用電力資訊進行主動式學習以應用於家電異常偵測

Active Learning for Anomaly Detection of Home Appliance Using Electric Power Information

電器老舊與使用行為不當，是造成台灣家庭火災的主要原因，因此如何提早得知電器的異常狀況，並提醒使用者做相關的預防措施，便是一項重要的研究議題。本研究針對此議題使用智慧電錶來收集電器的用電資料，並結合物聯網數據分析技術提出了一套基於主動式學習的家電異常偵測方法，改善了以往方法對於異常樣本特徵收集不易的情況，並以家庭常見的電器-電風扇來做為實際實驗，其實驗結果顯示本研究的方法與傳統的方法相比，偵測的準確度能更有所提升。 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.

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.