Design information-assisted graph neural network for modeling central air conditioning systems

Abstract

Buildings consume huge amounts of energy to create a comfortable and healthy built environment for people. The building engineering industry has benefitted from the advances in building informatics, including the rich data available in modern buildings and the rapid development in computing technology and data science, for building energy management. Dynamic modeling is often essential to online control and optimization of building energy systems. Data-driven modeling empowered by advanced machine learning has achieved ground-breaking performance in capturing temporal relationships among multivariate building operation data in recent years. However, the structural relationships among the physical entities, e.g., the topology of air conditioning ductworks and terminals, are generally overlooked in existing data-driven modeling methods, although they are very helpful in capturing the relationships among building operation data. This study proposes to represent building air conditioning systems as graphs for machine learning, whose nodes and edges represent physical entities (e.g., VAV terminals) and their connections (e.g., ductwork), respectively. A novel graph neural network-based methodology is developed for dynamic modeling of central air conditioning systems, which consists of three steps, i.e., automated graph structure design, development of graph neural network, and model evaluation and explanation. Viable and generalizable graph structure design methods based on design information, e.g., design drawings and BIM models, and machine learning algorithms for model development and explanation are proposed.