Source-Free Domain Adaptation With Self-Supervised Learning for Nonintrusive Load Monitoring

Nonintrusive load monitoring (NILM) benefits the planning of energy consumption and time-of-use pricing through disaggregating appliance-level electrical information. However, its widespread adoption and rapid application face significant restrictions and challenges. Variations in energy consumption backgrounds, like user habits and appliance brands, result in substantial distribution disparities in load data, which significantly deteriorate the performance of trained models when applied to new scenarios. Moreover, concerns regarding user privacy and costs further impede the collection of load data when transfer training for adaptability is necessary. To address these issues, we propose a source-free domain adaptation (SFDA) method for NILM to enhance the generalization performance under conditions of severely limited data acquisition. We design a self-supervised subnetwork based on a sequence masking-restoration task to learn domain-invariant features of appliances without the utilization of source-domain dataset and target-domain label data. Furthermore, the entropy minimization and representation subspace distance (RSD) are introduced to align the feature spaces of different domains and mitigate the feature scaling effect on model performance. The cross-house and a cross-dataset adaptation experiment are conducted on four publicly available datasets. The proposed method achieves an average 6.6% improvement in MAE and 7.1% in F1-score over the baseline and performs well compared to other state-of-the-art models using additional training data, which proves the great potential of the proposed method to enhance the generalization with data restrictions.