Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins

Wang, Weixi; Guo, Han Ding; Li, Xiaoming; Tang, Shengjun; Lv, Zhihan

doi:10.1016/j.seta.2021.101897

Cited by 46 publications

(32 citation statements)

References 35 publications

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“…Based on this information, BIM can be utilized to record and simulate the energy-consumption situation. In the process of energy-consumption simulation, the digital twin in BIM has a significant contribution; it can map the diverse properties of physical devices into virtual cyberspace to form an identical model that can be used as the basis for energy consumption simulations [70]. Based on the abovementioned technologies, the facility management personnel can utilize computational fluid dynamics, thermal radiation analysis, and illumination analysis to conduct energy consumption simulation and energy performance optimization [70].…”

Section: Energy Managementmentioning

confidence: 99%

“…In the process of energy-consumption simulation, the digital twin in BIM has a significant contribution; it can map the diverse properties of physical devices into virtual cyberspace to form an identical model that can be used as the basis for energy consumption simulations [70]. Based on the abovementioned technologies, the facility management personnel can utilize computational fluid dynamics, thermal radiation analysis, and illumination analysis to conduct energy consumption simulation and energy performance optimization [70]. With the integration of BIM and the Internet of Things (IoT), it can automatically control and optimize energy utilization according to the light environment around the building and in conjunction with historical movement data [71].…”

Section: Energy Managementmentioning

confidence: 99%

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Building Information Modeling (BIM) Capabilities in the Operation and Maintenance Phase of Green Buildings: A Systematic Review

2022

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In recent years, green buildings have gradually become a worldwide trend. Compared with traditional buildings, green buildings have advanced requirements and standards in their operation and maintenance phase. In such a context, some studies proposed that building information modeling (BIM) is an effective method to improve green buildings’ operation and maintenance quality. The aim of this study is to perform a comprehensive review of the BIM capabilities in the operation and maintenance phase of green buildings through a systematic literature review. To achieve this aim, the PRISMA protocol was used to perform this systematic review. The whole systematic review was conducted between January 2022 and April 2022: 128 articles were included. In the process of study, Web of Science (WoS) and Scopus were adopted as bibliographic repositories. Through this study, it can be determined that BIM capabilities can be utilized in the facility management of the green building in the following aspects: safety and emergency management, maintenance and repair, energy management, security, retrofit and renovation, space management, and asset management. Secondly, these BIM capabilities were discussed, and the challenges and shortcomings of BIM capabilities in the operation and maintenance phase of green buildings were reviewed. Finally, a comprehensive overview of BIM capabilities in the facility management of green buildings was developed, and suggestions for future study were provided.

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Section: Energy Managementmentioning

confidence: 99%

Section: Energy Managementmentioning

confidence: 99%

Building Information Modeling (BIM) Capabilities in the Operation and Maintenance Phase of Green Buildings: A Systematic Review

2022

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“…Ref. [11] suggests a framework for cloud computing systems to increase QoS while lowering the cost of providing services to end users. The system focuses on condensing VMs based on current resource utilization, creating virtual networks between VMs, and dynamically configuring virtual hubs.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Ahmed

Garg

Rai³

et al. 2022

Electronics

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For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

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“…Virtualization technology provides an effective solution for resource management in cloud computing model [ 18 , 19 ]. By encapsulating services in virtual machines and mapping them to physical servers, virtualization technology can remap virtual machines and physical resources according to the change of load, so as to dynamically realize the load balance of the whole system.…”

Section: Intelligent Community Architecture Design Based On Iotmentioning

confidence: 99%

Design and Implementation of Smart Community Big Data Dynamic Analysis Model Based on Logistic Regression Model

Hong

2022

Computational Intelligence and Neuroscience

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With the economic development, smart communities have been widely studied and applied. However, the system in this field is not perfect, and there are still a series of problems, such as high construction cost, low level of intelligence, mutual independence of different systems, difficulty in unified management, and so on. To solve the above problems, this paper proposes the smart community big data dynamic analysis model based on logistic regression model. First, this paper constructs the big data research architecture of smart community based on IOT technology, including IAAs, DAAS, PAAS, and SaaS layers and the virtual service layer of resource scheduling of spatiotemporal information cloud platform optimized by spatiotemporal law. And the IoT platform is designed to collect data to lay the foundation for research. Second, this paper is oriented to the big data application requirements with distribution and mobility as the main technical characteristics. Based on the distributed data flow, this paper designs mining operator to provide technical support for the data mining algorithm; at the same time, this paper constructs a high-dimensional random matrix model for measuring big data and then deduces its abnormal data detection theory and method to detect high-dimensional abnormal data. Finally, this paper uses logistic regression model to predict the development trend of smart community and provide guarantee for smart community service. The simulation results show the efficiency and accuracy of prediction can be improved based on logistic regression model. Furthermore, it effectively avoid repeated construction and waste of resources in the community and form a new community management model based on intelligent and information-based social management and service.

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Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins

Cited by 46 publications

References 35 publications

Building Information Modeling (BIM) Capabilities in the Operation and Maintenance Phase of Green Buildings: A Systematic Review

Building Information Modeling (BIM) Capabilities in the Operation and Maintenance Phase of Green Buildings: A Systematic Review

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Design and Implementation of Smart Community Big Data Dynamic Analysis Model Based on Logistic Regression Model

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