Digital Twins for Thermal Comfort and Energy Efficiency in Buildings: A Systematic Review

Abstract

This systematic review builds upon 51 published empirical studies out of 354 studies that were published between 2020 and 2025 to assess the effectiveness of building-scale digital twins (DTs) in providing thermal comfort and energy efficiency, and improving the indoor environment and system reliability. The results show that there is a rapidly developing field focused on five thematic clusters: system architecture, artificial intelligence and machine learning (AI/ML)-driven control, human-centric engagement, predictive maintenance, and blockchain-enabled cybersecurity. Existing DT frameworks not only achieve real-time building information modeling (BIM)–Internet of Things (IoT) integration with prediction errors under 10%, but reinforcement learning controllers are also able to achieve 25–40% heating, ventilation, and air conditioning (HVAC) energy savings, and human-centric interfaces increase thermal satisfaction from 0.64 up to 1.2 Likert points. Predictive maintenance models have diagnostic accuracies of 91–97%, and new blockchain applications enhance data integrity, but largely at the prototype level. The cross-cluster convergence signifies the transition towards adaptive, socio-technical systems with an equilibrium of efficiency, comfort, reliability, and trust. The major weaknesses identified in this paper were a lack of longitudinal validation, climatic bias and ethical governance. A framework of a modular six-layer architecture is proposed after the review of 51 studies, which facilitates scalable, interoperable, and ethically robust DT deployments.