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Статті в журналах з теми "Occupant-centric"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

zadeh, Zeinab Khorasani, and Mohamed M. Ouf. "Optimizing occupant-centric building controls given stochastic occupant behaviour." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012140. http://dx.doi.org/10.1088/1742-6596/2069/1/012140.

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Abstract Occupant-centric control (OCC) strategies represent a novel approach for indoor climate control in which occupancy patterns and occupant preferences are embedded within control sequences. They aim to improve both occupant comfort and energy efficiency by learning and predicting occupant behaviour, then optimizing building operations accordingly. Previous studies estimate that OCC can increase energy savings by up to 60% while improving occupant comfort. However, their performance is subjected to several factors, including uncertainty due to occupant behaviour, OCC configurational settings, as well as building design parameters. To this end, testing OCCs and adjusting their configurational settings are critical to ensure optimal performance. Furthermore, identifying building design alternatives that can optimize such performance given different occupant preferences is an important step that cannot be investigated during field implementations of OCC due to logistical constraints. This paper presents a framework to optimize OCC performance in a simulation environment, which entails coupling synthetic occupant behaviour models with OCCs that learn their preferences. The genetic algorithm for optimization is then used to identify the configurational settings and design parameters that minimize energy consumption under three different occupant scenarios. To demonstrate the proposed framework, three OCCs were implemented in the building simulation program, EnergyPlus, and executed through a Python package, EPPY to optimize OCC configurational settings and design parameters. Results revealed significant improvement of OCC performance under the identified optimal configurational settings and design parameters for each of the investigated occupant scenarios. This approach would improve OCC performance in actual buildings and avoid discomfort issues that arise during the initial implementation phases.

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2

O'Brien, William, Isabella Gaetani, Salvatore Carlucci, Pieter-Jan Hoes, and Jan L. M. Hensen. "On occupant-centric building performance metrics." Building and Environment 122 (September 2017): 373–85. http://dx.doi.org/10.1016/j.buildenv.2017.06.028.

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3

Choi, Haneul, Chai Yoon Um, Kyungmo Kang, Hyungkeun Kim, and Taeyeon Kim. "Review of vision-based occupant information sensing systems for occupant-centric control." Building and Environment 203 (October 2021): 108064. http://dx.doi.org/10.1016/j.buildenv.2021.108064.

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4

Liu, Yihang, Bin Yang, and Zhang Lin. "A pilot study of occupant centric control stratum ventilation based on computer vision." E3S Web of Conferences 356 (2022): 01029. http://dx.doi.org/10.1051/e3sconf/202235601029.

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Анотація:

Indoor occupant information has an obvious influence on operating parameters of heating ventilation and air conditioning (HVAC) system, which further affects occupants’ thermal comfort and energy consumption. This pilot study proposes an occupant centric control (OCC) strategy for stratum ventilation (SV) to achieve demand control ventilation (DCV). Firstly, the computer vision sensing system and deep learning algorithm are used to detect the number of occupants in real time, and the accuracy of the number of occupants in the office environment was evaluated. Then, the occupant centric stratum ventilation control strategy is designed by the dynamic changes of cooling load. Finally, the thermal comfort and air quality of the thermal environment created by the OCC strategy were evaluated through subject experiment, and the energy consumption of the HVAC system was calculated in combination with the energy consumption simulation software. This study adjusts system setting values according to actual needs, so that the HVAC system responds to the dynamic changes of the indoor cooling load in real time, creating a comfortable and healthy indoor environment in an energy efficient manner.

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5

Mosiman, Cory, Gregor Henze, and Herbert Els. "Development and Application of Schema Based Occupant-Centric Building Performance Metrics." Energies 14, no. 12 (June 13, 2021): 3513. http://dx.doi.org/10.3390/en14123513.

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Occupant behavior can significantly influence the operation and performance of buildings. Many occupant-centric key performance indicators (KPIs) rely on having accurate counts of the number of occupants in a building, which is very different to how occupancy information is currently collected in the majority of buildings today. To address this gap, the authors develop a standardized methodology for the calculation of percent space utilization for buildings, which is formulated with respect to two prevalent operational data schemas: the Brick Schema and Project Haystack. The methodology is scalable across different levels of spatial granularity and irrespective of sensor placement. Moreover, the methods are intended to make use of typical occupancy sensors that capture presence level occupancy and not counts of people. Since occupant-hours is a preferable metric to use in KPI calculations, a method to convert between percent space utilization and occupant-hours using the design occupancy for a space is also developed. The methodology is demonstrated on a small commercial office space in Boulder, Colorado using data collected between June 2018 and February 2019. A multiple linear regression is performed that shows strong evidence for a relationship between building energy consumption and percent space utilization.

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6

Zhu, Mingya, Yiqun Pan, Zejun Wu, Jiantong Xie, Zhizhong Huang, and Risto Kosonen. "An occupant-centric air-conditioning system for occupant thermal preference recognition control in personal micro-environment." Building and Environment 196 (June 2021): 107749. http://dx.doi.org/10.1016/j.buildenv.2021.107749.

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7

Li, Canjun, Han Zhu, Xiangchao Lian, Yuxin Liu, Xiaohan Li, and Yanbo Feng. "Study of “time-lag” of occupant behavior occurrences for establishing an occupant-centric building control system." Building and Environment 216 (May 2022): 109005. http://dx.doi.org/10.1016/j.buildenv.2022.109005.

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8

Li, Zhengrong, Han Zhu, Yan Ding, Xiaofeng Xu, and Binjie Weng. "Establishment of a personalized occupant behavior identification model for occupant-centric buildings by considering cost sensitivity." Energy and Buildings 225 (October 2020): 110300. http://dx.doi.org/10.1016/j.enbuild.2020.110300.

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9

Kong, Meng, Bing Dong, Jianshun Zhang, and Xuezheng Wang. "Develop a New Approach to Evaluate Energy Savings, Thermal Comfort and IAQ from Occupant-Centric Building Controls." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012148. http://dx.doi.org/10.1088/1742-6596/2069/1/012148.

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Анотація:

Abstract Occupant behavior is identified as one of the key factors influencing the energy use and indoor environmental quality of the building. Occupancy-centric control is famous for its potential to save building energy without sacrificing occupants’ comfort. This study utilized two identical lab spaces, configured as typical open-plan offices, to investigate the performance of the occupancy-centric control in terms of energy-saving, indoor air quality, and thermal comfort. The results have demonstrated that occupancy-centric control could save around 28% total energy, including fan, cooling, and heating energy, with minimal impact on the air quality and thermal comfort.

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10

Ouf, Mohamed M., June Young Park, and H. Burak Gunay. "On the simulation of occupant-centric control for building operations." Journal of Building Performance Simulation 14, no. 6 (November 2, 2021): 688–91. http://dx.doi.org/10.1080/19401493.2021.2001622.

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11

Abuimara, Tareq, William O’Brien, Burak Gunay, and Juan Sebastián Carrizo. "Towards occupant-centric simulation-aided building design: a case study." Building Research & Information 47, no. 8 (August 22, 2019): 866–82. http://dx.doi.org/10.1080/09613218.2019.1652550.

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12

Tabadkani, Amir, Astrid Roetzel, Hong Xian Li, and Aris Tsangrassoulis. "A review of occupant-centric control strategies for adaptive facades." Automation in Construction 122 (February 2021): 103464. http://dx.doi.org/10.1016/j.autcon.2020.103464.

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13

Xie, Jiaqing, Haoyang Li, Chuting Li, Jingsi Zhang, and Maohui Luo. "Review on occupant-centric thermal comfort sensing, predicting, and controlling." Energy and Buildings 226 (November 2020): 110392. http://dx.doi.org/10.1016/j.enbuild.2020.110392.

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14

Park, Jihyun, Vivian Loftness, and Tsung-Hsien Wang. "Examining In Situ Acoustic Conditions for Enhanced Occupant Satisfaction in Contemporary Offices." Buildings 12, no. 9 (August 25, 2022): 1305. http://dx.doi.org/10.3390/buildings12091305.

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Анотація:

Indoor acoustic quality is one of the critical indicators for occupants’ health, comfort, and productivity in contemporary office environments. Post-occupancy evaluation (POE) is usually employed to examine in situ acoustic measurements to ensure indoor acoustic quality. However, prevailing acoustic performance evaluation does not often consider the technical attributes of building systems (TABS) to holistically investigate the significant correlations between objective acoustic field measurements and subjective POE. As such, this study proposes to cross-examine in situ and perceived acoustic quality indices with TABS to quantify critical factors leading to enhanced occupant satisfaction. Statistical analyses suggest that technical building attributes can significantly influence occupants’ acoustic satisfaction compared to sound levels recorded in contemporary offices. For instance, lowering the distributed noise level from above 40% to 2% can lead to an average 21% increase in occupant satisfaction. Ultimately, incorporating environmental measurements with physical building attributes from an occupant-centric perspective can uncover applicable design guidelines for achieving optimal acoustic quality with the highest occupant satisfaction.

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15

Yang, Tao, Arkasama Bandyopadhyay, Zheng O’Neill, Jin Wen, and Bing Dong. "From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control." Building Simulation 15, no. 6 (December 7, 2021): 913–32. http://dx.doi.org/10.1007/s12273-021-0861-0.

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16

Ye, Yunyang, Yan Chen, Jian Zhang, Zhihong Pang, Zheng O’Neill, Bing Dong, and Hwakong Cheng. "Energy-saving potential evaluation for primary schools with occupant-centric controls." Applied Energy 293 (July 2021): 116854. http://dx.doi.org/10.1016/j.apenergy.2021.116854.

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17

Hashemloo, Alireza, Mehlika Inanici, and Christopher Meek. "GlareShade: a visual comfort-based approach to occupant-centric shading systems." Journal of Building Performance Simulation 9, no. 4 (July 9, 2015): 351–65. http://dx.doi.org/10.1080/19401493.2015.1058421.

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18

Park, June Young, Mohamed M. Ouf, Burak Gunay, Yuzhen Peng, William O'Brien, Mikkel Baun Kjærgaard, and Zoltan Nagy. "A critical review of field implementations of occupant-centric building controls." Building and Environment 165 (November 2019): 106351. http://dx.doi.org/10.1016/j.buildenv.2019.106351.

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19

Favero, Matteo, Jan Kloppenborg Møller, Davide Calì, and Salvatore Carlucci. "Human-in-the-loop methods for occupant-centric building design and operation." Applied Energy 325 (November 2022): 119803. http://dx.doi.org/10.1016/j.apenergy.2022.119803.

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20

Derbas, Ghadeer, and Karsten Voss. "Data-driven occupant-centric rules of automated shade adjustments: Luxembourg case study." Journal of Physics: Conference Series 2042, no. 1 (November 1, 2021): 012126. http://dx.doi.org/10.1088/1742-6596/2042/1/012126.

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Анотація:

Abstract This study presents key findings of observed datasets in a nearly zero-energy office building for over 66 working days from June to mid-September in 2019, Luxembourg. Measurements of indoor and outdoor environmental parameters as well as user-shade override adjustments were extracted from the KNX-based building management system (BMS) in 47 office rooms located in three typical floor levels. Relative frequency and “rate of change” of blind use were analysed in terms of window orientation, occupancy level, and the time of the day. Logistic regression and data mining techniques were used to identify potentially useful and understandable occupant behaviour patterns and reveal the main triggers behind blind adjustments. The well-designed automation system together with the inner glare protection formed the base of very low user-shade interactions. A mean of 0.184 manual blind adjustments per day per office. Eight regression sub-models were developed and all were incapable of predicting user-shade lowering and raising events. Alternatively, two user profiles were mined based on 20 rules gained from clustering analysis: user (ß) was representing the passive user, and user (μ) the medium user. Overall, we conclude that the automated shading system in this building is satisfactory, user-friendly, and a robust control system.

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21

Mahdavi, Ardeshir, Helene Teufl, and Christiane Berger. "An Occupant-Centric Theory of Building Control Systems and Their User Interfaces." Energies 14, no. 16 (August 6, 2021): 4788. http://dx.doi.org/10.3390/en14164788.

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This paper presents an occupant-centric theory of buildings’ indoor-environmental control systems and their user interfaces. Buildings typically can have multiple devices and systems to maintain indoor-environmental conditions within certain ranges in order to meet occupants’ health and comfort requirements. Therefore, it is important to understand what those ranges are exactly, who defines them, and for whom. Health and comfort sciences offer some broad directions concerning desirable indoor conditions. These are typically formulated in various codes, standards, and guidelines in terms of target values or the set points of control variables. However, preferable conditions may differ at different times and for different individuals. Another question concerns the agency responsible for maintaining the preferred conditions. In some settings, conditions may be centrally controlled via the buildings’ automation systems, whereas in other settings, occupants might have the possibility to control their immediate surroundings. Given these qualifications, the objective of the present inquiry can be stated more precisely. We outline a human-ecologically inspired theory pertaining to the occupants’ perception of and interaction with a building’s indoor-environmental control systems and their user interfaces. Specifically, we explore the operationalization potential of the proposed theory as a compact assessment protocol for the evaluation of buildings’ responsiveness to occupants’ preferences. Initial experiences with the derivative protocol are promising. Nonetheless, in order to be fully applicable in practice, certain challenges must be addressed. These specifically include the need for more robust procedures toward the translation of occupants’ subjective judgments into quantitative evaluation scales.

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22

De la Barra, Pedro, Alessandra Luna-Navarro, Alejandro Prieto, Claudio Vásquez, and Ulrick Knaack. "Influence of Automated Façades on Occupants." Journal of Facade Design and Engineering 10, no. 2 (December 6, 2022): 19–38. http://dx.doi.org/10.47982/jfde.2022.powerskin.02.

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Several studies performing building simulations showed that the automated control of façades can provide higher levels of indoor environmental quality and lower energy demand in buildings, in comparison to manually controlled scenarios. However, in several case studies with human volunteers, automated controls were found to be disruptive or unsatisfactory for occupants. For instance, automated façades became a source of dissatisfaction for occupants when they did not fulfil individual environmental requirements, did not provide personal control options, or did not correctly integrate occupant preferences with façade operation in energy-efficient controls. This article reviews current evidence from empirical studies with human volunteers to identify the key factors that affect occupant response to automated façades. Only twenty-six studies were found to empirically investigate occupant response to automated façades from 1998 onwards. Among the reviewed studies, five groups of factors were found to influence occupant interaction with automated façades and namely: (1) personal factors, (2) environmental conditions, (3) type and mode of operation, (4) type of façade technology, and (5) contextual factors.. Overall, occupant response to automated façades is often poorly considered in research studies reviewed because of the following three reasons: (i) the lack of established methods or procedures for assessing occupant response to automated façade controls, (ii) poor understanding of occupant multi-domain comfort preferences in terms of façade operation, (iii) fragmented research landscape, on one hand results are mainly related to similar contextual or climatic conditions, which undermines their applicability to other climates, while on the other hand the lack of replication within the same conditions, which also undermines replicability within the same condition. Lastly, this paper suggests future research directions to achieve a holistic and more comprehensive understanding of occupant response to automated façades, aiming to achieve more user-centric automated façade solutions and advanced control algorithms. In particular, research on the impact of personal factors on occupant satisfaction with automated controls is deemed paramount.

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23

Weerasinghe, Achini Shanika, Eziaku Onyeizu Rasheed, and James Olabode Bamidele Rotimi. "Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand." Sustainability 15, no. 3 (January 27, 2023): 2305. http://dx.doi.org/10.3390/su15032305.

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Understanding how occupants behave and interact with building systems is vital to energy efficiency in buildings. The building occupants’ behaviours are complex and influenced by diverse factors. A deep understanding of the underlying environmental, contextual, social, and psychological factors is the first step of many in establishing the relationship between the indoor environment and occupants’ behaviours. The current study investigates the influence of occupants’ perceived indoor environmental comfort, the availability of control, and the social-psychological impacts on occupant behaviours in a New Zealand context. The data were collected through online surveys, and 99 office occupants responded. A machine learning technique was applied to identify the critical factors influencing the decision-making of occupant behaviours. Of the occupant behaviours considered in the study, adjusting windows, doors, shades and blinds, and drinking beverages were mostly practised (>70%) while adjusting lighting, personal fans, thermostats/heaters, and computers (40−70%) was moderately practised by occupants. The availability of specific user controls was the main predictor of most occupant behaviours, followed by social-psychological factors such as actual knowledge, perceived knowledge, behavioural interventions, subjective norms, organisational support, personal norms, attitudes, and perceived behavioural control. The indoor environmental parameters such as indoor temperature, indoor air quality, natural light, and inside noise were highlighted as most influential in decision-making for occupant behaviours. Additionally, the demographic factors: gender, work duration, days at work, and permanence/temporariness of workspace, were also impactful. Knowing the complexity of occupants’ decision-making with respect to their behaviours helps building managers use this sensitive information to enhance building energy performance and enable more energy feedback to the occupants to raise their awareness. Such information is helpful for creating an intelligent environmental control system loop with eco-feedback and establishing occupant-centric buildings or features.

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24

Lei, Yue, Sicheng Zhan, Eikichi Ono, Yuzhen Peng, Zhiang Zhang, Takamasa Hasama, and Adrian Chong. "A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings." Applied Energy 324 (October 2022): 119742. http://dx.doi.org/10.1016/j.apenergy.2022.119742.

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25

Abuimara, Tareq, Burak Gunay, and William O’Brien. "An occupant-centric method for window and shading design optimization in office buildings." Science and Technology for the Built Environment 27, no. 2 (November 3, 2020): 181–94. http://dx.doi.org/10.1080/23744731.2020.1840217.

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26

Dabirian, Sanam, Karthik Panchabikesan, and Ursula Eicker. "Occupant-centric urban building energy modeling: Approaches, inputs, and data sources - A review." Energy and Buildings 257 (February 2022): 111809. http://dx.doi.org/10.1016/j.enbuild.2021.111809.

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27

Naylor, Sophie, Mark Gillott, and Tom Lau. "A review of occupant-centric building control strategies to reduce building energy use." Renewable and Sustainable Energy Reviews 96 (November 2018): 1–10. http://dx.doi.org/10.1016/j.rser.2018.07.019.

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28

Peng, Yuzhen, Zoltán Nagy, and Arno Schlüter. "Temperature-preference learning with neural networks for occupant-centric building indoor climate controls." Building and Environment 154 (May 2019): 296–308. http://dx.doi.org/10.1016/j.buildenv.2019.01.036.

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29

Azar, Elie, William O'Brien, Salvatore Carlucci, Tianzhen Hong, Andrew Sonta, Joyce Kim, Maedot S. Andargie, et al. "Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications." Energy and Buildings 224 (October 2020): 110292. http://dx.doi.org/10.1016/j.enbuild.2020.110292.

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30

Kim, Joyce, Stefano Schiavon, and Gail Brager. "Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control." Building and Environment 132 (March 2018): 114–24. http://dx.doi.org/10.1016/j.buildenv.2018.01.023.

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31

Pang, Zhihong, Yan Chen, Jian Zhang, Zheng O’Neill, Hwakong Cheng, and Bing Dong. "Quantifying the nationwide HVAC energy savings in large hotels: the role of occupant-centric controls." Journal of Building Performance Simulation 14, no. 6 (November 2, 2021): 749–69. http://dx.doi.org/10.1080/19401493.2021.1994650.

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32

Inanici, Mehlika, and Alireza Hashemloo. "An investigation of the daylighting simulation techniques and sky modeling practices for occupant centric evaluations." Building and Environment 113 (February 2017): 220–31. http://dx.doi.org/10.1016/j.buildenv.2016.09.022.

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33

Hafizi, Nazgol, and Sadiye Mujdem Vural. "New Taxonomy of Climate Adaptive Building Shell Office Buildings: Focus on User–Façade Interaction Scenarios." Energies 15, no. 14 (July 20, 2022): 5268. http://dx.doi.org/10.3390/en15145268.

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Анотація:

As one of the most critical considerations in the contemporary era, sustainability heightens the need to find more suitable solutions for architectural designs. Climate adaptive building shells (CABS) are among the most promising alternatives for achieving sustainability goals by reducing energy consumption. Regardless of technological developments, this type of system has a reputation for increasing the distraction of occupants and consequently decreasing their satisfaction level. This research has been developed to focus on the occupant-centric study rather than technological advancements of the system. This study introduces the user–façade interaction scenarios and applies this classification on CABS office buildings. The purpose of this study is to introduce a new multi-domain taxonomy for CABS office buildings and update the database of this system by adding a new variable focusing on occupants. The study was designed on the foundation found with PRISMA methodology which highlights the lack of occupant-centric research on CABS. The research carried on as a qualitative method with an inductive approach which with the literature review introduced the user–façade interaction scenarios and the latest update of the CABS database. Accordingly, the office cases were categorized within different climatic zones, and later as a correlational study, each case was studied based on user–façade interaction scenarios. Analysis of case databases according to user–façade interaction types clears the lack of development in the majority of scenarios. Lastly, the study concluded by introducing a novel multi-domain taxonomy of CABS office buildings by considering user–façade interaction scenarios. The further value of this study is to be a foundation for future studies on CABS office buildings by considering the occupants as a primary element of the research.

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34

Yang, Haoguang, Mythra V. Balakuntala, Jhon J. Quiñones, Upinder Kaur, Abigayle E. Moser, Ali Doosttalab, Antonio Esquivel-Puentes, et al. "Occupant-centric robotic air filtration and planning for classrooms for Safer school reopening amid respiratory pandemics." Robotics and Autonomous Systems 147 (January 2022): 103919. http://dx.doi.org/10.1016/j.robot.2021.103919.

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35

Das, Anooshmita, Masab Khalid Annaqeeb, Elie Azar, Vojislav Novakovic, and Mikkel Baun Kjærgaard. "Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods." Applied Energy 269 (July 2020): 115135. http://dx.doi.org/10.1016/j.apenergy.2020.115135.

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36

Pang, Zhihong, Yan Chen, Jian Zhang, Zheng O'Neill, Hwakong Cheng, and Bing Dong. "Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates." Applied Energy 279 (December 2020): 115727. http://dx.doi.org/10.1016/j.apenergy.2020.115727.

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37

Stopps, Helen, Brent Huchuk, Marianne F. Touchie, and William O'Brien. "Is anyone home? A critical review of occupant-centric smart HVAC controls implementations in residential buildings." Building and Environment 187 (January 2021): 107369. http://dx.doi.org/10.1016/j.buildenv.2020.107369.

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38

Kompos, Launonen, Navarro, Sohre, Schubert, and Zacharis. "Transforming Energy Efficiency into an Entirely New Customer Experience—An Effective Way to Engage Consumers—The UtilitEE Project Concept." Proceedings 20, no. 1 (July 26, 2019): 21. http://dx.doi.org/10.3390/proceedings2019020021.

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Анотація:

In this paper, we present the concept of UtilitEE project, an innovative and realistic solution for delivering a customer-oriented Behavioural Change Framework based on an open ICT ecosystem integrated into the building with low cost, off-the-shelf sensors. The solution also incorporates human-centric intelligent control features that use occupant comfort profiles and supportively control HVAC and lighting systems to minimize energy waste, while always keeping occupants comfortable and preserving a healthy indoor environment. Key part of the technical development work is the end users' involvement in co-designing the user interfaces and its features. In the following sections, the objectives and the preliminary high-level system architecture are presented along with the pilot deployment activities for system validation and demonstration.

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Zhang, Jingsi, Xiang Zhou, Song Lei, and Maohui Luo. "Energy and comfort performance of occupant-centric air conditioning strategy in office buildings with personal comfort devices." Building Simulation 15, no. 5 (November 4, 2021): 899–911. http://dx.doi.org/10.1007/s12273-021-0852-1.

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40

Alsharif, Rashed, Mehrdad Arashpour, Emadaldin Mohammadi Golafshani, M. Reza Hosseini, Victor Chang, and Jenny Zhou. "Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings." Journal of Building Engineering 46 (April 2022): 103846. http://dx.doi.org/10.1016/j.jobe.2021.103846.

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41

Kjærgaard, Mikkel B., Omid Ardakanian, Salvatore Carlucci, Bing Dong, Steven K. Firth, Nan Gao, Gesche Margarethe Huebner, et al. "Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings." Building and Environment 177 (June 2020): 106848. http://dx.doi.org/10.1016/j.buildenv.2020.106848.

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42

Lassen, Niels, and Francesco Goia. "A theoretical framework for classifying occupant-centric data streams on indoor climate using a physiological and cognitive process hierarchy." Energy and Buildings 241 (June 2021): 110935. http://dx.doi.org/10.1016/j.enbuild.2021.110935.

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43

Kong, Meng, Bing Dong, Rongpeng Zhang, and Zheng O'Neill. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study." Applied Energy 306 (January 2022): 117987. http://dx.doi.org/10.1016/j.apenergy.2021.117987.

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44

Sharmin, Tanzia, Mustafa Gül, and Mohamed Al-Hussein. "A user-centric space heating energy management framework for multi-family residential facilities based on occupant pattern prediction modeling." Building Simulation 10, no. 6 (May 19, 2017): 899–916. http://dx.doi.org/10.1007/s12273-017-0376-x.

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45

O'Brien, William, Andreas Wagner, Marcel Schweiker, Ardeshir Mahdavi, Julia Day, Mikkel Baun Kjærgaard, Salvatore Carlucci, et al. "Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation." Building and Environment 178 (July 2020): 106738. http://dx.doi.org/10.1016/j.buildenv.2020.106738.

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46

Wagner, Andreas, and William O'Brien. "Virtual special issue editorial – State-of-the-art in occupant-centric building design and operation: A collection of reviews." Building and Environment 180 (August 2020): 107026. http://dx.doi.org/10.1016/j.buildenv.2020.107026.

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47

He, Xinglei, Xiaohan Zhang, Rui Zhang, Jiaxin Liu, Xiaoyu Huang, Jinchen Pei, Jingyang Cai, Fen Guo, and Yichun Wang. "More intelligent and efficient thermal environment management: A hybrid model for occupant-centric thermal comfort monitoring in vehicle cabins." Building and Environment 228 (January 2023): 109866. http://dx.doi.org/10.1016/j.buildenv.2022.109866.

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48

Yayla, Alperen, Kübra Sultan Świerczewska, Mahmut Kaya, Bahadır Karaca, Yusuf Arayıcı, Yunus Emre Ayözen, and Onur Behzat Tokdemir. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings." Sustainability 14, no. 23 (December 2, 2022): 16107. http://dx.doi.org/10.3390/su142316107.

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Анотація:

Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves.

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Liu, Xuebo, Yingying Wu, and Hongyu Wu. "PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors." Sustainability 13, no. 24 (December 14, 2021): 13826. http://dx.doi.org/10.3390/su132413826.

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Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.

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Wang, Yuxiao, Yunsong Han, Yuran Wu, Elena Korkina, Zhibo Zhou, and Vladimir Gagarin. "An occupant-centric adaptive façade based on real-time and contactless glare and thermal discomfort estimation using deep learning algorithm." Building and Environment 214 (April 2022): 108907. http://dx.doi.org/10.1016/j.buildenv.2022.108907.

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