A Method for Compression and Deployment of Load Monitoring Model

In recent years, with the development of load monitoring, the actual deployment of load monitoring has become a key issue to maintain the model performance while minimizing the parameters and computations. Although existing computer vision and other fields in a wide range of attempts to modellightweighting, model compression, knowledge distillation and other methods seek to deploy models on edge devices, but the field of load monitoring has not yet been an in-depth attempt. Therefore, this paper proposes a practical load monitoring model compression deployment method. It first constructs colored V-I trajectory maps based on voltage and current signals, uses the VGG16 model commonly used in computer vision for image classification, and finally performs compressed deployment based on model compression methods. The effectiveness of the proposed method is verified by utilizing the WHITED dataset to classify five common appliances. The proposed method lays the foundation for load monitoring model deployment applications.