利用数字孪生和机器学习建立建筑物室内热舒适度模型

IF 6.2 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Ziad ElArwady , Ahmed Kandil , Mohanad Afiffy , Mohamed Marzouk
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引用次数: 0

摘要

数字孪生(DT)概念被用于不同的领域和行业,包括建筑行业,因为建筑行业在建筑信息模型(BIM)的帮助下拥有物理和数字资产。各种技术和方法不断丰富着建筑行业,因为在不同建筑阶段产生的数据量相当大,对建筑物的生命周期影响巨大。以往的研究强调了在一个综合框架内无缝交换物理和数字资产之间信息的重要性,特别强调了 BIM 数据与各种系统的集成,以提高效率并防止信息丢失。尽管技术不断进步,但在优化将 BIM 数据集成到 DT 框架的方法方面仍存在挑战,包括确保互操作性、可扩展性以及实时监控。本研究针对这一研究空白,提出了一个将 DT 概念与物联网和 BIM 技术相结合的综合平台。该平台的开发分为五个主要阶段:1)从激光扫描仪中获取建筑物的电子数据;2)开发 Wi-Fi 物联网模块以及物理资产和数字复制品的 BIM 数据;3)构建平台的 DT 元素;4)执行数据分析;5)实施热舒适度预测模型。实施两个机器学习模型(Facebook 预言家、NeuralProphet)来预测热舒适度。通过使用设施运行期间收集的历史训练数据评估其误差函数,确定最佳预测模型。一项案例研究展示了拟议框架的实际应用。该案例研究涉及一栋真实建筑,在该建筑中实施了该平台,以监测和控制室内环境。通过利用 BIM 模型中的预定义数据,该平台确保了数据的准确性、一致性和可用性。案例结果表明,Neuralprophet 提供了良好的预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling indoor thermal comfort in buildings using digital twin and machine learning

Digital Twin (DT) concept is used in different domains and industries, including the building industry, as it has physical and digital assets with the help of Building Information Modeling (BIM). Technologies and methodologies constantly enrich the building industry because the amount of data generated during different building stages is considerable and has a tremendous effect on the lifecycle of a building. Previous research underscores the importance of seamlessly exchanging information between physical and digital assets within a comprehensive framework, particularly emphasizing the integration of BIM data with various systems to enhance efficiency and prevent information loss. Despite advancements in technologies, challenges persist in optimizing methods for integrating BIM data into DT frameworks, including ensuring interoperability, scalability, and real-time monitor and control. This study addresses this research gap by proposing a comprehensive platform that integrates the DT concept with IoT and BIM technologies. The platform is developed in five main stages: 1) acquiring electronic data of the building from the laser scanner, 2) developing a Wi-Fi IoT module and BIM data for physical assets and digital replica, 3) constructing the DT elements of the platform, 4) performing data analysis 5) implementing thermal comfort prediction models. Two machine learning models (Facebook prophet, NeuralProphet) are implemented to predict thermal comfort. The best predictive model is identified by evaluating its error function using historical training data collected during facility operation. A case study demonstrates the practical application of the proposed framework. The case study involves a real building where the platform is implemented to monitor and control indoor environments. By utilizing predefined data in BIM models, the platform ensures data accuracy, consistency, and usability. The case outputs reveal that Neuralprophet provides good prediction results.

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来源期刊
CiteScore
7.40
自引率
1.20%
发文量
31
审稿时长
22 days
期刊介绍: Developments in the Built Environment (DIBE) is a recently established peer-reviewed gold open access journal, ensuring that all accepted articles are permanently and freely accessible. Focused on civil engineering and the built environment, DIBE publishes original papers and short communications. Encompassing topics such as construction materials and building sustainability, the journal adopts a holistic approach with the aim of benefiting the community.
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