A systematic review examines how digital twins, combined with AI, are transforming the design and management of smart, energy-efficient buildings.
This systematic review of the scientific literature provides an overview of the applications of AI coupled with digital twins — virtual replicas of real buildings — for the design and management of buildings with a low energy footprint. Continuously fed by data from embedded sensors, these digital twins enable predictive maintenance and permanent performance optimization throughout the life cycle of the building.
The study highlights that Building Information Modeling (BIM) provides the foundation of information, while energy simulation anticipates real-world performance and machine learning refines decisions from large volumes of data — a combination increasingly seen as the norm rather than the exception in the construction of 2026.
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