AI and Digital Twin Applications in Building Energy Management: A State-of-the-Art Review

Purpose: This paper examines the integration of Artificial Intelligence (AI) and digital twin technologies in commercial and residential buildings, focusing on self-learning systems and intelligent energy management solutions. Methods: A comprehensive review synthesizes recent developments in AI applications, digital twin architectures, and building automation systems, with emphasis on autonomous control and occupant-centric optimization. Results: The analysis reveals promising trends in AI-enhanced digital twins for building systems, highlighting their capability to learn from occupant behavior, make data-driven decisions, optimize performance in real-time, and seamlessly integrate with other building technologies to create an efficient, comfortable environment. Conclusions: The findings demonstrate the transformative potential of AI-driven digital twins in modern buildings, offering practical guidance for implementing these technologies while prioritizing occupant comfort, energy efficiency, and sustainability through self-learning building systems.