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Buildings
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New Approaches to Modelling Occupant Comfort
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New Approaches to Modelling Occupant Comfort

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 30820

Special Issue Editors

Dr. Thomas Parkinson

E-Mail 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

E-Mail Website
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 submissions that pass pre-check are 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 2600 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 (7 papers)

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Editorial

Jump to: Research, Review

3 pages, 163 KiB  
Editorial
New Approaches to Modelling Occupant Comfort
by Thomas Parkinson and Marcel Schweiker
Buildings 2022, 12(7), 985; https://doi.org/10.3390/buildings12070985 - 11 Jul 2022
Viewed by 1243
Abstract
Heating and cooling indoor environments is responsible for the largest share of energy consumption in buildings [...] Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)

Research

Jump to: Editorial, Review

22 pages, 4896 KiB  
Article
Monitoring and Predicting Occupant’s Sleep Quality by Using Wearable Device OURA Ring and Smart Building Sensors Data (Living Laboratory Case Study)
by Elena Malakhatka, Anas Al Rahis, Osman Osman and Per Lundqvist
Buildings 2021, 11(10), 459; https://doi.org/10.3390/buildings11100459 - 07 Oct 2021
Cited by 5 | Viewed by 4581
Abstract
Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture several important parameters that may be used to measure the indoor comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. These data could be used not only [...] Read more.
Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture several important parameters that may be used to measure the indoor comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. These data could be used not only for improving personal wellbeing, but for adjusting a better indoor environment condition. In this study, we have focused specifically on the sleeping phase. The main purpose of this work was to use the data from wearable devices and smart meters to improve the sleep quality of residents living at KTH Live-in-Lab. The wearable device we used was the OURA ring which specializes in sleep monitoring. In general, the data quality showed good potential for the modelling phase. For the modelling phase, we had to make some choices, such as the programming language and the AI algorithm, that was the best fit for our project. First, it aims to make personal physiological data related studies more transparent. Secondly, the tenants will have a better sleep quality in their everyday life if they have an accurate prediction of the sleeping scores and ability to adjust the built environment. Additionally, using knowledge about end users can help the building owners to design better building systems and services related to the end-user’s wellbeing. Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)
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26 pages, 24092 KiB  
Article
Determination of Thermal Comfort Zones through Comparative Analysis between Different Characterization Methods of Thermally Dissatisfied People
by Pedro Filipe da Conceição Pereira and Evandro Eduardo Broday
Buildings 2021, 11(8), 320; https://doi.org/10.3390/buildings11080320 - 26 Jul 2021
Cited by 13 | Viewed by 4021
Abstract
In order to maintain thermal comfort and preserve indoor environmental quality, people use heating, ventilation and air-conditioning (HVAC) systems inside buildings. However, buildings must be prepared not only to provide adequate thermal comfort to their occupants but also to align strategies that enable [...] Read more.
In order to maintain thermal comfort and preserve indoor environmental quality, people use heating, ventilation and air-conditioning (HVAC) systems inside buildings. However, buildings must be prepared not only to provide adequate thermal comfort to their occupants but also to align strategies that enable better energy performance. Thus, this work aimed to establish thermal comfort zones (TCZ) through different characterization methods of thermally dissatisfied people. Responses were collected from 481 students, through the application of questionnaires in classrooms, during the Brazilian winter of 2019. Three methods for determining the actual percentage of dissatisfied (APD) were adopted, which generated three different equations, namely: APD_1; APD_2 and APD_3, based on the original Predicted Percentage of Dissatisfied (PPD) equation. By using the probit model, three TCZ were calculated: 17.73–22.4 °C (APD_1); 20.71–20.93 °C (APD_2) and 17.89–24.83 °C (APD_3). In addition, a comfort zone based on the linear regression between the thermal sensation votes and the operative temperature was determined (18.77–22.69 °C). All thermal comfort zones resulting from this work have colder temperatures than that indicated by the American Society of Heating, Refrigerating and Air-Conditioning Engineers—ASHRAE (2017) of 23–26 °C for the winter, showing the potential for energy savings from the adoption of this type of strategy, while maintaining thermal comfort. Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)
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21 pages, 4576 KiB  
Article
An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities
by Seyyed Danial Nazemi, Mohsen A. Jafari and Esmat Zaidan
Buildings 2021, 11(6), 237; https://doi.org/10.3390/buildings11060237 - 31 May 2021
Cited by 12 | Viewed by 2838
Abstract
The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce [...] Read more.
The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings. Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)
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15 pages, 814 KiB  
Article
Identification of Environmental and Contextual Driving Factors of Air Conditioning Usage Behaviour in the Sydney Residential Buildings
by Bongchan Jeong, Jungsoo Kim, Zhenjun Ma, Paul Cooper and Richard de Dear
Buildings 2021, 11(3), 122; https://doi.org/10.3390/buildings11030122 - 18 Mar 2021
Cited by 9 | Viewed by 2197
Abstract
Air conditioning (A/C) is generally responsible for a significant proportion of total building energy consumption. However, occupants’ air conditioning usage patterns are often unrealistically characterised in building energy performance simulation tools, which leads to a gap between simulated and actual energy use. The [...] Read more.
Air conditioning (A/C) is generally responsible for a significant proportion of total building energy consumption. However, occupants’ air conditioning usage patterns are often unrealistically characterised in building energy performance simulation tools, which leads to a gap between simulated and actual energy use. The objective of this study was to develop a stochastic model for predicting occupant behaviour relating to A/C cooling and heating in residential buildings located in the Subtropical Sydney region of Australia. Multivariate logistic regression was used to estimate the probability of using A/C in living rooms and bedrooms, based on a range of physical environmental (outdoor and indoor) and contextual (season, day of week, and time of day) factors observed in 42 Sydney region houses across a two-year monitoring period. The resulting models can be implemented in building energy performance simulation (BEPS) tools to more accurately predict indoor environmental conditions and energy consumption attributable to A/C operation. Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)
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22 pages, 8882 KiB  
Article
Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models
by Prageeth Jayathissa, Matias Quintana, Mahmoud Abdelrahman and Clayton Miller
Buildings 2020, 10(10), 174; https://doi.org/10.3390/buildings10100174 - 01 Oct 2020
Cited by 70 | Viewed by 8487
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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Review

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25 pages, 5071 KiB  
Review
Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach
by Mohammad Nyme Uddin, Hsi-Hsien Wei, Hung Lin Chi and Meng Ni
Buildings 2021, 11(2), 41; https://doi.org/10.3390/buildings11020041 - 26 Jan 2021
Cited by 27 | Viewed by 6034
Abstract
Energy consumption in buildings depends on several physical factors, including its physical characteristics, various building services systems/appliances used, and the outdoor environment. However, the occupants’ behavior that determines and regulates the building energy conservation also plays a critical role in the buildings’ energy [...] Read more.
Energy consumption in buildings depends on several physical factors, including its physical characteristics, various building services systems/appliances used, and the outdoor environment. However, the occupants’ behavior that determines and regulates the building energy conservation also plays a critical role in the buildings’ energy performance. Compared to physical factors, there are relatively fewer studies on occupants’ behavior. This paper reports a systematic review analysis on occupant behavior and different modeling approaches using the Scopus and Science Direct databases. The comprehensive review study focuses on the current understanding of occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy conservation. Finally, the study identifies six significant research gaps for future development: occupant-centered space layout deployment; occupant behavior must be understood in the context of developing or low-income economies; there are higher numbers of quantitative occupant behavior studies than qualitative; the extensive use of survey or secondary data and the lack of real data used in model validation; behavior studies are required for diverse categories building; building information modeling (BIM) integration with existing occupant behavior modeling/simulation. These checklists of the gaps are beneficial for researchers to accomplish the future research in the built environment. Full article
(This article belongs to the Special Issue New Approaches to Modelling Occupant Comfort)
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