首页|Study Findings on Machine Learning Described by Researchers at Cairo University (Modeling indoor thermal comfort in buildings using digital twin and machine lea rning)
Study Findings on Machine Learning Described by Researchers at Cairo University (Modeling indoor thermal comfort in buildings using digital twin and machine lea rning)
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Research findings on artificial intell igence are discussed in a new report. According to news reporting from Giza, Egy pt, by NewsRx journalists, research stated, "Digital Twin (DT) concept is used i n different domains and industries, including the building industry, as it has p hysical 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 an d has a tremendous effect on the lifecycle of a building." The news journalists obtained a quote from the research from Cairo University: " Previous research underscores the importance of seamlessly exchanging informatio n between physical and digital assets within a comprehensive framework, particul arly emphasizing the integration of BIM data with various systems to enhance eff iciency and prevent information loss. Despite advancements in technologies, chal lenges persist in optimizing methods for integrating BIM data into DT frameworks , including ensuring interoperability, scalability, and real-time monitor and co ntrol. This study addresses this research gap by proposing a comprehensive platf orm that integrates the DT concept with IoT and BIM technologies. The platform i s developed in five main stages: 1) acquiring electronic data of the building fr om 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 pr edict thermal comfort. The best predictive model is identified by evaluating its error function using historical training data collected during facility operati on. A case study demonstrates the practical application of the proposed framewor k. The case study involves a real building where the platform is implemented to monitor and control indoor environments."
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