Abstract 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. 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.

中文翻译：

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楼宇自动化系统数据驱动元数据标记的统一架构

摘要 本文基于数据驱动方法的组合，提出了用于楼宇自动化系统 (BAS) 数据自动点标记的统一架构 (UA)。先进的能源分析应用程序——包括故障检测和诊断以及监督控制——已经成为提高我们建筑环境性能的重要机会。这些分析的有效应用取决于利用来自各种建筑控制和监控系统的结构化数据，但典型的 BAS 实施不采用任何标准化的元数据模式。虽然诸如 Project Haystack 和 Brick Schema 之类的标准已经被开发来解决这个问题，但结构化数据的过程，即标记点以应用标准元数据模式，迄今为止，一直是一个手动过程。此过程通常成本高昂、劳动密集且容易出错。在这项工作中，我们通过提出一种 UA 来解决这一差距，该 UA 通过利用通过连接到 BAS 可访问的数据（包括时间序列数据和原始点名称）来自动化点标记过程。UA 将机器学习中的监督分类和无监督聚类技术交织在一起，并利用它们的确定性和概率输出来通知点标记过程。此外，我们扩展了 UA 以嵌入额外的输入和输出数据处理模块，这些模块旨在解决与此自动化解决方案的实时部署相关的挑战。我们在现实建筑的两个数据集上测试 UA：(i) 商业零售建筑和 (ii) 国家可再生能源实验室 (NREL) 园区的办公楼。所提出的方法在每个测试场景中正确应用了 85-90% 和 70-75% 的标签，分别用于测试 UA 全功能原型的两种截然不同的建筑类型。因此，提议的 UA 为自动标记 BAS 数据提供了有前途的方法，因为它达到了接近 90% 的准确度。进一步建立在这个框架上以算法识别设备类型及其关系是未来的一个合适的研究方向。因此，提议的 UA 为自动标记 BAS 数据提供了有前途的方法，因为它达到了接近 90% 的准确度。进一步建立在这个框架上以算法识别设备类型及其关系是未来的一个合适的研究方向。因此，提议的 UA 为自动标记 BAS 数据提供了有前途的方法，因为它达到了接近 90% 的准确度。进一步建立在这个框架上以算法识别设备类型及其关系是未来的一个合适的研究方向。