Fire and accidental damage caused by appliance aging or improper operating are the main factors of home security. Therefore, how to detect the anomaly of appliances and promptly warn the users to replace or pay attention to the improvement of the appliances become an important research topic. In this study, we combine the Internet of Things and data analysis technology to this issue and apply smart meter data analysis to propose an anomaly detection method based on active learning to detect home appliance operation anomaly and to overcome the situation that collecting anomaly label is not easy. The experiment uses fans as measurement targets for proposed method. The results show that this approach compared to traditional anomaly detection method effectively improve the detection error.