Transferable Nonintrusive Load Monitoring in Smart Grids via Frequency-Division Fusion Scattering Time-Series Transformer

Nonintrusive load monitoring (NILM) aims to accurately identify appliance-level power consumption patterns solely based on the total household power signal, facilitating fine-grained management of smart grid demands. Previous monitoring methods have often focused on classifying time domain power signals or identifying appliance signatures in the frequency domain, lacking sufficient analysis of diverse, sparsely labeled data across different households in the time-frequency domain. We propose a novel model named frequency-division fusion scattering time-series transformer (FFSTT). Specifically, in addition to NILM task-driven token embedding, self-attention, and feed forward blocks, we innovatively employ dual-tree complex wavelet transform for time-frequency transformation of tokens. Distinct feature extraction methods are applied to low- and high-frequency components, respectively, to efficiently separate and learn the power consumption patterns of different appliances. Furthermore, to achieve effective domain transfer among households in different regions, we utilize a small amount of labeled data to perform low-rank fine-tuning on the pretrained FFSTT. Experiments conducted on the REDD and U.K.-DALE datasets confirm that the proposed model achieves state-of-the-art performance across distinct scenarios of available labeled data.