A large-scale evaluation of automated metadata inference approaches on sensors from air handling units
A large-scale evaluation of automated metadata inference approaches on sensors from air handling units
复制标题
对空气处理机组传感器的自动元数据推理方法的大规模评估
DOI：
10.1016/j.aei.2018.04.010
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Berges
中科院分区：
文献类型：
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作者：
Jingkun Gao;M. Berges
楼宇自动化系统提供丰富的传感器数据，可以利用数据分析来提高建筑物的能源效率等。然而，由于组织、管理和提取应用程序所需的与传感器相关的元数据（例如，有关其位置、功能等的信息）需要付出很大的努力，因此在建筑物上部署这些应用程序（例如在多个建筑物上进行故障检测和诊断（FDD））仍然是一个挑战。导致该问题的原因之一是使用不同的约定、首字母缩略词和标准来定义此元数据。为了更好地了解问题的本质以及现有解决方案的性能和可扩展性，我们对来自 614 个空气处理机组 (AHU) 的传感器实施并测试了 6 种不同的基于时间序列的元数据推理方法，这些空气处理机组 (AHU) 分布在美国 35 个建筑工地（涵盖 400 多座建筑）。我们推断出基于规则的 FDD 应用所需的 AHU 中的 12 种传感器和执行器：AHU 性能和评估规则 (APAR)。我们的结果表明：（1）这些方法在准确度方面的平均性能在各个建筑工地中相似，但存在显着差异； (2) 对于新的未见过的建筑物，APAR 所需的点类型分类的预期准确度平均为 75%； (3)只要从相邻月份提取训练数据和测试数据，模型的性能就不会下降。
Building automation systems provide abundant sensor data to enable the potential of using data analytics to, among other things, improve the energy efficiency of the building. However, deployment of these applications for buildings, such as, fault detection and diagnosis (FDD) on multiple buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their location, function, etc.), which is required by applications. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. To better understand the nature of the problem, as well as the performance and scalability of existing solutions, we implement and test 6 different time-series based metadata inference approaches on sensors from 614 air handling units (AHU) instrumented in 35 building sites accounting for more than 400 buildings distributed across United States of America. We infer 12 types of sensors and actuators in AHUs required by a rule-based FDD application: AHU performance and assessment rules (APAR). Our results show that: (1) the average performance of these approaches in terms of accuracy is similar across building sites, though there is significant variance; (2) the expected accuracy of classifying the type of points required by APAR for a new unseen building is, on average, 75%; (3) the performance of the model does not decrease as long as training data and testing data are extracted from adjacent months.
从每个房间的传感器单元创建建筑物的房间连接图
DOI：
10.1145/2422531.2422563
发表时间：
2012
期刊：
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影响因子：
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作者：
Ellis C
通讯作者：
Ellis C