article · Developments in the Built Environment
Integrating Building Information Modelling with digital twin platforms and Internet of Things technologies helps monitor and control the indoor climate of facilities. A comprehensive framework connects laser scanner data, Wi-Fi IoT modules, and building data to bridge physical structures with their digital counterparts. Developed across five stages, the platform encompasses initial data acquisition, digital replica creation, data analysis, and predictive modelling for thermal comfort. Machine learning models, specifically Facebook Prophet and NeuralProphet, were trained and evaluated on historical operational records to identify the most accurate forecasting method. Testing the system on an operational building validated its ability to maintain data accuracy, support real-time monitoring, and manage environmental settings, with NeuralProphet delivering successful predictive results.
Maintaining comfortable indoor environments is essential for building occupants and efficient facility operation. By linking sensors and building design files directly to predictive machine learning tools, building operators can anticipate climate fluctuations rather than simply reacting to them. This approach establishes a practical method for running smarter, automated climate control systems in modern facilities.
The work represents an applied and tested system validated through a real building case study. It could be adopted by facility managers, building automation providers, and smart infrastructure software developers. The framework enables tools that combine physical sensor data and Building Information Modelling to predict indoor comfort and assist automated environmental controls.
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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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DOI: 10.1016/j.dibe.2024.100480
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