Non-intrusive multi-label load monitoring via transfer and contrastive learning architecture.

• A transfer and contrastive learning architecture identifying multi-label loads. • Contrastive learning architecture to extract deep features. • Transfer learning design solves the sparsity of multi-label appliances. • Gramian angular field encoding enhances the feature extraction efficiency. • Verifications on both public datasets and real-world measurements from China. To achieve the goal of peaking carbon emissions globally and carbon neutrality, smart energy management is a promising way to boost energy conservation and estimate the residential potential for regional demand response, among which the non-intrusive load monitoring technique is highlighted due to its effectiveness on the residential side. However, the identification of multi-label appliance switching operations is still a challenge in this field, which may critically affect the total identification results due to the few-shot learning problem and the complicated overlap of features belonging to different appliances. Therefore, this paper proposed a transfer and contrastive learning architecture to identify multi-label appliances effectively. In the first stage, Gramian angular field encoding is implemented to visualize power sequences to highlight the correlation between timestamps and enhance the feature extraction efficiency. Secondly, a contrastive learning architecture is implemented to learn the general distinguishing features between samples of different labels, and density-based spatial clustering of applications with noise clustering is utilized to detect multi-label samples. Thirdly, transfer learning is utilized to enhance the multi-label identification capacity of contrastive learning structures based on the existing trained model. Finally, the effectiveness of the proposed algorithm is verified through two low-frequency non-intrusive load monitoring public datasets and real-world measurements from a pilot project in China. The results show that the proposed architecture can achieve the efficacy of deep features extraction and few-shot learning in identifying multi-label appliance switching operations. [ABSTRACT FROM AUTHOR]