A Unified Architecture for Data-Driven Metadata Tagging of Building Automation Systems

• URL: http://arxiv.org/abs/2003.07690v2
• Date: Fri, 11 Sep 2020 18:27:29 GMT
• Title: A Unified Architecture for Data-Driven Metadata Tagging of Building Automation Systems
• Authors: Sakshi Mishra, Andrew Glaws, Dylan Cutler, Stephen Frank, Muhammad Azam, Farzam Mohammadi, Jean-Simon Venne
• Abstract summary: This article presents a Unified Architecture for automated point tagging of Building Automation System data. We propose a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS. The proposed methodology correctly applied 85-90 percent and 70-75 percent of the tags in each of these test scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article presents a Unified Architecture for automated point tagging of Building Automation System 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 Building Automation System 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: 1. commercial retail buildings and 2. office buildings from the National Renewable Energy Laboratory campus. The proposed methodology correctly applied 85-90 percent and 70-75 percent of the tags in each of these test scenarios, respectively.

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