Non-Intrusive Load Identification Method Based on Self-Supervised Regularization

Non-intrusive load monitoring is a novel and cost-effective technology for monitoring details of electricity consumption and identifying the operating status of appliances. It supports the construction of the energy internet and big data on electricity consumption in smart cities. However, one of the most challenging problems in this area is that machine learning algorithms often require large amounts of labeled data. In this paper, a non-intrusive load monitoring model based on the Self-supervised Regularization is proposed. The model reduces the pre-processing stage compared to the traditional methods. We make full use of the unlabeled data by using them to generate proxy labels to participate in the model training together with the true labels. We performed experiments on the common data set PLAID to compare performance with the existing method Mean Teacher and CoMatch. The experimental results show that: 1) when using all labeled data, the model with self-supervised regularization significantly improves the traditional supervised classifier with a recognition accuracy of 0.965; 2) when coupled with unlabeled data, our model produces good semi-supervised performance. It is highly competitive with current state-of-the-art Mean Teacher and Contrastive Learning.