Application of Deep Learning in Building Digital Twin—A Review

Digital twin technology is a rapidly developing domain that has garnered substantial interest in recent years. It involves the creation of virtual representations of physical assets and systems, such as buildings, to enable real-time monitoring, control, and optimization of building performance. Digital twin technology provides a comprehensive and data-driven model of the building, incorporating information from various sources, including design information, operational data, and sensory data. This empowers building managers and operators to conduct analysis and optimize building performance, enhance energy efficiency, and reduce operational costs. The implementation of the deep learning (DL) method will provide more accurate results for the usage of digital twin. The focus of this review is on the various DLs for 3 main applications of digital twin which are predictive maintenance, energy optimization, and 3D building model generations. Based on the reviews, the common DL that is being implemented for digital twin has been analyzed; also some of the limitations are discussed.