Navigating the Future of Building Management: A Deep Dive into Prescriptive Digital Twins

Ayu Latifah; Suhono Harso Supangkat; Edi Leksono; Aswin Indraprastha

doi:10.20944/preprints202406.1927.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 June 2024 / Approved: 27 June 2024 / Online: 27 June 2024 (09:21:18 CEST)

This paper presents the development of a prescriptive digital twin model designed to optimize building environments by leveraging advanced smart technologies such as machine learning, artificial intelligence, cloud computing, and the Internet of Things. The study identifies critical factors affecting user activities, including lighting, HVAC, indoor air quality, and acoustics, and incorporates these into the model to enhance user productivity and comfort in workspaces. Our findings demonstrate that the integration of smart technologies can significantly improve workspace efficiency and user satisfaction, providing actionable insights for future implementations. This study not only highlights the potential benefits of prescriptive digital twins in smart buildings but also emphasizes the importance of comprehensive data gathering and analysis from various sources to support further research. Ultimately, our work offers valuable contributions to the field of building management and underscores the need for continued exploration of digital twin technologies.

prescriptive digital twin; optimization; building environment; smart technologies; workspace efficiency

Engineering, Architecture, Building and Construction

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Navigating the Future of Building Management: A Deep Dive into Prescriptive Digital Twins

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