Challenges and opportunities of occupant-centric building controls in real-world implementation: A critical review

Energy and Buildings, Год журнала: 2024, Номер 308, С. 113958 - 113958

Опубликована: Фев. 14, 2024

Over the past few decades, attention in buildings’ design and operation has gradually shifted from promoting only energy efficiency objectives to also addressing human comfort well-being. Researchers have developed a wide range of control algorithms ranging rule-based controls complex learning approaches that can fully capture occupants’ personalized preferences smart buildings. This direction occupant-centric building bridge gap between satisfaction sustainability objectives. However, most these promising technologies not yet found their way into real-world applications. study will perform critical review on thermal lighting studies aiming (i) analyze strengths weaknesses different approaches; (ii) identify requirements for techniques be implemented systems; (iii) propose new research directions promote usability such catalyst towards adoption. Computational complexity, integration with Building Automation Systems (BAS), data availability quality, scalability, lack more featuring actual implementation emerge as barriers. Addressing challenges is imperative successful deployment

Язык: Английский

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A hybrid active learning framework for personal thermal comfort models

Building and Environment, Год журнала: 2023, Номер 234, С. 110148 - 110148

Опубликована: Март 1, 2023

Personal thermal comfort models are used to predict individual-level responses inform design and control decisions of buildings achieve optimal conditioning for improved energy efficiency. However, the development data-driven requires collecting a large amount sensor-related measurements user-labelled data (i.e., user feedback) accurate predictions, which can be highly intrusive labour intensive in real-world applications. In this work, we propose hybrid active learning framework reduce collection costs developing data-efficient robust personal that users' air movement preferences. Through proposed framework, evaluated performance two algorithms Uncertainty Sampling Query-by-Committee) labelling strategies (Independent Joint Labelling strategies) reduction effort modelling. The effectiveness was demonstrated on dataset involving 58 participants collected over 10 working days with 2,727 under 16 conditions. final results showed 46% 35% preference models, respectively, increasing reductions occurring time when encountering new users. insights gained study, future studies adopt as viable effective solution address high cost while maintaining model's scalability predictive performance.

Язык: Английский

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Human physiology for personal thermal comfort-based HVAC control – A review

Building and Environment, Год журнала: 2023, Номер 240, С. 110418 - 110418

Опубликована: Май 22, 2023

Standardized methods for thermal comfort assessment already exist, namely the predicted mean vote (PMV) and adaptive model, both valid groups of people. To identify whether a specific person is comfortable under different factors such as thermal, air quality, lighting, acoustics, only current reliable method subjective evaluation. reduce need occupant feedback, personal models are currently being developed that aim to predict response based on information from its surroundings. These leverage machine learning tools have been found provide suitable estimations responses. According literature, an average prediction accuracy 70–80% attainable. Therefore, these promoted innovative efficient ways comfort-based HVAC control. The challenge however identifying most relevant indicators acquiring them in simple way. Integrating anthropometric data, e.g., age, sex, body mass index may represent generating model. Including physiological data skin temperature, heart rate, signal transformation could increase performance. Strong relationships were identified between indicators, their variation was not be governed solely by thermoregulation. Few automatic control implementation examples using shows challenges still exist. In order achieve accurate control, certain issues remain regarding acceptable thresholds model performance optimum set combination it.

Язык: Английский

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What is NExT? A new conceptual model for comfort, satisfaction, health, and well-being in buildings

Building and Environment, Год журнала: 2024, Номер 252, С. 111234 - 111234

Опубликована: Янв. 24, 2024

Язык: Английский

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Reinforcement Learning for Control and Optimization of Real Buildings: Identifying and Addressing Implementation Hurdles

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112283 - 112283

Опубликована: Март 1, 2025

Язык: Английский

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Personal comfort models based on a 6‐month experiment using environmental parameters and data from wearables

Indoor Air, Год журнала: 2022, Номер 32(11)

Опубликована: Ноя. 1, 2022

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their environment. Previous work has critical limitations related to the length of data collected and diversity spaces. This paper outlines longitudinal field study comprising 20 participants who answered Right-Here-Right-Now surveys using smartwatch for 180 days. We more than 1080 field-based per participant. Surveys were matched with environmental physiological measured variables indoors homes offices. then trained tested seven machine learning participant predict preferences. Participants indicated 58% time want no change environment despite completing 75% these at temperatures higher 26.6°C. All but one personal model had median prediction accuracy 0.78 (F1-score). Skin, indoor, near body temperatures, heart rate most valuable accurate prediction. found that ≈250–300 points needed We, however, identified strategies significantly reduce this number. Our provides quantitative evidence on improve models, prove benefits wearable devices preference, validate results from previous studies.

Язык: Английский

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Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Journal of Physics Conference Series, Год журнала: 2023, Номер 2600(14), С. 142003 - 142003

Опубликована: Ноя. 1, 2023

Abstract Collecting feedback from people in indoor and outdoor environments is traditionally challenging complex to achieve a reliable, longitudinal, non-intrusive way. This paper introduces Cozie Apple, an open-source mobile smartwatch application for iOS devices. platform allows complete watch-based micro-survey provide real-time about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of collect physiological (e.g., heart rate, activity) (sound level) data. outlines data collected 48 research participants who used report perceptions urban-scale comfort (noise thermal) contextual factors such as they were with what activity doing. The results 2,400 micro-surveys across various urban settings are illustrated this paper, showing variability noise-related distractions, thermal comfort, associated context. show that experienced at least little noise distraction 58% time, talking being most common reason (46%). effort novel due its focus on spatial temporal scalability collection noise, distraction, information. These set stage larger deployments, deeper analysis, more helpful prediction models toward better understanding occupants’ needs perceptions. innovations could result control signals building systems or nudges change behavior.

Язык: Английский

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Human-Centric Artificial Intelligence of Things–Based Indoor Environment Quality Modeling Framework for Supporting Student Well-Being in Educational Facilities

Journal of Computing in Civil Engineering, Год журнала: 2024, Номер 38(2)

Опубликована: Янв. 3, 2024

Maintaining the quality of indoor environments in educational facilities is crucial for student comfort, health, well-being, and students' learning performance. Current environment maintenance practices building systems facility spaces often fail to include feedback from students exhibit limited adaptability their needs. To address this problem, paper introduces a novel artificial intelligence things (AIoT)-based framework predict multidimensional (IEQ) conditions. The proposed integrates internet (IoT) with deep algorithms systematically incorporate IEQ data multimodal occupants. By collecting, fusing, analyzing real-time occupant data, predicts future condition based on current This yields insights into conditions potential impacts thereby facilitating development climate-adaptive, data-driven, human-centric facilities. was deployed, validated, tested selected at Virginia Tech Blacksburg campus, encouraging results.

Язык: Английский

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Occupant-centric control in buildings: Investigating occupant intentions and preferences for indoor environment and grid flexibility interactions

Energy and Buildings, Год журнала: 2024, Номер 317, С. 114393 - 114393

Опубликована: Июнь 7, 2024

Язык: Английский

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Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions

Building and Environment, Год журнала: 2023, Номер 235, С. 110201 - 110201

Опубликована: Март 20, 2023

Язык: Английский

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