Zobrazeno 1 - 10
of 306
pro vyhledávání: '"Miller Clayton"'
Publikováno v:
E3S Web of Conferences, Vol 562, p 03004 (2024)
The widespread availability of open datasets in cities is transforming the way urban energy systems are planned, simulated, and visualized. In this paper, a cross-scale approach is pursued to better understand the reciprocal effects between building
Externí odkaz:
https://doaj.org/article/b5d69234120e4a92831e31b91e5dc1ae
Autor:
Miller Clayton, Tan Charlene
Publikováno v:
E3S Web of Conferences, Vol 562, p 06001 (2024)
It’s not just the models, techniques, or technologies that improve building performance; the digital skills of built environment professionals also play a significant part. The deluge of data from buildings, intelligent systems, and simulation tool
Externí odkaz:
https://doaj.org/article/47d29ad30dba4127abfb17b3d25d84f7
The indoor environment greatly affects health and well-being; enhancing health and reducing energy use in these settings is a key research focus. With advancing Information and Communication Technology (ICT), recommendation systems and reinforcement
Externí odkaz:
http://arxiv.org/abs/2411.08734
Autor:
Abdelrahman, Mahmoud, Macatulad, Edgardo, Lei, Binyu, Quintana, Matias, Miller, Clayton, Biljecki, Filip
The concept of digital twins has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a u
Externí odkaz:
http://arxiv.org/abs/2409.19005
Autor:
Chwalek, Patrick, Zhong, Sailin, Perry, Nathan, Liu, Tianqi, Miller, Clayton, Alavi, Hamed Seiied, Lalanne, Denis, Paradiso, Joseph A.
This study presents a comprehensive dataset capturing indoor environmental parameters, physiological responses, and subjective perceptions across three global cities. Utilizing wearable sensors, including smart eyeglasses, and a modified Cozie app, e
Externí odkaz:
http://arxiv.org/abs/2408.08323
Publikováno v:
Energy Build. 2024;312: 114216
Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While gene
Externí odkaz:
http://arxiv.org/abs/2404.00525
Autor:
Liguori, Antonio, Quintana, Matias, Fu, Chun, Miller, Clayton, Frisch, Jérôme, van Treeck, Christoph
Publikováno v:
Energy Build. 2024;310: 114071
Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior
Externí odkaz:
http://arxiv.org/abs/2311.16632
Publikováno v:
Building and Environment (2023), 111112
This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to
Externí odkaz:
http://arxiv.org/abs/2310.04247
The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building information models. Data interoperability is a key component to eliminate
Externí odkaz:
http://arxiv.org/abs/2307.13197
Publikováno v:
Appl Therm Eng. 2024;236: 121545 (2023)
Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple so
Externí odkaz:
http://arxiv.org/abs/2307.05926