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.