基于词袋表示和聚类的建筑物数据模式识别
Finding the different patterns in buildings data using bag of words representation with clustering
摘要：
由于节能建筑中记录的大量数据，了解建筑物的运行已成为一项具有挑战性的任务。尽管如此，今天的专家使用可视化工具来分析数据。为了使任务的现实，本文提出了一种方法来自动检测建筑物中的不同模式。K均值聚类用于自动识别冷却器的开启（操作）周期。在接下来的步骤中，通过使用符号聚合近似（SAX）方法将ON循环转换为符号表示。然后将SAX符号转换为词袋表示进行层次聚类。此外，所提出的技术被应用到实际生活中的数据的吸附式制冷机。此外，从所提出的方法和动态时间规整（DTW）的方法的结果进行了讨论和比较。
英文摘要：
The understanding of the buildings operation has become a challenging task due to the large amount of data recorded in energy efficient buildings. Still, today the experts use visual tools for analyzing the data. In order to make the task realistic, a method has been proposed in this paper to automatically detect the different patterns in buildings. The K Means clustering is used to automatically identify the ON (operational) cycles of the chiller. In the next step the ON cycles are transformed to symbolic representation by using Symbolic Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller. Additionally, the results from the proposed method and dynamic time warping (DTW) approach are also discussed and compared.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
