建筑复杂用能系统运行状态的异常检测研究对建筑安全稳定、高效节能运行具有重要意义，但是由于受到建筑外部环境因素繁多、内部人行为的不确定性、设备运行数据复杂等多方面因素的影响，其异常运行状态的检测存在信息特征提取困难、运行异常状态难以界定等问题。本研究在运行参数分类的基础上，利用信息熵对不确定性的定量化描述与无监督学习的适应性，提出基于信息熵并融合K-均值聚类与自组织映射模型的复杂用能系统运行状态二次聚类异常检测方法。针对夏热冬暖地区某大型办公建筑中央空调系统的实际监测数据，通过K-均值聚类对外部参数进行初次聚类并划分一次工况、计算各工况下子系统运行参数的信息熵、自组织映射模型二次聚类判定异常运行状态等步骤，实现建筑复杂用能系统的运行状态异常检测。结果表明，本研究提出的方法在单主机运行工况下的平均类内异常检测正确率（简称类内异常检测率）为97.45%，双主机运行工况下的类内异常检测率为96.70%。此外，本研究针对不同工况中类数量与类内异常率相关指标之间的关系展开进一步的讨论与分析，得出结论：单、双主机的类内异常检测率随着其所在类内运行状态总数的减少而升高。本研究为建筑能源大数据背景下的复杂用能系统运行状态异常检测提供了新的系统化思路与方法。

The research on the anomaly detection of the operation state of complex energy consumption system is of great significance to the safe, stable, efficient and energy-saving operation of buildings. However, due to the influence of many factors, such as the variety of external environmental factors of buildings, the uncertainty of insider behavior, the complexity of equipment operation data and so on, the detection of abnormal operation state often meets some difficulties in information feature extraction, abnormal operation state definition and so on. Based on the classification of operation parameters and by using the quantitative description of uncertainty by information entropy and the adaptability of unsupervised learning, this paper proposed a secondary clustering anomaly detection method for the operation state of complex energy consumption system based on information entropy and by integrating K-means and self-organizing mapping model. According to the actual monitoring data of the central air-conditioning system of a large office building in the hot summer and warm winter area, the external parameters were clustered for the first time through K-means, and the steps such as dividing the primary working conditions, calculating the information entropy of the operating parameters of the subsystem under each working condition, and determining the abnormal operating state by secondary clustering of self-organizing mapping model were used to realize the abnormal detection of the operating state of the complex energy consumption system of the building. The results show that the average intra class anomaly detection accuracy of the method proposed in this study is 97.45% under the operation condition of single machine and 96.70% under the operation condition of two machines. In addition, this study further discussed and analyzed the relationship between the number of classes and the relevant indicators of in class anomaly rate under different working conditions. It concludes that the in class anomaly detection rate of single and dual hosts increases with the decrease of the total number of operating states in their class. This study provides a new systematic idea and method for the abnormal operation state detection of complex energy consumption system under the background of building energy big data.