Добірка наукової літератури з теми "Occupant-centric"

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.

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.

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.

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.

The proportion of older people (i.e., those aged 65 or over) in the world’s population is increasing due to historically low fertility rates combined with increased life expectancy. In order to respond to these demographic trends, a growing body of policy and research over the last decades has accepted that ageing-in-place is most beneficial in the interests of older people’s independence, health and wellbeing, as well as to reduce the economic burden on governments and society for the provision of aged care facilities. While there are several guidelines that provide information about designing dwellings to suit ageing-in-place, information to aid older people’s thermal comfort and related wellbeing is not always considered. This thesis addresses the current knowledge on thermal comfort of older people in order to provide environments that meet their individual requirements and help improve their overall wellbeing. Traditionally, thermal comfort standards adopt aggregate modelling approaches as the bases on which to establish the requirements for human occupancy in the built environment. Aggregate models explain thermal comfort at a population level, which can result in limitations in real scenarios as individual thermal perceptions can vary significantly. In recent years, a growing number of studies have been conducted to address these limitations by developing ‘personal comfort models’. Instead of an average response from a large population, personalised models predict individuals’ thermal comfort by using a single person’s direct feedback. Nonetheless, up until the research presented in this thesis, studies on personal comfort models have focused on younger adults, generally in office environments. This presents a critical research gap because intergroup heterogeneity in personal capabilities and needs tends to be greater among older people, causing the use of aggregate models for older adults to result in even more frequent exposure to unacceptable thermal environments. These, in turn, can interact with multiple comorbidities, leading to adverse health outcomes and possibly premature institutional care. Thus, personalising models hold the promise of a more accurate way to predict older people’s thermal comfort and to manage their thermal environments better. Considering the issues and opportunity presented above, the goal of this research is to advance the current knowledge on the use of personal thermal comfort models by focusing on older people in their home environments. The research aims to achieve this goal by: (1) reviewing the present understandings of personal comfort models, (2) investigating older people’s’ thermal environment, behaviours and preferences; (3) developing personal comfort models for older people and comparing the results with the predictions by established aggregate models; and (4) investigating the application of personal comfort models in managing the thermal environment of older people. Two indoor environmental monitoring field studies and related point-in-time thermal comfort surveys were conducted to collect datasets for the analyses. The first dataset was collected from 71 older adults in 57 households located in South Australia across 9 months. This was followed by the application of deep learning (i.e., a class of machine learning) to develop personal comfort models for 28 out of these 71 participants using different combinations of the collected series of indoor environmental measurements, along with behavioural and health/wellbeing survey answers. The second dataset was collected during shorter 2-week periods involving 11 of the original 71 participants, during which, in addition to measuring the indoor environmental parameters and collecting behavioural and health/wellbeing survey answers, the participants’ hand skin temperatures were measured. The development of personal models for 4 of these participants was then conducted, including skin temperatures as an additional modelling input. Several performance indicators, including average accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were employed to assess the skill of the developed individual models. All models’ performance indicators were then compared with a ‘version’ of the Predicted Mean Vote (PMV) model, termed, in this thesis, the PMVc. The results showed that the 28 personal thermal comfort models for older adults that used environmental, behavioural and health/wellbeing perception as input variables presented an average accuracy of 74%, an average Cohen’s Kappa Coefficient of 61% and an average (AUC) of 0.83. This represented a significant improvement in predictive performance when compared with the generalised PMVc model, which presented an average accuracy of 50%, an average Cohen’s Kappa Coefficient of 24%, and an average AUC of 0.62. Similarly, the exploration with the 4 personal comfort models adding skin temperature measurements to the abovementioned input variables, and excluding health/wellbeing perception − which yielded slightly lower performance when included −, resulted in an average accuracy of 67%, an average Cohen’s Kappa Coefficient of 50% and an average AUC of 0.77. This also represented a superior predictive performance of the individualised models when compared with the PMVc model. In order to investigate the applications of the personal comfort models in operation, two participants were selected as case studies and their respective personal models were tested for their ability to estimate personal heating and cooling temperature set points, using calibrated building performance simulation models. The simulated energy loads derived from the use of personal set points were compared with simulated energy loads using 21°C as the heating set point and 24°C as the cooling set point, which represented the common averaged set points used in building simulation studies. The results show that, using the personal set points, good agreement between the actual and simulated heating and cooling energy loads was achieved. When comparing the error ratios with the ones resulting from simulations assuming a 21°C set point for heating and a 24°C for cooling, the study also showed that the personal set points significantly outperformed these traditional assumptions. Finally, as a secondary application exploration, one selected participant’s personal model was converted to a smart phone Application (App) format to examine the opportunity to use the model as a web-based smart phone tool to aid designers and caregivers to manage the thermal environments of older people by considering individual requirements. This conversion also proved to be successful, allowing the automatic calculation of thermal preference for the selected participant, thereby demonstrating its potential to aid designers and caregivers. The novelty and therefore the contributions of this research lay in different areas. Whilst the literature on personal comfort models has focussed solely on younger adults in office environments, this research has explored a methodology for predicting thermal comfort of older people in their dwellings. Additionally, it has introduced health/wellbeing perception as a predictor of thermal preference – a variable often overlooked in architectural sciences and building engineering. Finally, the research indicates that, compared with aggregated models, personal models provide superior utility in predicting an individual’s preferred thermal environment, which therefore offers the potential for more accurate tools to design and improve older people’s living environments so that wellbeing is optimised, healthy ageing is fostered and autonomy while ageing is prolonged. The research recommends a range of topics for future investigation, such as the models’ misclassification costs and the integration among wearable sensors, predictors and actuators in the context of older people. In addition, the development of standard protocols necessary for the models’ deployment in real scenarios is prescribed. In conclusion, the research demonstrates that, as a concept, personal comfort models have the ability to absorb people’s diversity in the context of their environmental conditions, and may therefore represent an important step towards providing knowledge aimed at enhancing wellbeing and improving the overall resilience of the built environment.
Thesis (Ph.D.) -- University of Adelaide, School of Architecture and Built Environment, 2022

In the last years, as a result of environmental concerns, changes in lifestyle during the COVID-19 crisis, the role of healthy buildings in addition to the main lighting design principles are highlighted. Therefore, today’s lighting design issues include social well-being, mental well-being, and physical well-being more than we discussed in the last century. Hence, we are familiar with occupant-centric and performance-based metrics for residential and non-domestic buildings. The study analyses the extended occupancy patterns, daylight availability, and annual lighting energy demand through a case study in Bursa, Turkey including the COVID-19 pandemic scenario.

This paper introduces a novel computational method for mapping indoor luminance values to the facade of an open workplace to improve its daylight performance. 180-degree fisheye renderings from different locations, view positions, and times of the year are created. These renderings are then transformed from two-dimensional (2D) images into three-dimensional (3D) hemispheres. High luminance values are filtered and projected from the hemisphere to the facade design. This framework will highlight the areas of the facade that allow too much light penetration into the interior environment. This study introduces a flexible framework that allows for an occupant-centric lighting analysis to compute multiple design parameters and synthesize results based on luminance values mapped on the facade design for localized performance optimization to improve facade performance.

Although blast mitigation seats are historically designed to protect the 50th percentile male occupant based on mass, the scope of the occupant centric platform (OCP) Technology Enabled Capability Demonstration (TEC-D) within the U.S. Army Tank Automotive Research Development Engineering Center (TARDEC) Ground System Survivability has been expanded to encompass lighter and heavier occupants which represents the central 90th percentile of the military population. A series of drop tower tests were conducted on twelve models of blast energy-attenuating (EA) seats to determine the effects of vertical accelerative loading on ground vehicle occupants. Two previous technical publications evaluated specific aspects of the results of these drop tower tests on EA seats containing the three sizes of anthropomorphic test devices (ATDs) including the Hybrid III 5th percentile female, the Hybrid III 50th percentile male, and the Hybrid III 95th percentile male. The first publication addressed the overall trends of the forces, moments, and accelerations recorded by the ATDs when compared to Injury Assessment Reference Values (IARVs), as well as validating the methodology used in the drop tower evaluations1. Review of ATD data determined that the lumbar spine compression in the vertical direction could be used as the “go/no-go” indicator of seat performance. The second publication assessed the quantitative effects of Personal Protective Equipment (PPE) on the small occupant, as the addition of a helmet and Improved Outer Tactical Vest (IOTV) with additional gear increased the weight of the 5th percentile female ATD more than 50%2. Comparison of the loading data with and without PPE determined that the additional weight of PPE increased the overall risk of compressive injury to the lumbar and upper neck of the small occupant during an underbody blast event. Using the same data set, this technical paper aimed to evaluate overall accelerative loading trends of the 5th percentile female ATD when compared to those of the 50th percentile male ATD in the same seat and PPE configuration. This data trend comparison was conducted to gain an understanding of how seat loading may differ with a smaller occupant. The focus of the data analysis centered around the lumbar spine compression, as this channel was the most likely to exceed the IARV limit for the 5th percentile female ATD. Based on the previous analysis of this data set, the lightest occupant trends showed difficulty in protecting against lumbar compression injuries with respect to the 5th percentile female’s IARV, whereas the larger occupants experienced fewer issues in complying with their respective IARVs for lumbar compression. A review of pelvis acceleration was also conducted for additional kinetic insight into the motion of the ATDs as the seat strokes. This analysis included a review of how the weight and size of the occupant may affect the transmission of forces through a stroking seat during the vertical accelerative loading impulse.