Building automation system - BIM integration using a linked data structure

• Published in: Automation in construction Vol. 118; p. 103257
• Main Authors: Quinn, Caroline, Shabestari, Ali Zargar, Misic, Tony, Gilani, Sara, Litoiu, Marin, McArthur, J.J.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2020; Elsevier BV
• Subjects: Building automation; Building automation systems; Building information modeling; Building management systems; Buildings; Data management; Data structures; Facilities management; FM-BIM integration; Information management; Internet of Things; Linked data; Machine learning; Time series; Linked data; FM-BIM integration; Building automation systems
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2020.103257

Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings, both monitoring building conditions and managing the building system control points. At present, these controls are prescriptive and pre-determined by the design team, rather than responsive to actual building performance. These are further limited by prescribed logic, possess only rudimentary visualizations, and lack broader system integration capabilities. Advances in machine learning, edge analytics, data management systems, and Facility Management-enabled Building Information Models (FM-BIMs) permit a novel approach: cloud-hosted building management. This paper presents an integration technique for mapping the data from a building Internet of Things (IoT) sensor network to an FM-BIM. The sensor data naming convention and time-series analysis strategies integrated into the data structure are discussed and presented, including the use of a 3D nested list to permit time-series data to be mapped to the FM-BIM and readily visualized. The developed approach is presented through a case study describing the scalability of the approach to integrate a building BAS system with a BIM. The resultant data structure and key visualizations are presented to demonstrate the value of this approach, which permits the end-user to select the desired timeframe for visualization and readily step through the spatio-temporal building performance data. •A linked data approach connects IoT data with BIM.•Acquisition and ingestion, Batch analytics and Integration supports the overall integration.•Time-series navigation of summarized data is facilitated with visual programming languages.•Lab- and full-scale case studies demonstrate the scalability of this approach.