Emerald Publishing Limited eBooks, Год журнала: 2024, Номер unknown, С. 107 - 131
Опубликована: Сен. 25, 2024
Journal of Building Engineering, Год журнала: 2024, Номер 89, С. 109354 - 109354
Опубликована: Апрель 18, 2024
Язык: Английский
Процитировано
40
Energy and Buildings, Год журнала: 2024, Номер 323, С. 114746 - 114746
Опубликована: Авг. 31, 2024
Язык: Английский
Процитировано
12
Journal of Building Engineering, Год журнала: 2024, Номер 88, С. 109169 - 109169
Опубликована: Март 29, 2024
The deployment of Unmanned Ground Vehicles (UGVs) offers a promising solution for addressing spatial inefficiency and non-detection zone challenges the Indoor Environmental Quality (IEQ) monitoring area. Nevertheless, it is crucial to assess current state research development in this area, identify persistent challenges, outline future directions. This review examines previous studies prospects. data on four classified clusters (UGV structure components, Monitoring capturing, Data analysis validation, Future directions) were extracted from 30 out total 111 studies. results suggested that UGV navigation could benefit integration 3D environmental models, its sensing reliability performance can be further better evaluated by including human behavior into research, developing practical approaches mitigating sensor response time error calculating optimal velocity during particulate matter capturing enable enhance acquisition stage. Furthermore, leveraging strengths approach advance domains such as method with Building Automation System (BAS), source apportionment, IEQ predictive IAQ simulation air pollutants concentration spikes analysis. findings study offer valuable insights researchers interested implementing UGV-based mobile effective assessment.
Язык: Английский
Процитировано
9
Journal of Building Engineering, Год журнала: 2024, Номер 84, С. 108529 - 108529
Опубликована: Янв. 13, 2024
Язык: Английский
Процитировано
7
Building and Environment, Год журнала: 2023, Номер 243, С. 110661 - 110661
Опубликована: Июль 24, 2023
Язык: Английский
Процитировано
15
Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 203, С. 114791 - 114791
Опубликована: Июль 30, 2024
Язык: Английский
Процитировано
6
Building and Environment, Год журнала: 2024, Номер 253, С. 111277 - 111277
Опубликована: Фев. 10, 2024
Achieving a balance between energy efficiency and thermal comfort is pivotal aspect of sustainable building design. Traditional control methods typically maintain indoor air temperature within predetermined limits, disregarding variable factors like occupancy activity clothing levels, which significantly influence perception. Conversely, comfort-based strategies present an opportunity to automate heating cooling systems, dynamically responding variations in comfort. To achieve this, real-time information on insulation (and its adjustment) indispensable for accurately estimating In this study, we explore the potential novel detection approach capable classifying utilizing optimize operation systems. By doing so, proposed method facilitates delivery conditions tailored user requirements potentially reduces wastage. The development 2 stage computer vision-based framework classification forms core approach. Leveraging deep learning network algorithms, performs recognition tasks, even with limited training data, enabling light, medium heavy clothing. address nonlinearity traditional predicted mean vote (PMV) models, applied piecewise linearization our PMV-based optimal strategy. Through initial experimental field tests conducted case study university building, evaluate method's performance. results demonstrate ability classify levels generate profiles. We further analyze impact performance through scenario-based modelling simulations. showed integrating controls enhance overcome limitations predefined or fixed schedules. However, while highlights feasibility multiple occupants engaged diverse activities, acknowledge need refinement accuracy seamless integration
Язык: Английский
Процитировано
5
Building and Environment, Год журнала: 2024, Номер 254, С. 111396 - 111396
Опубликована: Март 11, 2024
Язык: Английский
Процитировано
5
Applied Thermal Engineering, Год журнала: 2024, Номер 245, С. 122853 - 122853
Опубликована: Март 2, 2024
Язык: Английский
Процитировано
4
Sustainability, Год журнала: 2024, Номер 16(18), С. 8032 - 8032
Опубликована: Сен. 13, 2024
The integration of smart buildings (SBs) into cities (SCs) is critical to urban development, with the potential improve SCs’ performance. Artificial intelligence (AI) applications have emerged as a promising tool enhance SB and SC development. authors apply an AI-based methodology, particularly Large Language Models OpenAI ChatGPT-3 Google Bard AI experts, uniquely evaluate 26 criteria that represent services across five infrastructure domains (energy, mobility, water, waste management, security), emphasizing their contributions quantifying impact on efficiency, resilience, environmental sustainability SC. framework was then validated through two rounds Delphi method, leveraging human expert knowledge iterative consensus-building process. framework’s efficiency in analyzing complicated information generating important insights demonstrated via case studies. These findings contribute deeper understanding effects domains, highlighting intricate nature SC, well revealing areas require further realize performance objectives.
Язык: Английский
Процитировано
4