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

Abstract

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.

Keywords

• Indoor environmental quality
• Comfort
• Digital twins
• Artificial intelligence
• Machine learning