A cluster analysis approach to sampling domestic properties for sensor deployment
A cluster analysis approach to sampling domestic properties for sensor deployment
复制标题
DOI：
10.1016/j.buildenv.2023.110032
复制
发表时间：
2023-01
影响因子：
7.4
通讯作者：
T. Menneer;Markus Mueller;S. Townley
中科院分区：
文献类型：
--
作者：
T. Menneer;Markus Mueller;S. Townley
传感器是用于监视公用事业使用的日益广泛的工具（例如，电力）和环境数据（例如，温度）。在大规模项目中，将传感器放置在所有感兴趣的地点通常是不切实际的，有时甚至是不可能的，例如由于传感器数量或访问受限。我们测试聚类分析是否可以用来解决这个问题。我们使用可能影响传感器测量的因素创建潜在传感器站点的集群。集群提供彼此相似且组间不同的站点组。从每个组中抽取几个站点提供了一个子集，可以捕获站点的多样性。我们用两种类型的传感器测试这种方法：公用事业使用（天然气和水）和户外环境。对每种传感器类型进行单独分析，我们使用来自多达298个潜在站点的特征创建聚类。我们对这些集群进行采样，以提供传感器安装的代表性覆盖范围。我们验证的方法使用的数据从传感器安装作为采样的结果，以及使用其他传感器的措施，从所有可用的网站超过一年。结果表明，传感器数据在不同的集群，并与用于创建集群的因素而异，从而提供证据表明，这种基于集群的方法捕获跨传感器站点的差异。这种新颖的方法提供了潜在的传感器站点之间的代表性采样。它可推广到其他传感器类型和任何已知潜在地点影响因素的情况。我们还讨论了未来基于传感器的大型项目的建议。
Sensors are an increasingly widespread tool for monitoring utility usage (e.g., electricity) and environmental data (e.g., temperature). In large-scale projects, it is often impractical and sometimes impossible to place sensors at all sites of interest, for example due to limited sensor numbers or access. We test whether cluster analysis can be used to address this problem. We create clusters of potential sensor sites using factors that may influence sensor measurements. The clusters provide groups of sites that are similar to each other, and that differ between groups. Sampling a few sites from each group provides a subset that captures the diversity of sites. We test the approach with two types of sensors: utility usage (gas and water) and outdoor environment. Using a separate analysis for each sensor type, we create clusters using characteristics from up to 298 potential sites. We sample across these clusters to provide representative coverage for sensor installations. We verify the approach using data from the sensors installed as a result of the sampling, as well as using other sensor measures from all available sites over one year. Results show that sensor data vary across clusters, and vary with the factors used to create the clusters, thereby providing evidence that this cluster-based approach captures differences across sensor sites. This novel methodology provides representative sampling across potential sensor sites. It is generalisable to other sensor types and to any situation in which influencing factors at potential sites are known. We also discuss recommendations for future sensor-based large-scale projects.