Toward Non-I.I.D. in Load Disaggregation: An Unsupervised Domain Adaptation Framework for Heterogeneous Energy Consumption Sectors

This article proposes a generalizable nonintrusive load monitoring (NILM) framework to address nonindependent and identically distributed (Non-I.I.D.) data challenges in heterogeneous energy consumption sectors. The proposed framework uses adversarial feature augmentation based on the observed states of appliances in source sectors, and implements unsupervised domain adaptation for NILM tasks in target. A ConvNet feature extractor is built to extract the features of source samples, which are then, integrated with observed labels for adversarial data augmentation in the feature space. The generated features are used to establish a domain-invariant NILM algorithm in an unlabeled manner. Feature augmentation and domain invariant feature extractors are employed to learn effective feature mapping between the source and target sectors, thereby, accomplishing NILM tasks in Non-I.I.D. samples without additional labels. The experimental results validate the effectiveness of the proposed framework in three scenarios consisting of multiple datasets, with the best performance compared to the five state-of-the-art models.