A Distributed Anomaly Detection Method of Operation Energy Consumption using Smart Meter Data

Along with the rapid development of communication network construction, the operation energy consumption grows significantly in recent years, and the expensive electricity cost is hard to he ignored. Therefore, it is necessary to develop an operation energy anomaly detection mechanism to enhance the control ability of electricity cost. According to the practical distribution and data characteristic of smart meters, this paper presents a distributed anomaly detection method of operation energy consumption based on deep learning methods. An IOT-based distributed structure is implemented to execute data interaction. Stacked sparse autoencoder is used to extract the high-level representation from massive monitoring data acquired automatically from actual smart meter network. Then softmax is used for classification to detect anomaly and send alarm messages using web technologies. The experimental results show that the proposed method with good prospect for intelligent applications achieves better accuracy and meanwhile decreases computing delay caused by central arithmetic method.