A text analytic framework for gaining insights on the integration of digital twins and machine learning for optimizing indoor building environmental performance

Recent technological advancements in distributed sensing, pervasive computing, context-awareness, machine learning and Digital Twins (DTs) allow the built environment to cope with upcoming challenges in a better way than before and achieve comfort and well-being in buildings. This paper takes a unique approach by not conducting a systematic and exhaustive review, that would require enormous effort to uncover intricate interdependencies among various subtopics. Instead, it proposes a framework leveraging Artificial Intelligence and Machine Learning (AI/ML) techniques to extract valuable insights from the existing literature. Adopting the Digital Twin high-level architecture as its foundation, the paper introduces a clustering approach to scrutinize Indoor Environmental Quality, Energy Efficiency, and Occupant Comfort-key facets influencing indoor building performance. This innovative methodology aims to provide a more nuanced understanding of the relationships within these critical aspects by harnessing the capabilities of AI/ML techniques and the conceptual framework of Digital Twin architecture.