Unified architecture for data-driven metadata tagging of building automation systems

Journal Article · Tue Sep 15 00:00:00 EDT 2020 · Automation in Construction

DOI:https://doi.org/10.1016/j.autcon.2020.103411· OSTI ID:1669624

Mishra, Sakshi ^[1]; Glaws, Andrew ^[1]; Cutler, Dylan ^[1]; Frank, Stephen ^[1]; Azam, Muhammad ^[2]; Mohammadi, Farzam ^[2]; Venne, Jean-Simon ^[2]

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1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
2. BrainBox AI, Montreal (Canada)

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: BrainBox AI; USDOE
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1669624
• Alternate ID(s): OSTI ID: 1809654
• Report Number(s): NREL/JA--7A40-75890; MainId:6091; UUID:2d4b4bd1-3f3d-ea11-9c2f-ac162d87dfe5; MainAdminID:18607
• Journal Information: Automation in Construction, Journal Name: Automation in Construction Vol. 120; ISSN 0926-5805
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
97 MATHEMATICS AND COMPUTING
automated tagging
building automation systems
building control systems
building management systems
building metadata
data-driven solution
metadata tagging