The SDE4 Learning Trail: Crowdsourcing occupant comfort feedback at a net-zero energy building

Sood, Tapeesh; Quintana, Matias; Jayathissa, Prageeth; Abdelrahman, Mahmoud; Miller, Clayton

doi:10.1088/1742-6596/1343/1/012141

Cited by 22 publications

(11 citation statements)

References 4 publications

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“…For buildings that are already smart, as in the case of BMS, a smartphone app can easily become part of the loop, allowing the users to make the most of the potentiality of the entire system. Sood et al [112] created a human-building interaction framework called the SDE Learning Trail, under the new Net Zero Energy Building (NZEB) paradigm. SDE refers to the building of the experiment, the School of Design & Environment (SDE) of Singapore.…”

Section: Crowdsensing For Thermal Comfortmentioning

confidence: 99%

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A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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This paper presents a review of technologies under the paradigm 4.0 applied to the study of the thermal comfort and, implicitly, energy efficiency. The research is based on the analysis of the Internet of Things (IoT) literature, presenting a comparison among several approaches adopted. The central objective of the research is to outline the path that has been taken throughout the last decade towards a people-centric approach, discussing how users switched from being passive receivers of IoT services to being an active part of it. Basing on existing studies, authors performed what was a necessary and unprecedented grouping of the IoT applications to the thermal comfort into three categories: the thermal comfort studies with IoT hardware, in which the approach focuses on physical devices, the mimicking of IoT sensors and comfort using Building Simulation Models, based on the dynamic modelling of the thermal comfort through IoT systems, and Crowdsensing, a new concept in which people can express their sensation proactively using IoT devices. Analysing the trends of the three categories, the results showed that Crowdsensing has a promising future in the investigation through the IoT, although some technical steps forward are needed to achieve a satisfactory application to the thermal comfort matter.

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Section: Crowdsensing For Thermal Comfortmentioning

confidence: 99%

“…Sood et al [ 112 ] created a human–building interaction framework called the SDE Learning Trail, under the new Net Zero Energy Building (NZEB) paradigm. SDE refers to the building of the experiment, the School of Design & Environment (SDE) of Singapore.…”

Section: Mobile Crowdsensingmentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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“…Subjective indoor comfort information can be collected either actively through asking occupants what they want or by passively observing behavior that indicates discomfort. Several new active methods are emerging through the use of mobile [113] and wearable applications [114] to crowd-source comfort feedback that can be used for control. New passive techniques are emerging from adaptive behaviors such as occupant interactions with the building interfaces, e.g., thermostats or light switches.…”

Section: State-of-the-artmentioning

confidence: 99%

Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation

O’Brien

Wagner

Schweiker

et al. 2020

Building and Environment

Self Cite

161

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Despite the fact that buildings are designed for occupants in principle, evidence suggests buildings are often uncomfortable compared to the requirements of standards; difficult to control by occupants; and, operated inefficiently with regards to occupants' preferences and presence. Meanwhile, practitioners-architects, engineers, technology companies, building managers and operators, and policymakers-lack the knowledge, tools, and precedent to design and operate buildings optimally considering the complex and diverse nature of occupants. Building on the success of IEA EBC Annex 66 ("Definition and simulation of occupant behavior in buildings"; 2013-2017), a follow-up IEA EBC Annex 79 ("Occupantcentric building design and operation"; 2018-2023) has been developed to address gaps in knowledge, practice, and technology. Annex 79 involves international researchers from diverse disciplines like engineering, architecture, computer science, psychology, and sociology. Annex 79 and this review paper have four main areas of focus: (1) multi-domain environmental exposure, building interfaces, and human behavior; (2) data-driven occupant modeling strategies and digital tools; (3) occupant-centric building design; and (4) occupant-centric building operation. The objective of this paper is to succinctly report on the leading research of the above topics and articulate the most pressing research needs-planned to be addressed by Annex 79 and beyond.

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“…Another recent study that deployed a cyber-physical system to collect longitudinal data in offices focused on occupant concentration [72]. The work in this paper is most directly related to previous work in collecting longitudinal comfort feedback from smartphone interfaces for the allocation of activity-based workspaces [73] and through a sustainability tour in a university campus building [74].…”

Section: Similar Work In Intensive Longitudinal Data Collection In the Built Environmentmentioning

confidence: 99%

Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models

et al. 2020

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Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect occupant comfort preference. Human perception could be helpful to capture these disparate phenomena and interpreting their impact; the challenge is collecting spatially and temporally diverse subjective feedback in a scalable way. This paper presents a methodology to collect intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An experiment with 30 occupants over two weeks produced 4378 field-based surveys for thermal, noise, and acoustic preference. The occupants and the spaces in which they left feedback were then clustered according to these preference tendencies. These groups were used to create different feature sets with combinations of environmental and physiological variables, for use in a multi-class classification task. These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned. The most accurate model had multi-class classification F1 micro scores of 64%, 80% and 86% for thermal, light, and noise preference, respectively. The discussion outlines how these models can enhance comfort preference prediction when supplementing data from installed sensors. The approach presented prompts reflection on how the building analysis community evaluates, controls, and designs indoor environments through balancing the measurement of variables with occupant preferences in an intensive longitudinal way.

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The SDE4 Learning Trail: Crowdsourcing occupant comfort feedback at a net-zero energy building

Cited by 22 publications

References 4 publications

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation

Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models

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