使用電力資訊進行主動式學習以應用於家電異常偵測

Abstract

電器老舊或電器使用行為不當所造成火災或故障傷害，成為影響家庭安全的主要因素，如何有效偵測家電異常情況並預先警示使用者進行更換或改善，成為一項重要研究議題。本研究針對此議題結合物聯網與大數據分析技術，應用智慧電表資料分析提出了一套主動式學習的家電異常偵測方法，改善以往方法樣本收集不易的情況。以家庭常見的電器-電風扇為例進行相關驗證，其結果顯示我們的方法較傳統方法能有效改善偵測誤差。異常偵測的結果可讓使用者參考以進行相關電器保養及更換措施，避免因電器故障所引發的危害。

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 big 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 experience use fans as measurement targets for proposed method. The results show that this approach compared to traditional anomaly detection method effectively improve the detection error. Users can refer to the results of anomaly detection to carry out the relevant electrical maintenance and replacement measures to avoid the electrical fault happens and cause harm.