Energy consumption of heat pump system accounts for a large part of the total building energy consumption, and the energy-saving operation of heat pump system has always been the focus of researchers. A promising solution to tackling energy wastes during system operations is anomaly detection. In this study, we propose an anomaly detection method for GSHP system in a public building based on mode decomposition based LSTM and statistical modeling method Grubbs’ test. The system energy consumption is predicted using mode decomposition based LSTM algorithm, and the difference between predicted value and actual value is used to detect the abnormal system energy consumption by Grubbs’ test. Results show that detected anomalies can be summarily divided into three categories (parabola anomaly, abrupt anomaly and time related anomaly) depending on their characteristics, and the rationality of detected anomalies are evaluated through field investigation and expert knowledge. This work is enlightening and indicates that the proposed method would efficiently detect the abnormal performance of GSHP system, and find out unreasonable operating patterns.

热泵系统的能耗占建筑物总能耗的很大一部分，热泵系统的节能运行一直是研究者关注的焦点。解决系统运行过程中的能源浪费的一种有前途的解决方案是异常检测。在这项研究中，我们提出了一种基于模式分解的LSTM和统计建模方法Grubbs检验的公共建筑GSHP系统异常检测方法。使用基于模式分解的LSTM算法预测系统能耗，并通过Grubbs检验将预测值与实际值之差用于检测异常系统能耗。结果表明，检测到的异常可以大致分为三类（抛物线异常，异常和与时间有关的异常）取决于它们的特征，并通过现场调查和专家知识评估检测到的异常的合理性。这项工作是有启发性的，并且表明所提出的方法可以有效地检测GSHP系统的异常性能，并找出不合理的工作模式。