Hybrid Approach for Digital Twins in the Built Environment

Lin, Yu Chang; Tang, Tsz Ling Elaine; Spanos, Costas J.

doi:10.1145/3447555.3466585

Cited by 9 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lin et al (2021) build a building model based on information available to minimize the number of uncertain parameters by combining physics-based equations with a neural network. Other researchers (Daw et al, 2017; Robinson et al, 2022) explored physics-based ML for modeling lake temperature and other dynamical systems.…”

Section: Applications Of ML In Smart Buildingsmentioning

confidence: 99%

Machine Learning for Smart and Energy-Efficient Buildings

Das,

Lin,

Agwan

et al. 2024

Environ. Data Science

Self Cite

View full text Add to dashboard Cite

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the United States, and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Machine learning (ML) has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review some of the most promising ways in which ML has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction to the relevant ML paradigms and the components and functioning of each smart building system we cover. Finally, we discuss the challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research in this field.

show abstract

Section: Applications Of ML In Smart Buildingsmentioning

confidence: 99%

Machine Learning for Smart and Energy-Efficient Buildings

Das,

Lin,

Agwan

et al. 2024

Environ. Data Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the built environment context, DT has evolved as an exhaustive approach to plan, predict, manage, and demonstrate the assets. Consequently, it can provide the industry with benefits in terms of efficiency, safety, reliability, security, innovation, maintenance decision-making, and lowering costs, risk, and design time [92][93][94][95]. Thus, DTs aim to synchronize the real world with a virtual model for seamless administration and control of the construction processes, environmental monitoring, infrastructure solutions, and lifecycle operations [96].…”

Section: Digital Twin-a Live Mirror Of the Assetmentioning

confidence: 99%

Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review

et al. 2023

View full text Add to dashboard Cite

The integration of blockchain and digital twins (DT) for better building-lifecycle data management has recently received much attention from researchers in the field. In this respect, the adoption of enabling technologies such as artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), cloud and edge computing, Big Data analytics, etc., has also been investigated in an abundance of studies. The present review inspects the recent studies to shed light on the foremost among those enabling technologies and their scope, challenges, and integration potential. To this end, 86 scientific papers, recognized and retrieved from the Scopus and Web of Science databases, were reviewed and a thorough bibliometric analysis was performed on them. The obtained results demonstrate the nascency of the research in this field and the necessity of further implementation of practical methods to discover and prove the real potential of these technologies and their fusion. It was also found that the integration of these technologies can be beneficial for addressing the implementation challenges they face individually. In the end, an abstract descriptive model is presented to provide a better understanding of how the technologies can become integrated into a unified system for smartening the built environment.

show abstract

“…In another study, Lydon, et al [28] used the DT for the optimization of concrete roof structures for space conditions by geothermal renewable energy resources. While using ML approaches, Lin, et al [29] created a hybrid DT model for capturing the building's HVAC system dynamic behavior for the occupant's satisfaction and energy efficiency. In another work, Bayer, et al [30] proposed a method for calibration and validation of a DT for a multifunctional and prefabricated façade element for radiant heating.…”

Section: Building Energy Efficiency Through Digital Twinmentioning

confidence: 99%

Energy Efficient Buildings in the Industry 4.0 Era: A Review

Bilal,

Afzal,

Irfan

et al. 2023

Preprint

View full text Add to dashboard Cite

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

show abstract

Hybrid Approach for Digital Twins in the Built Environment

Cited by 9 publications

References 13 publications

Machine Learning for Smart and Energy-Efficient Buildings

Machine Learning for Smart and Energy-Efficient Buildings

Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review

Energy Efficient Buildings in the Industry 4.0 Era: A Review

Contact Info

Product

Resources

About