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Smart city article details
| Title | Empowering Smart Cities With Digital Twins Of Buildings: Applications And Implementation Considerations Of Data-Driven Energy Modelling In Building Management |
|---|---|
| ID_Doc | 22926 |
| Authors | Elnour M.; Ahmad A.M.; Abdelkarim S.; Fadli F.; Naji K. |
| Year | 2024 |
| Published | Building Services Engineering Research and Technology, 45, 4 |
| DOI | http://dx.doi.org/10.1177/01436244241239290 |
| Abstract | Smart buildings and cities are rapidly emerging as solutions to address the challenges of efficiency, urbanisation, and sustainability in the sector. The study proposes deploying data-driven digital twins for smart buildings by utilising the available building’s technology and IT infrastructure to complement and augment existing functions. The digital twin will consist of a core data-driven energy model and a 2D visual representation of the building’s systems, with the potential for future evolution into a 3D model. This study aims to present a preliminary investigation into the idea of data-driven digital twins in building management towards enhancing the operations of smart buildings and empowering the concept of smart cities. It is demonstrated on a building on the campus of Qatar University. With an emphasis on the air conditioning systems of the building, considering their substantial contribution to overall energy consumption, the study maintains an open approach to also encompass other energy systems within the buildings, and presents a comparative evaluation between simulation-based and data-driven modelling on the case study, as well as an exploration of various machine learning algorithms that can be used. Furthermore, exploring essential smart applications of the building’s data-driven digital twin. Practical Application: The study provides a comprehensive exploration of the practical aspects of deploying data-driven digital twins for smart buildings, addressing challenges related to data collection, model development, integration with building infrastructure, and potential limitations. The paper aims to advance the field of facility management and promote smart and sustainable practices in building operations. By contributing to the existing knowledge in facility services and management, our study offers practical guidance towards optimising building performance, reducing energy consumption, and fostering sustainable urban development. © The Author(s) 2024. |
| Author Keywords | Building energy modelling; building information modelling; data-driven models; machine learning; practical deployment; smart building; smart cities |
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