Heating and cooling consumes most of the energy in buildings. Faults and problems in HVAC systems waste up to 20% of heating and cooling energy. Identifying spaces with HVAC problems within a facility remains a major challenge for facility managers.
This study aims to detect spaces with potential problems that causes energy overconsumption, human discomfort, or HVAC work overload. To achieve that, a Building Information Model (BIM)-based framework that combines the output data of building energy simulations, Building Energy Management Systems (BEMS), and Computerized Maintenance Management Systems (CMMS) is proposed. The framework enables BIM components to utilize data collected by the other systems to determine the intended energy performance and compare it with actual energy performance, as well as to provide access to maintenance history and BEMS alarms that occurred in the building at element level.
The framework was tested using data collected from an educational building over a one-month period when the building was unoccupied to prevent users from manipulating the results. Experimental results show that the framework enabled identification of building spaces with abnormal or malfunctioning behavior that was not detected by the BEMS. This study supplements the body of knowledge in facilities energy management by providing a BIM-based framework that utilizes output data of energy simulation, BEMS, and CMMS to locate and detect building spaces with potential problems that need maintenance. Furthermore, it enables facility managers to collect and view relevant data from various systems in one central platform: BIM. It also allows them to adjust their maintenance plans based on the poor behavior of specific spaces within their building.
This study was originally published in the June 2020 edition of ITcon, the Journal of Information Technology in Construction. Learn more at itcon.org.
Citation: Shalabi F, Turkan Y (2020). BIM–energy simulation approach for detecting building spaces with faults and problematic behavior, ITcon Vol. 25, pg. 342-360, https://doi.org/10.36680/j.itcon.2020.020
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