Special Issue "New Approaches to Modelling Occupant Comfort"

A special issue of Buildings (ISSN 2075-5309).

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Thomas Parkinson
Website
Guest Editor
Center for the Built Environment, University of California, Berkeley, CA, USA
Interests: thermal comfort; indoor environmental quality; high performance buildings; adaptive behaviors; sensor technologies; thermal physiology; psychophysics; occupant satisfaction; personal comfort systems; machine learning
Prof. Dr. Marcel Schweiker
Website SciProfiles
Guest Editor
Institute for Occupational, Social and Environmental Medicine, University Hospital RWTH, Aachen, Germany
Interests: thermal comfort; thermal adaptation; occupant behavior; occupant satisfaction; healthy buildings; alliesthesia; psychophysical modelling

Special Issue Information

Dear Colleagues,


Energy spent on heating and cooling indoor environments is responsible for the largest share of electricity consumption in buildings. Reducing the carbon footprint of the built environment relies on a balance of energy efficiency measures that simultaneously ensure occupant thermal comfort. Overlaying this challenge is a shift towards building designs and layouts that promote well-being, health, and improve occupants’ experience of their space. As such, long-standing paradigms of thermal neutrality and steady-state conditions should be reconsidered. Successfully navigating this changing landscape requires novel approaches to understanding the psychophysical relationship between thermal environments and occupant perception to increase the resilience of humans and buildings in a rapidly changing world.


This Special Issue of Buildings will focus on innovative research efforts to model the thermal experience of building occupants. Of particular interest are works building upon comfort theories reflecting the dynamics involved, such as the adaptive thermal comfort theory or thermal alliesthesia, to better understand the relationship between climate, comfort and energy use in buildings. This includes diverse focus areas such as (i) modifications to existing or new comfort indices, (ii) personal comfort systems, (iii) post occupancy evaluations, (iv) application of building sensor networks, and (v) machine learning techniques. Works that employ laboratory studies, field studies, and numerical simulations are invited. Meta-analyses of existing databases such as the ASHRAE Global Thermal Comfort Database II are also encouraged.


Dr. Thomas Parkinson
Prof. Dr. Marcel Schweiker
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Adaptive
  • Thermal comfort
  • Comfort models
  • HVAC
  • Climate
  • Occupant behavior
  • Energy efficiency
  • Buildings
  • Statistical modeling

Published Papers (1 paper)

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Research

Open AccessArticle
Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models
Buildings 2020, 10(10), 174; https://doi.org/10.3390/buildings10100174 - 01 Oct 2020
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)
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