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Heating, ventilation, and air-conditioning (HVAC) are vital components in providing a comfortable indoor climate for the occupants of buildings. In commercial buildings, HVAC setpoints are set according to average comfort temperatures. However, individual temperature preferences may be different. The purpose of this study is to explore the means of making HVAC systems respond automatically to local occupant temperature preferences. To create an occupant-centric indoor temperature environment, we propose an online-learning-based control strategy together with its design process. Four essential variables from four domains—time, indoor and outdoor climates, and occupant behavior—are extracted to construct datasets for preference models. A neural network algorithm and corresponding hyperparameters are suggested to model temperature preferences. According to time-dependent setpoints learned from dynamic contexts, a set of specified rules is used to determine setpoints for HVAC systems. For a period of five months, the resulting learning-based temperature preference control (LTPC) was applied to a cooling system of an office space under real-world conditions. The four case study rooms consisted of typical office uses: single-person and multi-person offices. The experimental results indicate that occupant preferences in the individual rooms differ from each other in both time horizon and temperature levels. The results report energy savings of between 4% and 25% as compared to static temperature setpoints at the low values of preferred temperature ranges. Meanwhile, during LPTC, the need for occupant interventions for adjusting room temperatures to fit their preferences was reduced from four to nine weekdays a month to a maximum of one weekday a month. Image 1 • An intelligent control strategy using machine learning techniques was proposed together with its design process for creating occupant-centric indoor climate. • 4%–25% potential energy savings and the temperature comfort improvement were verified through a real-world test. • Applicability for commercial buildings was presented. [ABSTRACT FROM AUTHOR]

Occupant-Centric Control and Operation (OCC) represents a transformative approach to building management, integrating sensing of indoor environmental quality, occupant presence, and occupant-building interactions. These data are then utilized to optimize both operational efficiency and occupant comfort. This paper summarizes the findings from the IEA-EBC Annex 79 research program's subtask on real world implementations of OCC during the past 5 years. First, in Q1 and Q2, we provide a definition and categorization of OCC. Q3 addresses the role of building operators for OCC, while Q4 describes the implications for designers. Then, Q5 and Q6 discuss the role and possibilities of OCC for load flexibility, and for pandemic induced paradigm shifts in the built environment, respectively. In Q7, we provide a taxonomy and selection process of OCC, while Q8 details real world implementation case studies. Finally, Q9 explains the limits of OCC, and Q10 provides a vision for future research opportunities. Our findings offer valuable insights for researchers, practitioners, and policy makers, contributing to the ongoing discourse on the future of building operations management. • Expert summary on recent developments in occupant-centric control (OCC). • We introduce definitions and categorizations of OCC. • We address the role of building operators and the implications for designers. • We discuss the role of OCC for load flexibility. • We explain the limits of OCC and provide future research directions. [ABSTRACT FROM AUTHOR]

The satisfaction of occupants with an indoor environment supplied by a conventional central VAV air-conditioning system was compared to a novel system represented by decentralized ventilation units and a network of passive chilled beams. In parallel with occupant survey measurement of indoor air quality (IAQ) parameters was conducted with the developed wireless IAQ sensing kits. The same occupant cohort was surveyed for both systems, as occupants of the conventionally-conditioning office, known as the ‘Previous Office’ later moved into the office conditioned by the novel system, known as the ‘3for2 Office’. We observed that the perceived thermal comfort and air quality satisfaction of the occupants were markedly higher in the 3for2 Office than in the Previous Office. While occupants of the 3for2 Office could raise or lower indoor air temperatures, they appeared to be consistently satisfied with higher indoor air temperatures than that set by the building management of the Previous Office. The primarily Singaporean occupants of the 3for2 Office also felt thermally comfortable even when indoor air velocities in the office were measured to be below 0.1 m/s recommended by Singaporean Standards. The 3for2 Office's ventilation system was designed to supply outdoor air beyond minimal required ventilation rates, which lead to relatively low levels of C O 2 and PM2.5 concentrations in the 3for2 office. We believe that higher ventilation rates resulting in lower pollutant concentrations are responsible for a higher-than-average survey score on air quality satisfaction. We also believe that enabling occupants to set air temperature in the space resulted in higher-than-average thermal comfort satisfaction. [ABSTRACT FROM AUTHOR]

Mixed-mode buildings use natural ventilation (NV) through operable building envelopes (e.g., windows) and mechanical air-conditioning (AC) systems to condition indoor spaces with the goal of energy savings without compromising occupant comfort requirements. As a result, occupants within such buildings experience different thermal and acoustic conditions. In mixed-mode buildings, however, there is no clear guideline on assessing or predicting occupants' thermal perception (e.g., thermal sensation and acceptability) and the interaction effects between thermal and acoustic comfort. This study investigates thermal and acoustic comfort as well as their interaction effects in mixed-mode buildings under NV and AC modes. A field study was conducted in a mixed-mode office building in Singapore with a tropical climate. The results showed that subjects' actual thermal sensation and thermal acceptability were affected by the operation modes. Elevated air movement by ceiling fans improved subjects' thermal acceptability, particularly in the NV mode. The results indicated the need for a different thermal comfort model even when switching between NV and AC modes within the same day. Traffic noise also significantly affected thermal sensation and thermal acceptability at noise level higher than 55 dBA. The findings suggest that mixed-mode buildings are potentially applicable in the tropics and provide valuable information on the practical operation of mixed-mode buildings from thermal and acoustic perspectives. Future research needs to investigate how to integrate thermal and acoustic studies and their results into mixed-mode operations to improve building energy efficiency and occupant comfort. • Experimental study was conducted in a mixed-mode building in a tropical climate. • Thermal comfort was affected by natural ventilation and air-conditioning operation modes. • Adaptive comfort was appropriate. • Traffic noises affected acoustic annoyance. • Traffic noises impacted thermal acceptability and thermal sensation. [ABSTRACT FROM AUTHOR]

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

Occupancy in buildings is one of the key factors influencing air-conditioning energy use. Occupant presence and absence are stochastic. However, static operation schedules are widely used by facility departments for air-conditioning systems in commercial buildings. As a result, such systems cannot adapt to actual energy demand for offices that are not fully occupied during their operating time. This study analyzes a seven-month period of occupancy data based on motion signals collected from six offices with ten occupants in a commercial building, covering both private and multi-person offices. Based on an occupancy analysis, a learning-based demand-driven control strategy is proposed for sensible cooling. It predicts occupants' next presence and the presence duration of the remainder of a day by learning their behavior in the past and current days, and then the predicted occupancy information is employed indirectly to infer setback temperature setpoints according to rules we specified in this study. The strategy is applied for the controls of a cooling system using passive chilled beams for sensible cooling of office spaces. Over the period of two months both a baseline control and the proposed demand-driven control were operated on forty-two weekdays of real-world occupancy. Using the demand-driven control, an energy saving of 20.3% was achieved as compared to the benchmark. We found that energy savings potential in an individual office was inversely correlated to its occupancy rate. [ABSTRACT FROM AUTHOR]

In recent years, research has emerged to quantitatively and qualitatively understand occupants' interactions with buildings. However, there has been surprisingly little research on building interfaces and how their design, context (e.g., location), and underlying logic impact their usability and occupants' perceived control, as well as the resulting comfort and energy performance. Research is needed to better understand how occupants interact with building interfaces in both commercial and residential applications; both applications are important to address as there are many differences in interface types, level of control and understanding, and even expectations of engagement. This paper provides a cursory review and discussion of select common building interfaces: windows, window shades/blinds, thermostats, and lighting controls. The goal of this paper is to review literature related to these human-building interfaces to explore interface characteristics, current design and use challenges, and relationships between building interfaces and occupants. Human-building interface interactions are complex, more research is needed to understand design, use, and characteristics. Common themes emerged throughout the literature review to explain occupant interactions (or lack of interactions) with building interfaces, which included thermal and visual comfort, ease and access of control, interface/control placement, poor interface/control design, lack of understanding, and social-behavioral dynamics. • Understanding control interfaces and use of building systems is critical. • Literature indicates drivers of occupant interactions with building interfaces. • Well-designed building interfaces can lead to energy savings and occupant comfort. [ABSTRACT FROM AUTHOR]

In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications. • A systematic literature review of models for occupant behavior in buildings is presented. • The review covers window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use. • A biographical database of documents on occupant behavioral models is created and analyzed. • An overview of the scientific production and the scientific landscape on occupant behavioral modeling are presented. • A future outlook in occupant behavioral modeling is drawn. [ABSTRACT FROM AUTHOR]

Recent developments in information and communication technology have sparked the notion of smart buildings; an example of which is occupant-centric control (OCC). In this approach, a control system acquires various data from occupants, the indoor environment, and outdoor climate, and learns or derives useful information for building control, e.g., room occupancy patterns and adaptive set-points. Ultimately, these adaptive control parameters are used to improve both occupant comfort and energy efficiency. Typical OCC approaches have been put forward in concept papers, small-scale experiments, as well as simulations. However, there are relatively few studies in which OCC is implemented in real buildings. In this article, we review OCC research, focusing on field-implementation case studies in actual buildings under realistic use conditions. First, we analyze the topical aspects of these studies, e.g., building types, location, and building systems. Next, we present a methodological review focusing on the different approaches used to implement OCC in existing building systems. Lastly, we investigate the experimental design approach of the reviewed case studies, focusing on measurement and verification techniques. Results highlight wide discrepancies in the implementation approach of OCC case studies and identify common challenges facing OCC in actual buildings. We propose future directions for OCC research by providing recommendations to address these challenges and to standardize OCC implementations. [ABSTRACT FROM AUTHOR]