Whole-process AI in building applications: A framework of integrating AI with domain knowledge

Artificial intelligence (AI) has transformed the field of building energy and environment over recent decades. It enables substantial improvements in energy efficiency, cost-effective operation, and carbon emission mitigation in buildings. Despite these advances, the applications of AI in real-world engineering still face barriers in interpretability, physics-consistency, and generalization. These limitations lead to a performance gap between objective function of AI model and the targets of final application. To bridge the performance gap of AI applications in buildings, this perspective paper focuses on how domain knowledge can be integrated throughout the AI workflow in building energy and environmental applications. Furthermore, this paper proposes a framework of whole-process AI in building applications as a fundamental perspective of integrating AI and domain knowledge. The framework emphasizes that domain knowledge can be systematically embedded throughout the entire AI application process, including data preparation, model structure, model training, and performance evaluation. Rather than viewing AI as a substitute for domain knowledge, this study argues that domain knowledge is critical to application-oriented AI solutions. In particular, the novel technologies of large language models and physics-informed machine learning are highlighted as promising pathways to integrate data and physics in AI applications. This study promotes a synergistic integration of AI technologies and domain knowledge, enabling further improvements on whole-process application performance of AI in the building domains.