Semi-supervised learning with flexible threshold for non-intrusive load monitoring

Non-intrusive load monitoring (NILM) can obtain fine-grained power consumption information for individual appliances within the user without installing additional hardware sensors. With the rapid development of the deep learning model, many methods have been utilized to address NILM problems and have achieved enhanced appliance identification performance. However, supervised learning models require a substantial volume of annotated data to function effectively, which is time-consuming, laborious, and difficult to implement in real scenarios. In this paper, we propose a novel semi-supervised learning method that combines consistency regularization and pseudo-labels to help identification of appliances with limited labeled data and an abundance of unlabeled data. In addition, given the different learning difficulties of various appliance categories, for example, feature learning is more difficult for multi-state appliances than two-state appliances, the thresholds employed for different appliances are adjusted in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be delivered. Experiments have been conducted on publicly available datasets, and the results indicate that the proposed method attains superior appliance identification performance compared to cutting-edge methods.