arXiv 建筑语义标准论文· Sakshi Mishra·· 2020-02-27AI 评分69
「英中对照」研究提出楼宇自动化系统数据驱动元数据标注统一架构
A Unified Architecture for Data-Driven Metadata Tagging of Building Automation Systems
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研究提出融合监督分类与无监督聚类的统一架构，可自动完成楼宇自动化系统数据点的元数据标注。该架构融合两类机器学习方法的确定性与概率输出，以楼宇自动化系统可获取的时间序列数据、原始点位名称为输入，还扩展了适配实时部署的数据处理模块。在商业零售建筑、国家可再生能源实验室园区办公楼两类真实建筑数据集测试中，研究主张标签正确应用率分别为85%-90%、70%-75%。
正文 · 原文
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.
| Comments: | 19 pages, 9 figures, accepted for publication in Automation in Construction |
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2003.07690 [cs.CY] |
| (or arXiv:2003.07690v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2003.07690 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1016/j.autcon.2020.103411
DOI(s) linking to related resources |
Submission history
From: Sakshi Mishra [view email]
[v1]
Thu, 27 Feb 2020 00:35:01 UTC (1,054 KB)
[v2]
Fri, 11 Sep 2020 18:27:29 UTC (1,148 KB)
来源：arXiv 建筑语义标准论文 · arxiv.org