The abnormal operation patterns in building energy systems can be revealed by analyzing their historical operational data. In practice, the amount of data is so tremendous that manual data analysis is challenging. Visual data mining is a promising solution to this problem. This study proposes a generic visual data mining-based framework for extracting abnormal operation patterns in building energy systems from their historical operational data. The framework consists of three steps. First, a kernel density estimation-based approach is utilized to preprocess the raw data. Then, a decision tree-based approach is adopted to identify the system operation conditions. Finally, a maximal frequent subgraph mining-based approach is developed to reveal the system operation patterns. The framework is applied to analyze the one-year operational data of a chiller plant. This study proves that the framework can appropriately interpret the data mining results, and can make the analysis of the results more convenient.

可以通过分析其历史运行数据来揭示建筑能源系统中的异常运行模式。实际上，数据量如此之大，以至于手动数据分析具有挑战性。可视数据挖掘是解决该问题的有希望的解决方案。这项研究提出了一个基于可视数据挖掘的通用框架，用于从建筑能源系统的历史运行数据中提取异常运行模式。该框架包括三个步骤。首先，利用基于核密度估计的方法对原始数据进行预处理。然后，采用基于决策树的方法来识别系统运行状况。最后，开发了一种基于最大频繁子图挖掘的方法来揭示系统操作模式。该框架可用于分析冷水机组的一年运营数据。这项研究证明该框架可以适当地解释数据挖掘的结果，并且可以使结果的分析更加方便。