BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment

Gan, Vincent J.L.; Luo, Han; Tan, Yi; Deng, Min; Kwok, Helen H.L.

doi:10.3390/s21134401

Cited by 37 publications

(13 citation statements)

References 26 publications

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“…Initially, traditional mathematical methods were used to give precise definitions of the otherwise vague boundaries between the various levels of thermal sensation. As research progressed, machine learning methods (particularly artificial neural network) were proposed to build algorithmic models for evaluation ( 28 ). Artificial neural networks, which are programmed similarly to the human brain, can process large amounts of data.…”

Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Guo

Ren

et al. 2022

Front. Public Health

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BackgroundArtificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.MethodsAll literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.ResultsBeginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.ConclusionResearches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

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Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Guo

Ren

et al. 2022

Front. Public Health

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“…The prediction models use the static or dynamic parameters, sometimes combined with the environmental parameters, to make the estimation. For example, human activity level and clothing type associated with room temperature and humidity are generally considered good features for predicting thermal comfort (Ma et al 2019;Gan et al 2021).…”

Section: Did Data Componentsmentioning

confidence: 99%

Digital ID framework for human-centric monitoring and control of smart buildings

Deng

Wang

et al. 2022

Build. Simul.

Self Cite

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Smart offices can help employers attract and retain talented people and can positively impact well-being and productivity. Thanks to emerging technologies and increased computational power, smart buildings with a specific focus on personal experience are gaining attraction.Real-time monitoring and estimation of the human states are key to achieving individual satisfaction. Although some studies have incorporated real-time data into the buildings to predict occupants' indoor experience (e.g., thermal comfort and work engagement), a detailed framework to integrate personal prediction models with building systems has not been well studied.Therefore, this paper proposes a framework to predict and track the real-time states of each individual and assist with decision-making (e.g., room assignment and indoor environment control). The core idea of the framework is to distinguish individuals by a new concept of Digital ID (DID), which is then integrated with recognition, prediction, recommendation, visualization, and feedback systems. The establishment of the DID database is discussed and a systematic prediction methodology to determine occupants' indoor comfort is developed. Based on the prediction results, the Comfort Score Index (CSI) is proposed to give recommendations regarding the best-fit rooms for each individual. In addition, a visualization platform is developed for real-time monitoring of the indoor environment. To demonstrate the framework, a case study is presented. The thermal sensation is considered the reference for the room allocation, and two groups of people are used to demonstrate the framework in different scenarios. For one group of people, it is assumed that they are existing occupants with personal DID databases. People in another group are considered the new occupants without any personal database, and the public database is used to give initial guesses about their thermal sensations. The results show that the recommended rooms can provide better thermal environments for the occupants compared to the randomly assigned rooms. Furthermore, the recommendations regarding the indoor setpoints (temperature and lighting level) are illustrated using a work engagement prediction model. However, although specific indoor metrics are used in the case study to demonstrate the framework, it is scalable and can be integrated with any other algorithms and techniques, which can serve as a fundamental framework for future smart buildings.

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“…Nowadays, there is a growing interest to integrate CFD simulation with a 3D geometric modeling tool that contains the geometrics and semantics (such as material properties) of the buildings to facilitate the creation of analytical models are [22]. A suitable platform is needed to integrate these necessary data.…”

Section: Introductionmentioning

confidence: 99%

Integrated BIM and VR for Interactive Aerodynamic Design and Wind Comfort Analysis of Modular Buildings

Gan

Liu

2022

Buildings

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Modular building is becoming a common sight due to government policies promoting greater automation and productivity. When moving towards modularity, indoor comfort within volumetric modules, such as levels of humidity and temperature, natural ventilation, and air pollutant transport, have a major effect on human health and well-being. Computational fluid dynamics simulations (CFD) are used to evaluate the efficiency of natural ventilation. However, designers usually find it difficult to visualize the CFD simulation results, which can deepen users’ understanding of the wind environment and help optimize the design of modular buildings. To overcome this challenge, this paper presents an integrated approach based on building information modeling (BIM) and virtual reality (VR), with the aim of analyzing the aerodynamic design and wind comfort for modular buildings. The framework consists of four salient components. First, a new method, combining OpenStreetMap and Dynamo, is proposed to achieve rapid urban modeling of modular buildings. The second step involves the use of CFD to simulate the outdoor wind environment surrounding modular buildings. The third step emphasizes the integration of CFD-computed data with VR applications to create an immersive virtual environment for designers to analyze the wind environment of design alternates. Finally, the visual experience of non-professional users is used to improve the ventilation of the building and support more informed decision marking at the early stage of building design. The proposed framework is illustrated via a case study that focuses on a group of modular housings in the urban area of Singapore. The results indicate that visualization of CFD simulations in VR provides designers with more details regarding the actual space, and it is expected to help optimize the architectural design.

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BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment

Cited by 37 publications

References 26 publications

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Digital ID framework for human-centric monitoring and control of smart buildings

Integrated BIM and VR for Interactive Aerodynamic Design and Wind Comfort Analysis of Modular Buildings

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