A Wavelet Decomposition Network Based Edge Monitoring System for Classification of Electrical Equipment

As the growth number of electrical appliances used in industry and household, identifying the categories of the electrical devices act as a priority to enable an intelligent management system for electricity consumption. Appliance load monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used for optimal energy utilization. However, collecting electrical features from numerous and multivariate appliances may cause a high burden in the communication network and existing identification methods do not perform well for online monitoring. Thus, this paper proposed an edge monitoring system with a multilevel wavelet decomposition network for classification of electrical appliances. The proposed model can achieve an average error of 1.4% on the UCR public dataset and an error rate of 2.7% on the self-built electrical appliance dataset with a maximum service delay of 50 milliseconds.