IFC-based Information Extraction and Analysis of HVAC Objects to Support Building Energy Modeling

The heating, ventilation, and air conditioning (HVAC) system is a highly complex part of a building that requires high specialty and expertise to understand and analyze for energy modelling and simulation purposes. Significant manual effort is needed for information extraction from the mechanical designs, to support the creation of an energy model, including information such as HVAC system type, cooling/heating load, pressure drop, and thermal zones, etc. However, such information can be readily available in Building Information Modeling (BIM)-based mechanical, electrical, and plumbing (MEP) models. In this paper, data analysis and information extraction were conducted on HVAC systems of industry foundation classes (IFC)-based MEP models. By following the state-of-the-art Data-driven Reverse Engineering Algorithm Development (D-READ) method, an algorithm was developed to automatically parse and extract HVAC information from the IFC models. The algorithm was tested on a commercial building with 1 hot water boiler and 19 radiators, which achieved error-free information parsing and extraction. This is expected to reduce the manual effort in information extraction of HVAC systems for building energy modeling (BEM). It also is built upon and supports the open and neutral IFC-based information workflow, which could be a solid step towards automation and interoperability between BIM and BEM in the HVAC domain.