A personalized measure of thermal comfort for building controls

Daum, David; Haldi, Frédéric; Morel, Nicolas

doi:10.1016/j.buildenv.2010.06.011

Cited by 166 publications

(70 citation statements)

References 19 publications

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“…Model driven approaches which model individual preferences based on feedback can be found in several papers [11,18,5] and some commercial systems such as the Comfy system from BuildingRobotics. 2 In particular it has been stated that Building Management Systems' (BMS) operators tend to set conservative setpoints (to avoid complaints) leading to wasted energy.…”

Section: Related Workmentioning

confidence: 99%

“…The system differs from that examined here in that user preferences are combined with equal weighting and the approach is not model driven (a users preference is used immediately in a voting pool to adjust the environment). Daum et al [18], discusses the conflict between different users preferences noting the diversity of preferences and problems faced when trying to combine them into one function with a well-defined peak. Song et al [20] present a system with a similar aim to that here which learns user preferences and combines them for use in the BMS system.…”

Section: Related Workmentioning

confidence: 99%

“…Song et al [20] present a system with a similar aim to that here which learns user preferences and combines them for use in the BMS system. While our approach allows combination of preferences and active learning as in [18,20], it differs in that we use a purely Bayesian approach based on the what the samples themselves reveal rather than assuming a model for the data a priori 3 ; a functional logistic model relationship in [18] or using a collaborative rule engine in the case of [20]. Zhao et al [21] proposes two zones, a comfort and discomfort zone and recursively estimates the boundary set between these.…”

Section: Related Workmentioning

confidence: 99%

“…humans share a common structure in the second level of the hierarchy (see Section 5.3.1). Daum [18] similarly uses global priors so that new users will have a profile even though they may not have yet donated any samples. These profiles are then updated as new measurements arrive as is examined in this research.…”

Section: Related Workmentioning

confidence: 99%

“…what should we do about users whose responses are conflictual? 18 Combining these factors can be done quite naturally given the Gaussian Process (here we use GP1) representation of the process. Fig.…”

Section: Combining Preference Functionsmentioning

confidence: 99%

See 4 more Smart Citations

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Fay

O'Toole

Brown

2017

Pervasive and Mobile Computing

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a b s t r a c tThis paper presents a ubiquitous thermal comfort preference learning study in a noisy environment. We introduce Gaussian Process models into this field and show they are ideal, allowing rejection of outliers, deadband samples, and produce excellent estimates of a users preference function. In addition, informative combinations of users preferences becomes possible, some of which demonstrate well defined maxima ideal for control signals. Interestingly, while those users studied have differing preferences, their hyperparameters are concentrated allowing priors for new users. In addition, we present an active learning algorithm which estimates when to poll users to maximise the information returned.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Combining Preference Functionsmentioning

confidence: 99%

See 3 more Smart Citations

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Fay

O'Toole

Brown

2017

Pervasive and Mobile Computing

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Smart Textile‐Based Personal Thermal Comfort Systems: Current Status and Potential Solutions

Tabor

Chatterjee

Ghosh

2020

Adv Materials Technologies

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Thermophysiological comfort in humans is sought universally but seldom achieved due to biological and physiological variances. Most people in developed parts of the world rely on central heating and cooling systems to achieve thermophysiological comfort which is highly energy-intensive, inefficient, and rarely satisfactory. A potential solution to this issue is a wearable personal thermal comfort system (PTCS) consisting of textile-based temperature and moisture sensors, that can sense the environment and physiology of the wearer, combined with thermal and moisture responsive actuators, and/or heating/cooling devices to provide an individualized thermal environment. Moving thermal regulation away from the built environment to the microclimate surrounding the human body with the use of textiles has the This article is protected by copyright. All rights reserved. 2 potential to provide personalized thermal comfort and energy savings. Such a system may employ thermal comfort models and leverage the Internet of Things (IoT) and machine learning (ML) to understand individuals' comfort requirements in the current environment. Herein, the current state of textile-based active and passive thermophysiological comfort systems/technologies is summarized, including their environmental impact, major thermal comfort models, and factors influencing comfort (such as heat and moisture transfer). Also, active and passive textile-based devices (sensors, actuators, and flexible heating/cooling devices) that may be incorporated into a textile-based wearable PTCS are comprehensively discussed with an emphasis on their advantages, limitations, and future prospects.

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Human-in-the-Loop Thermal Management for Smart Buildings

Gupta

Kar

2017

Intelligent Building Control Systems

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A personalized measure of thermal comfort for building controls

Cited by 166 publications

References 19 publications

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection

Smart Textile‐Based Personal Thermal Comfort Systems: Current Status and Potential Solutions

Human-in-the-Loop Thermal Management for Smart Buildings

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