant.

Despite of their value, it is found that POEs are rarely performed in buildings. Reasons may be that they are unpractical and costly to perform. Meanwhile, the current digital revolution has brought technologies, services and products that have opened new possibilities for collecting subjective (or objective) information close to the user. Data acquisition, ownership, access and processing have become some of the most prosperous industries of our time, with new business models emerging at a rapid pace. Digital solutions also bring the possibility for data harvesting close to each user, capturing their personal preferences and collecting valuable information on how products are viewed, used and adopted.

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For this reason, there was a desire to explore the possibilities for using modern technology to close the gap between occupants and building designers and operators. Along the way, other benefits of systematical collection of subjective occupant opinions in buildings have emerged.

In time, we expect that the information gathered from occupants with these new methods could lead to new knowledge, a better understanding of occupants in buildings and minimization of the gap that currently exists in automated buildings between the occupant and the building.

The purpose of this thesis is to find knowledge and solutions for gathering key data on the building's indoor climate and the user's subjective experience of this in a non-intrusive way, and to validate the accuracy of the information we collect.

Hopefully, the knowledge generated in this PhD can, directly or indirectly, contribute to a shift in the way we design and operate our buildings. Rather than creating complex buildings relying on standards, models, simulations and automation to ensure comfortable occupants and low energy use, we will create simple and robust buildings that interact with the occupants resulting in satisfied users and a drastically reduced environmental footprint.

1.2 Aims

The general aim of this thesis is to investigate how new technology can help realize the automated collection of subjective occupant opinions, and how these may help improve the functioning, quality and performance of both new and existing buildings. The research aim is to evaluate to what extent low cost, non-intrusive methods of subjective occupant feedback regarding indoor climate can improve the lifetime efficiency, robustness and footprint of existing and future commercial buildings. An additional aim of defining the different data types present in Occupant Centric Data collection was added during the course of the research, as a research gap emerged. In the research field of indoor climate control, and thermal comfort especially, there appears to be a strong belief in, and focus on, the use of occupant feedback for developing individual models to predict and control for optimal occupant comfort. This thesis takes the standing point that models are only one of several ways where continuous occupant feedback can be used to improve buildings. The focus of the thesis is therefore to investigate the occupant and data collection sides of occupant feedback, regardless of application.

In the research, focus has been laid on uncovering the potential, functioning and

representativeness of a limited selection of feedback methods in office buildings specifically.

Focus is on methods for subjective data collection, not on occupant responses to actual indoor climate. Although the usability and interaction design of the solution is of high importance for the results, this topic was only briefly studied without an in-depth study of the theoretical aspects of this field. The scope of the research is to uncover the potential, functioning and validity of a limited selection of feedback methods applicable in existing and future commercial buildings. The stakeholders or this research are believed to be researchers and practitioners looking to improve indoor climate through design and operation of buildings.

The field in question is rapidly growing and much work is done, both on the academical and commercial fronts. Hopefully, the findings in this research work can benefit future work on both these fronts. Expected readerships are researcher in the field of continuous occupant

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feedback and occupant centric data, designers of buildings and building control systems, as well as experts in the smart building and IoT fields.

1.3 Outline

The thesis is structured in seven sections. The first two sections (1&2) are the introductory part, presenting the motivation, outline and theoretical background of the thesis. The following two sections (3&4) present the research questions and research design of the thesis, before the research results of each of the five research questions are presented in the Results section (5). Finally, the findings and conclusions are discussed and summarized in the last two sections (6 and 7). The thesis covers five research activities which have a logical order, as shown in Figure 1.

Research activity 1 naturally functions as an introduction to the other research questions as it focuses on the potential of continuous subjective feedback. A special focus is laid on the potential of the concept for which we apply the term “Continuous Subjective Occupant Feedback” (CSOF) methods for improving holistic design in high performance buildings.

Research activity 2 aims to narrow down the problem by distinguishing forms and formats of subjective feedback. And we seek to differentiate subjective feedback from other forms of occupant centric data (OCD). It combines a theoretical study with a limited literature review of existing studies of Occupant Centric Data collection systems. We investigate existing literature and summarize state of the art. We create a hierarchy of human sensory processing based on theory of environmental psychology, market psychology and neuroscience, distinguishing between different levels or types of feedback.

In research activity 3 we suggest, based on the previous activities, the design of a low-cost and non-intrusive system for several types of subjective feedback, based on the previously developed hierarchy, for use in office buildings.

Research activity 4 focuses on the real-life application of the suggested system from activity 3 and reports how occupants respond to the system in field tests in 5 real office buildings in Norway and California. The tests were conducted as longitudinal blind tests over a total of 307 days, involving in total 183 occupants. Results include usage data, data from separate surveys, measurements of the physical environment as well as focus group interviews. Intentional stepwise changes (temperature interventions) were performed to test occupant responses under differing conditions.

The final research activity 5 focuses on the validity and representativeness of the data collected by the system, comparing the continuous subjective feedback collected in field tests to data collected in the surveys. Logistic regression models are used to investigate the statistical correlations and explanatory variables between collected feedback and survey data.

The thesis is concluded by summarizing the research findings and drawing the lines toward a total understanding of how CSOF methods can contribute to improve buildings design, tuning, operation and benchmarking, and how their accuracy compares with traditional methods. It also presents several recommendations for further study.

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Answers to the research activities has originally been reported in five research articles that can be found in Appendix A. The relation between research activities and publications is shown in Figure 1.

Activity 1 was answered in Article 1 “Enabling holistic design for high energy efficient office buildings by the use of subjective occupant feedback” [1] which has been submitted to the Journal of Sustainable Cities and Society with Manuscript No. SCSI-D-20-02948.

Activity 2 was answered in Article 2 “A theoretical framework for classifying occupant-centric data streams on indoor climate using a physiological and cognitive process hierarchy” [2]

which has been submitted to the Energy and Buildings Journal with Manuscript No. ENB-S-20- 00241.

Activity 3 was answered in the first part of Article 3 “Design and in-field testing of a multi- level system for continuous subjective occupant feedback on indoor climate” [3] which has been published in the Energy and Buildings Journal.

Activity 4 was answered in the second part of Article 3, as well as in Article 4 “Field

investigations of a smiley-face polling station for recording occupant satisfaction with indoor climate” [4] which has been published in the Energy and Buildings Journal.

Activity 5 was answered in Article 4 as well as in Article 5 “Design and in-field testing of a multi-level system for continuous subjective occupant feedback on indoor climate” [5] which has been submitted for publication in the Energy and Buildings Journal with Manuscript No.

BAE-S-21-00172.

Four conference articles [6–9] related to the subject have also been published by the author while working with the thesis.

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Figure 1 Schematic outline of research design with relevant publications.

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2 Background

2.1 Research domain

The topic of this thesis is a highly multidisciplinary one, as it exists in the crossroads between classical indoor climate theory and ICT but takes a meta-view by focusing on methods for interacting and collecting information from occupants. It touches upon the fields of indoor climate, thermal comfort, occupant behavior, physiology, biology, environmental psychology, interaction design, and Information and Communications Technology (ICT). Existing research of CSOF systems originates and categorizes under several research domains, and relevant research publications often originate from differing fields such as thermal comfort, indoor climate (such as lighting, air quality etc.), facility management, automation, building simulation, ICT, energy use in buildings and more. The topic is still new and research works are highly influenced by innovative approaches. A brief background is given here, although it will be impossible to go in depth in all the involved research disciplines and connect all the multiple domains.

The concept for which we apply the term “Continuous Subjective Occupant Feedback” (CSOF), also goes by several other names, although their meanings are not entirely aligned. Other studies have used terms such as “Occupant-centric Control” (OCC), “Occupant Voting Systems”

(OVS), “Occupant Feedback”, and “Human-in-the-loop HVAC operations”. The different terms refer to distinct and overlapping topics of similar systems, often defined according to the intended application if the feedback system. The concept of CSOF does not clearly belong to one established area of application, either. It is a relatively new concept and its existing applications are divided between several fields of application, such as building modeling, HVAC control, research, and facility management. Several new and undiscovered applications may also emerge.

The emerging research fields of Occupant Behavior, Occupant Centric Control, and Human-in- the-loop HVAC focus on how modern technology can be used to gain a better understanding of how occupants behave in real buildings, or to use occupant centric data to control HVAC in buildings. They involve:

x Occupant movement and location tracking (i.e. mobile positioning, presence) x Physiological monitoring (i.e. skin temperature, heartrate & galvanic skin response

from wristbands, pupil size tracking)

x Occupant control behavior (i.e. smart thermostat interactions, use of personal comfort systems)

x Voting and subjective feedback from occupants (i.e. polling stations, smartphone apps)

In most cases, the focus is directed toward how data from occupants can help inform building control operations, facility management, or models and simulations for predicting building performance. A smaller fraction of the existing research investigates how the data collection methods themselves perform, how they affect the occupants, or the validity of the collected data. These kinds of more “in-depth” investigations of occupant responses to environmental stimuli and how the occupant reactions can be measured are often found in the more traditional fields of thermal comfort (for the thermal domain), but mainly this knowledge is found in the fields of environmental psychology, psychology, physiology and neuroscience.

The study of more “higher order” occupant reactions and decisions, such as occupant

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satisfaction and use of interfaces, is often found in the fields of market psychology, consumer psychology and interaction design.

In this thesis, it has been a high priority to keep a holistic and multidisciplinary approach in order to, in depth, understand and describe the underlying mechanisms and functioning of CSOF systems.

2.2 Theoretical foundation

2.2.1 Current status for occupant involvement in buildings

Buildings need to meet targets in relation to energy efficiency, as well as provide a healthy, comfortable and productive indoor environmental quality. As demands to decrease the use of energy become more prominent, these coincide with increasing demands for comfort in buildings [10]. Although handled by separate design disciplines, these performance areas are highly intertangled.

Studies have found that there exists a large performance gap between design (simulated) and actual (measured) energy performance [11]. The performance gap is not only evident for energy performance, but is also found to exist for occupant comfort and satisfaction [12] and for facility management and maintenance costs [13,14]. In a comparison of 12 field studies from the United States and six countries in Europe, covering 467 buildings with approximately 24 000 total occupants, the air-conditioned buildings showed between 30% and 200% more cases of sick building syndrome (SBS) symptoms than in the naturally ventilated buildings’

[15]. The causes for these performance gaps can be many. One study emphasizes the lack of operational understanding in the design phase as an important factor for the energy and facility management related gaps [13]. Another study identifies the dominant factors to be related to specification uncertainty in modeling, occupant behavior, and poor operational practices [16]. It has been emphasized how causal factors for the energy performance gap often relate to the use of unrealistic input parameters regarding occupant behavior and facility management in building energy models, further associated with the lack of feedback to designers once a building has been constructed and occupied [17]. The information flow in the building operation phase has been pointed out to be detached from the other parts of a building’s lifetime [18].

Several studies have emphasized factors and phenomena related to human psychology as causes for performance gaps related to occupant comfort and satisfaction. Madsen & Gram- Hansen emphasize the role that technology plays in shaping our habits, and how occupant expectations and habits change when they are introduced to new and advanced technology. In the same way, technology and the possibilities and expectations that follow with it may shape and affect the occupant’s evaluations of comfort or satisfaction [19]. Expectations are found to heavily influence occupant’s perceptions, comfort and satisfaction with the indoor climate, which has been particularly investigated for the thermal domain [20–23]. Building design and technologies are found to influence thermal expectations and heat-related habits, which coincides with the theories of practice describing how materiality affects practices through reconfiguring practical understandings, for example comfort expectations [24]. Automated buildings rely on automated and actively controlled systems to maintain the desired indoor environmental conditions. Occupant interactions with building systems are often discouraged,

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as the building operators want to avoid occupants disturbing the set-points in the finely tuned systems [25]. A growing body of literature has, however, found that full automation of indoor climate measures come at a great risk - occupant tolerance for discomfort is significantly reduced as occupant control possibilities are removed [26–28].

On the contrary, several studies have found that the opportunity for occupants to interact with their indoor environment and with the building operation in general positively affects their satisfaction [29–31]. The adaptive principle for thermal comfort describes how people, when experiencing discomfort, “react in ways that tend to restore their comfort” [32]. It recognizes that a person is not a passive receiver of sensations and perceptions but is an active and dynamic participant in a system for maintaining a thermal equilibrium with the environment.

The ability to control your personal environment is found to be of great significance for occupant satisfaction with thermal comfort [25,29,33–35]. Comfort expectations and the availability and constraints of effective control are clearly important measures in this context [29], although they are normally not considered in building design. Studies have also found that occupants’ perceptions of the different indoor environmental quality domains are affected by other domains. One example of this is how thermal comfort perception is influenced by the light color and intensity [36–38]. Another example can be found in the thermal comfort domain. Based on established theories and models, a very small temperature range of about 1-2°C is usually adopted as best-practice in existing office buildings to ensure occupant thermal comfort [9,23,39]. Studies have, however, found that only 8 % of buildings in the ASHRAE Global Thermal Comfort Database II meet the threshold of 80% satisfied occupants (as intended in ISO 7730), if one includes votes from 0 to +3 (‘neutral’ to ‘very satisfied’). In total 43% of the occupants were thermally dissatisfied, 19% neutral and 38%

satisfied [40]. In this case, there is a great deal to be learned by studying subjective information from the operation phase. The feedback from occupants is used for immediate changes of IC, but seldom as a systematic tool for long-term improvements [9]. When the occupants have to submit their complaints via systems with several hidden layers (for example facility management call centres), or when it is unclear how to submit the complaint, we imagine that occupants may find it difficult to provide feedback and lose interest in doing so. Tight design set-points and limited user-feedback can have large effects on energy use, user satisfaction, and investment costs [41]. Such tight bandwidths are probably adopted to prevent complaints from occupants [9,23,39]. These demands and practices lead to a need for equipment with large heating and cooling capacities, high power peaks, and high energy use [42]. At the same time, several field studies have found that narrow temperature bands do not necessarily lead to higher occupant satisfaction with the thermal environment [43,44].

2.2.2 Occupant behavior

The term subjective is defined as “based on or influenced by personal feelings, tastes, or opinions” [45]. Subjectiveness, or subjective feedback, is referred to in a large number of studies in the indoor climate field [46–50], but the term is rarely elaborated on or defined. If a building occupant or laboratory test subject is to provide information about his or her perception of the indoor climate, the information will in all cases be subjective. This is because the individual has made an active decision and chosen what to answer through a cognitive thought process. If the goal of providing thermal comfort in buildings is to have satisfied occupants, then the comfort performance can only be verified via subjective evaluation.

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The recent research field of Occupant Behavior focuses on the fact that occupants can influence energy consumption. With tightening requirements regarding building energy performance and sustainability, researchers, architects, planners, engineers, and building managers have begun to recognize the importance of understanding building occupants’

behavior. Focus is laid on collecting data that sheds light on occupant behavior in actual buildings [51]. Some of this data can be said to be subjective occupant feedback and can answer the same questions as those asked in POEs, without the cost of the POE studies. The data may also be continuous, adding additional value compared to POE studies, which are taken at one point in time. Subjective data must originate from occupant actions, who again relate to how occupants interact with their building. One can distinguish between adaptive and non-adaptive occupant action triggers, as well as contextual factors. Adaptive triggers or actions are defined as those rooted in occupant discomfort (or expectation of discomfort), such as opening a window, closing the blinds or making a complaint. Non-adaptive triggers or actions are those that are part of occupant’s tasks, such as shutting off the light when leaving the room, closing the window before going home, or answering a questionnaire. Contextual factors can be grouped into physical environmental factors; psychological factors, which are related to individual and social factors; and physiological factors. Several examples of contextual factors that are highly relevant for building design choices are mentioned in the literature, such as physical factors: building quality, availability of controls, interior design, ease of use of system interfaces, view to outside, feedback effects of control; psychological factors: knowledge, expectations, awareness; and social factors: ownership of building [52].

Users are heavily affected by the context in which they interact with the building and experience the indoor environmental quality.

2.2.3 Methods for occupant involvement

Post Occupancy Evaluations (POE) are currently the only established method for collecting subjective evaluations from building users. POEs have been described as the most people- oriented method for analyzing architectural spaces and can play a role in attempts to determine an acceptable balance between creativity and utility in construction projects [53].

This can be done by bringing in the element of user satisfaction as well as the actual functioning of the building in the use phase [54]. The subjective data gathered in POEs can integrate the pre- and post-handover phases in the building life cycle; the various

stakeholders in the building process, the various building disciplines, practice with research, subjective and objective dimensions of building use and experience, and lastly to bridge the static performance of the building with the dynamic functioning when real users interact with and modify static features [54]. However, despite the wide declared interest on them, POEs are rarely used in the building industry [55]. The most common explanations for this are that POEs are expensive and that conducting POEs may uncover legal liabilities [56].

In addition to POE questionnaires, subjective feedback may also be collected non-intrusively through occupant control actions that can provide important information about the subjective preferences of the occupant if held up against information about the current physical ambient conditions. Another potential data source for subjective opinions are the fields of

Participatory Sensing or Participatory Comfort Control, which aim to let occupants in a democratic manner vote for their wished indoor climate settings using their smart phone. By using modern sensor and communication technology, it is possible to collect data from several of the strategies and actions occupants use to interact with their environment, and thus their

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preferences. This type of data is often called Human-in-the-loop data, defined as: “Occupancy and/or behavior data that are collected with humans involved in measurement and recording –knowingly or unknowingly– that are comprised of studies where a researcher manually records occupants as well as studies that use active engagement of occupants in their own recording (e.g., using thermostat interactions to collect data)” [52].

Recent developments in information and communication technology have enabled new forms of building interfaces, control concepts and occupant feedback methods.

The concept of the Internet of Things (IoT) represents the overarching framework of a digital revolution that has unfolded during the last 10-15 years. The term generally refers to the concept of network-addressable devices embedded in everyday objects, allowing them to invisibly interact. This technological evolution has made advanced Information and Communication Technology (ICT) functionality accessible, affordable and possible to implement into a context suitable for continuous occupant feedback. Many new technologies and methods for occupancy and occupant behavior sensing and data acquisition have been developed during the latest years. Several recent research approaches related to smart building [57,58] have investigated continuous occupant-centric data collection, such as participatory sensing apps where occupants voluntarily provide feedback through a

smartphone app [59–61], internet enabled thermostats, or wearable and static devices where control behavior is tracked and logged [62–64]. Both participatory sensing apps and more generalized apps for occupant feedback and control are already available on the market today as Smart building solutions.

2.2.4 The emerging research field of continuous occupant feedback The new technologies and solutions mentioned above, and research related to them, can be grouped together to form a new and emerging research field that encompasses the use of ICT technology to continuously collect objective and subjective data regarding the environment on a personal level for each occupant. This general concept has in this thesis been named Occupant Centric Data (OCD).

The research field of Occupant Behavior has already been mentioned in a previous paragraph.

The current IEA Annex 79 “Occupant-Centric Building Design and Operation” [65,66] covers this field and drives several current research activities regarding occupant involvement in buildings. One major focus of this research work is the collection and use of OCD for research and modeling purposes. The emerging field of Occupant Centric Control (OCC), recently reviewed by Park et al. [67] refers to the control of building systems based on

presence/absence data, data from the environment (e.g., illuminance, temperature, humidity, CO²) in conjunction with human-building interactions (e.g., use of light switch, window, blind, or thermostat, etc.). The study highlights the following potentials of these approaches:

x Personalization/individual control/human-in-the-loop x Energy saving (detect unoccupied spaces)

Jung and Jazizadeh [68] reviewed Human-in-the-loop HVAC operations, referring to human interactions related to the dynamics of occupants in indoor environments (e.g., occupancy and thermal comfort). Most recently Khan et al. [69] presented a thorough review of Occupant Voting Systems (OVS) including a framework for characterization. OVS is in that case defined as “a system using information and communication technology that occupants can use at any

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given time to provide continuous and real-time feedback on their perception of IEQ” and does not include information gathered from occupant control actions.

Several scientific field- and laboratory studies exist of CSOF systems. The most relevant are given in Table 1.

Table 1 List and key information on selected studies investigating systems collections of continuous subjective data

Ref.

No.

Interface type Participants Research focus [70] Common Polling station,

Smiley face buttons

Uninformed visitors, Unknown number

Presents a method to conduct continuous longitudinal studies of IEQ and occupant comfort

[71] Common LCD screen thermostat showing current room temperature.

Informed, 7

Explores the means of making HVAC systems respond automatically to local occupant temperature preferences [72] Personal manual

thermostat control

Informed, 38

Explores use of feedback with a personal comfort system (PCS) to learn occupants’ heating and cooling behaviour for the development of personal comfort models [73] Personal digital interface,

widget and WebApp

Uninformed, Approx. 4300

Tests a software tool that solicits thermal feedback from students, and analyses its impact on energy use and energy management procedures [63] Personal Smartphone App Informed,

1 +1 occupants

Tests a mobile application for thermal preference feedback for training personal models

[74] Personal tablet with 3 different levels of automation

Informed, 30 households

Occupant perspectives from testing smart thermostats that automate heating based on occupants’ heating preferences and real-time price variations.

[75] Personal Smartphone App Informed, 61

Proposes 5 application feedback types that use various methods of data presentation and environmental stimuli to promote specific behaviour.

Occupant perspective.

[76] Smartphone App Informed, 49

Energy savings potential is examined in context with occupant subjective feedback.

[59] Website or personal smartphone app

Informed, 60

Satisfaction and energy saving by implementation of a system that integrates building occupants’

personalized thermal profiles into the HVAC control logic.

[77] Touch screen, personal single office

Informed, 6

Test a data-driven learning method to implement personal models with thermal complaint behaviour in a control system.

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Ref.

No.

Interface type Participants Research focus [78] Mini interaction device

registering preferences, mobile application for alternative interface

Informed, 12

Occupant responses from testing a system that allow occupants to report subjectively perceived comfort levels.

[79] Smartphone App Informed, 4

Tests framework to integrate building occupants in the HVAC control loop and control the HVAC system based on personal comfort profiles [60] Personal Smartphone App

/ web interface

Partially uninformed, 12

Tests satisfaction and energy use with system that captures occupant’s favourite temperature non-intrusively and optimizes the set-point

temperature with a model [80] Personal Smartphone App Informed,

20

Tests satisfaction and energy use framework to integrate building occupants in the HVAC control loop and controls the system directly [81] Personal Smartphone App Informed,

4

Tests satisfaction and energy use with framework to integrate building occupants in the HVAC control loop and controls the system based on personal comfort profiles [82] Personal WebApp Informed

65

Tests energy use with an embedded sensing and information management architecture that provides for effective participation by the building occupants.

[83] Personal Polling station at desk

Informed, 44

Tests occupant usage of a personal polling station with embedded sensors [84] Personal Smartphone App Informed,

39

Tests satisfaction and energy use with system for participatory voting.

Demonstrate a learned and real-time method of utilizing occupant data.

Tests communication platform, which enables occupants to communicate preferences to building control system.

Demonstrates how occupants can be clustered into comfort personality types for prediction and recommendation systems [87] Personal Smartwatch App Informed,

15

Demonstrates how large data sets of human feedback can be analysed to reveal building anomalies, occupant behaviour, occupant personality clustering, and general feedback related to the building

Case study implementation of app for Activity Based Working (ABW) allocation platform demonstrating how

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13

Ref.

No.

Interface type Participants Research focus

occupants can be classified into specific types who can be matched.

Discusses methodological aspects of the photograph-based smartphone post- occupancy evaluation for collecting qualitative results

The above mentioned studies cover a variety of methods for occupant centric data collection.

The various types are described below, categorized by type.

Physical and spatial data: The review of occupant-centric control by Park et. al [67] found that over half of the studies studied so-called occupancy based occupant centric control, meaning that they focus on measured presence/absence of the occupant for control purposes. When the objective was detecting occupancy counts, motion detectors were either complemented with other types of sensors such as CO2, acoustics, plug loads, chair sensors, camera-based motion detectors, signals from Bluetooth and WiFi-enabled devices, RFID [90] or a fine grid of motion detectors were used for indoor localization. Temperature sensors, PIR sensors [91,92], cameras, wearable devices, smartphones, ultrasound and other types of devices may also be used for collecting presence or motion data [93].

Data of physiological reactions of the occupants: Continuously sampled information on body- level quantities can be linked to and used to describe the functioning of the human body. In most, cases wearable devices such as smart watches or wristbands[87,94], or even mobile phones and body sensors [64], are used to monitor physiological parameters such as heart rate, activity, skin temperature, and electrodermal activity (galvanic skin response). In other cases, fixed infrared cameras at the workstations have been used to monitor facial skin temperature to predict thermal comfort [95,96], or 3D scanning devices or motion cameras are used to monitor occupant activity [97] or even body posture or facial expressions. Data streams from occupant physiology have been shown to make a significant improvement in predicting the comfort wishes of individuals, often by developing personal comfort models [48]. These approaches are currently at the research stage and seldom used in commercial buildings. A certain number of studies, such as [94–98] investigate how physiological reactions in occupants measured with wearable sensors or thermo-imaging can be used for predicting occupant preferences, thus explicitly linking physiological quantities to occupant experience of the indoor climate.

Data from occupant control actions: New sensor and wireless communication technology has also made possible a development in data collection from the control actions performed by the occupants. As the price, size, and convenience of wireless sensing equipment has improved, it has become possible to gather information for occupant interactions with windows and personal environmental control devices such as heaters and fans [99], in addition to the possibility to record though the building management systems the changes operated by the occupants on set-point values for example though thermostats. In some cases, furniture such as office desks and chairs have incorporated personal heaters and fans, as well as internet connection providing usage data [62].

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Data from occupant complaints: Participatory sensing or participatory comfort control lets occupants control, in a democratic manner, the ambient temperature (which gives no immediate change) via the HVAC system using, for example, their smart phones. The key idea behind participatory sensing is to empower ordinary people to collect and share experiences from their surrounding environments using own devices or simple personal interfaces [100].

The individual differences in use of the subjective voting solution result in potential challenges related to fairness between occupants [101]. Even though participatory sensing and control solutions in theory are continuous data streams, they may in practice not be continuous for individual users if they do not make use of the solution on a regular basis.

Several research studies have been conducted on the concepts of Participatory Voting and Participatory Control used in an indoor climate setting [63,84]. In addition, there are some commercial products available who utilize Participatory control in office buildings [102].

Some systems have combined a Participatory Sensing or complaint feedback functionality with more operational or facility management related feedback where occupants can use smartphones to report complaints or problems to facility operators [89] [73].

Data from satisfaction evaluations of the occupants: Some surveys or voting polls are aimed at collecting voluntary evaluative responses from a representative number of users, and usually targeting the level of satisfaction that the occupant/user assesses. Most often this is done through smartphone apps, smartwatch apps or by polling stations (fixed button or touch screen). They can be directed at the individual user (such as apps or personal polling stations), or at the public (as publicly accessible polling stations placed in an environment where the user passes by). Publicly located smiley-face polling stations have recently had a rapid growth for capturing customer satisfaction in airports, retail, public facilities and healthcare, and the survey responses are entered by single presses at smiley face buttons ranged from “Angry” via

“Neutral” to “Happy”. The concept relies on the low cost in time and effort for users to enter their response, resulting in higher response numbers. The concept has only been tested in a small number of research studies for application in indoor climate in buildings [4,70].

2.2.5 Occupant interfaces

User Experience (UX) design refers to shaping the experience of using a product. It involves interaction design, user research (finding out who the users are) creating user personas (why, and under what conditions, would they use the product), performing user testing and usability testing, etc [103]. The above-mentioned studies cover systems with several different user experiences and occupant interfaces. The field of interaction design covers the interaction between humans and products. It is heavily studied for the application of digital and web development, but also other consumer products. The interaction between a user and a product often involves elements like aesthetics, motion, sound, space, and many more [103].

With the development of more advanced machines, machine learning techniques, and artificial intelligence (AI) has come a shift in the perception of how humans interact with machines.

Rather than just using machines, we now interact with them as well [104]. The “five dimensions” of interaction design are often used to describe interaction design more closely:

Words: How are words used to communicate clearly and efficiently? Visual representations:

How does the visual presentation communicate the intended information? Physical objects or space: Through what physical objects do users interact with the product? Within what kind of physical space does the user do so? Time: is the amount of time a user spends interacting with the product appropriate? Behavior: How do users perform actions on the device, how do they

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operate it, what are their reactions, emotional responses of feedback? The qualities of an interaction determine a user’s overall experience of and satisfaction with it [105]. Few studies connect the existing knowledge on interaction design with design of occupant feedback systems. There are, however, some examples within the field of thermostat design, such as [106,107].

In the field of personal control, the concept of constraints has been put forth as an influencing factor for which level of control the occupants experience. In the understanding of personal control, constraints are shown to come from limited control opportunities, limited knowledge, a limited responsiveness of the building and its systems, and from the social environment [29]. Similar views can most likely be transferred to the field of occupant feedback.

2.3 Knowledge gaps

As previously pointed out, the field of continuous occupant feedback is still young, and for this reason there are several profound research gaps. The following gaps were identified as relevant for investigation in this thesis:

Potential applications not related to building operation and control: Several studies mention the potential of occupant feedback systems to improve building control and operation. No work has been done to investigate the potential of continuous subjective occupant feedback (CSOF) systems for building design and benchmarking. The tuning (trial operation)

application is not clearly investigated either, but this application may be assumed to be closely linked to control and operation applications.

Emphasis of practical implementation: None of the discovered literature covers occupant feedback systems that are designed with the intent of being practical to implement in buildings, with minimal intrusion to occupants and low cost. On the other hand, many of the studies cover systems that require a high level of commitment from the users such as wearing smart-wristbands, installing smartphone apps or similar.

Focus on usability with “real” occupants: A system has never been tested in field tests with un- informed occupants while at the same time performing surveys or interviews to assess the occupant’s views of the system. Few systems have been tested with a specific focus on occupant usability and user experience.

Validity of collected feedback: The performance of occupant feedback systems has been tested and evaluated against physical environmental parameters and through prediction modeling, but no studies exist assessing occupant feedback compared to traditional occupant survey results or evaluations. Post Occupancy Evaluations (POEs) have traditionally been used for learning and benchmarking purposes in buildings, and if CSOF systems are seen as a possible supplement or predecessor for these applications it is necessary to validate the two information types against each other.

Framework and critical view on feedback information types: As mentioned, the existing literature in the field covers many different types of feedback types and interfaces, ranging from physical occupant centric data to satisfaction evaluations. No framework exists to

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distinguish between different types of feedback information, and to question the differences and qualitative nature of each information type.

3 Research questions

3.1 Research questions

The existing theory in the field has demonstrated that various CSOF systems can improve building operation and control. Knowledge gaps were identified for other applications that could be relevant, such as building design, tuning (trial operation) and benchmarking. Another knowledge gap was identified regarding the assessment of collected CSOF data in comparison to subjective data collected in traditional surveys, and how CSOF systems are used and perceived by the users.

Main Research Question:

Accordingly, the main research question is “How can continuous non-intrusive subjective occupant feedback (CSOF) improve building design, control, tuning and benchmarking?”. This research question was further divided into five sub-research questions covering the areas that were seen to be of most value to reaching the overall aims.

Each sub-research question represents a separate research activity in the thesis. The sub- research questions are described below.

RQa) “What is the potential contribution of CSOF to holistic design in plus energy buildings?”

The question answers to the research gap that was found regarding the contribution of CSOF methods for building design. Holistic design is found to be a crucial factor for achieving goals of future sustainable and plus energy buildings. In our current situation, discipline specific performance criteria act as a barrier for holistic design by demanding quantified

documentation for each design discipline that is difficult to produce for multidisciplinary phenomenon such as occupant perception or satisfaction. It is therefore relevant to investigate whether CSOF methods can have a potential application for documenting the performance of holistic design solutions, in addition to its known potential for HVAC control and creation of personal preference models.

RQb) “What type of information can CSOF collect and what is the link to human physiology and psychology?”

The question answers to a knowledge gap found regarding the lack of research organizing different types of occupant centric data. This gap does not lie within the original aim of the thesis but was identified and found to be of high relevance for developing the field of subjective occupant feedback. An additional aim was added to the thesis: “defining the different data types present in Occupant Centric Data collection”. In order to define the different data types, relevant processing levels within the field of environmental psychology must be identified, and the linkages to data collection methods established. This research question answers to a need for a fundamental framework and taxonomy to identify and distinguish different types of CSOF systems and their inherent data streams.

RQc) “What are the key features for a low-cost, non-intrusive CSOF system?”

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The question answers to how low-cost, non-intrusive CSOF methods can improve within the to existing and new commercial buildings, and mainly office buildings. The intended focus areas of the thesis: low-cost, non-intrusive CSOF methods for commercial and office buildings are together with the findings from the previous research questions used to devise an example of a system, and the design of this system is demonstrated. The system will be further tested through the following research questions in order to attain an answer to the main research question – whether a CSOF system can improve building design, control, tuning and benchmarking. No previous research has been found regarding the design of systems that have an aim of being multi-level, non-intrusive, and low-cost.

RQd) “What is the response of occupants to a system when tested in real buildings?”

This research question addresses the partial lack of research found taking the occupant’s view of CSOF systems. No previous research has been found covering in field tests with uninformed occupants. Further, no previous research has been found where deliberate temperature interventions are performed in field tests, or where while at the same time performing surveys.

RQe) “What is the relation and what are the key factors that link information from CSOF to information from surveys?”

The validity of CSOF feedback has been tested against physical environmental parameters and for personal comfort prediction modeling, but no studies exist assessing CSOF feedback compared to “traditional” survey results. Within the long existing field of Post Occupant Evaluations (POEs) there exists a vast base of knowledge concerning subjective occupant responses to surveys, benchmark values, as well as a POE studies must currently be viewed as the best method of obtaining a “ground truth” of subjective occupant opinions regarding indoor climate. By comparing CSOF data to survey results we can assess the validity of CSOF information to the most trusted subjective data source available. Linking the collected CSOF data to simultaneously collected survey results could potentially make it possible to use CSOF data to build on existing knowledge from POE studies.

The structure and flow of the research, with research questions, objectives and research activities is presented in Figure 2 in the following Section 4.

4 Research design

4.1 Objectives

The overall research design is illustrated in Figure 2. Sub-research question RQa) and RQb) are answered through theoretical analysis of existing literature. RQc) and RQd) are answered by using a mixed method research approach using a mix of quantitative and qualitative data at different stages [108]. In this case, a mix of theoretical data from review of existing research, as well as qualitative data in the form of focus group interviews from field experiments and quantitative data from surveys, feedback and measurements in field experiments. The strength of a mixed methods approach is to combine the rigidity and validity of quantitative data to the flexibility and informativeness of an open-ended qualitative data. RQe) is answered by analysis of quantitative data from field experiments. The objectives of each research activity are given below.

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The theoretical study of RQa) is answered in Article 1 “Enabling holistic design by the use of subjective occupant feedback”. The research was planned as a theoretical study investigating the challenges relevant when attempting to design high-performing buildings, and further analyzing how continuous subjective occupant feedback can improve design by empowering holistic design choices. The difficulties with performing holistic design with conventional design methods and discipline specific performance criteria, and the limitations of active climatization concepts in high performance buildings were described. It is further explained why holistic design is a necessary path toward more sustainable buildings, illustrated by presentation of examples from real projects where discipline specific performance criteria have limited the ability to choose holistic solutions. Further referred how subjective feedback from POEs has been found to be important for improving both design and operation and the emerging technological possibilities for automated and continuous collection of subjective occupant feedback. Finally, it was argued how this possibility can empower holistic design and improve the quality and performance of future high-performance buildings.

The main outcome of RQb) is presented in Article 2 “A theoretical framework for classifying occupant-centric data streams on indoor climate using a physiological and cognitive process hierarchy”. The research was planned as a theoretical study investigating the applications of theories and concepts developed in environmental and market psychology for creating a taxonomy for the classification of occupant-centric data in the domain of indoor climate. The goal was to create a conceptual background that covers both objective and subjective information. A multidisciplinary array of background information from fields that are functional to explain the logic and terminology, such as environmental psychology, market psychology, and physiology was used in the development of the classification scheme. A conceptual framework for occupant-centric data was introduced and the link between data collection methods, information embedded in the data, and human physiological and psychological processes was highlighted. Three short examples of the use of the proposed framework were given. The results are suggested as a potentially useful tool for further work within the theories and models for thermal comfort prediction.

RQc) is answered in the first part of Article 3 “Design and in-field testing of a multi-level system for continuous subjective occupant feedback on indoor climate”. The research was planned as a study of current know-how from previous research on subjective feedback systems regarding indoor climate for further use in developing a possible design for a low cost and non-intrusive system for multi-level occupant feedback. A dedicated survey on the literature was performed to identify current trends, experiences, and outcomes from other research. The study only focused on research activities where subjective feedback systems were used in field tests. The knowledge gathered from the literature was used to drive the design of a system targeting the collection of 3 types of subjective information regarding indoor climate from the occupants: occupant complaints, occupant control actions, and occupant satisfaction evaluations. According to the previously developed hierarchy (from RQb), these system types represent different levels of subjective occupant feedback. The surveyed articles were coded according to feedback interface type, number and type of participants, and focus of the research. The system was developed for use in field tests with a special focus on developing a low-cost, robust, usable system using off the shelf components.

RQd) is answered in the second part of Article 3 “Design and in-field testing of a multi-level system for continuous subjective occupant feedback on indoor climate”, as well as in Article 4

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“Field investigations of a smiley-face polling station for recording occupant satisfaction with indoor climate”. The research was planned as a test of the previously designed system to reveal occupant opinions in a realistic office setting, using both qualitative and quantitative methods. The studies were done by asking occupants in the five studied buildings to use the CSOF systems which were placed in the office environments over a period of several weeks.

Simultaneously the room temperature was measured, and the researchers assessed occupant opinions by approaching each occupant on given days and asking them to fill out a separate 2- minute survey. The survey included, among other, questions regarding the use of the CSOF systems, indoor climate satisfaction measures, perceived control, and metadata about the respondent (sex, age, workplace location). In order to investigate the sensitivity of the systems, deliberate temperature interventions were performed on selected days in some of the buildings. The occupants were not aware in detail of the ultimate goal of the study (i.e. to study the use of the CSOF systems), but they were informed on the study being focused on assessing indoor climate satisfaction. In this way, the tests became partially blind, and the occupants may be said to be un-biased and representative of real-life occupants introduced to new equipment. The results were analysed by comparing feedback frequencies and trends, assessing reported occupant opinions and assessing technical performance and finally evaluating the lessons learned.

RQe) is answered in the second part of Article 4 “Field investigations of a smiley-face polling station for recording occupant satisfaction with indoor climate”, as well as in Article 5

“Building trust in occupant-centric data streams - Quantifying the relationships between surveys and feedback”. The research was planned as a quantitative study of the validity of the feedback collected with the previously designed system in field the tests. In the first study the question was answered for two of the system types (complaints and satisfaction evaluations) by comparing CSOF feedback and survey data for identical questions using simple methods.

These were also compared to a simple thermal comfort model using the room temperature as input. This study only covered two of the five field tests. In the second study, CSOF data from all field tests was compared to survey data for all three types of feedback using more advanced methods of analysis. In this case, the relationships between the two sets of data were modelled using logistic regression to uncover how and to what extent the data are comparable.

It should be noted that the focus of the research has been on investigating the fundamental qualities of a non-intrusive multi-level CSOF system, rather than directly investigating the performance of CSOF systems for the four specified applications (design, control, tuning and benchmarking). In this way, the research results can hopefully have a more general impact on the research field.

The methods used are described further in the following section 4.2.

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Figure 2 Outline of research with main research question, sub-research questions, objectives and research activities.

Continuous data-streams for occupant feedback on indoor climate: Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

Doctoral theses at NTNU, 2021:129

Niels Lassen

Continuous data-streams for occupant feedback on indoor climate

Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

Doctoral thesis

Niels Lassen

Continuous data-streams for occupant feedback on indoor climate

Theoretical and experimental analysis of concepts, methods and systems for real-life implementation

Thesis for the Degree of Philosophiae Doctor

Trondheim, April 2021

Norwegian University of Science and Technology Faculty of Architecture and Design

Department of Architecture and Technology

Continuous data-streams for occupant feedback on indoor climate

Niels Lassen

Abstract

Acknowledgements

Table of Contents

List of acronyms

1 Introduction

1.1 Motivation

1.2 Aims

1.3 Outline

2 Background

2.1 Research domain

2.2 Theoretical foundation

2.3 Knowledge gaps

3 Research questions

3.1 Research questions

4 Research design

4.1 Objectives