Multiscale Shapelet Contrastive Learning for Nonintrusive Load Monitoring

Abstract

Nonintrusive load monitoring (NILM) enables the acquisition of appliance switch states and power consumption information, providing valuable References for energy conservation and emission reduction, making it an important tool for promoting appliance energy efficiency. However, existing NILM methods face significant issues in terms of result interpretability and label dependence. To address these challenges, this article proposes a semi-supervised learning method based on multiscale shapelet contrastive learning. By introducing shapelets, the model captures the current waveform differences generated by different appliances under the same voltage, thereby solving the interpretability problem. Furthermore, some appliances exhibit multiple waveforms due to variations in operating states and supplier differences. Single-scale shapelets are difficult to capture the diverse current information of these appliances. Therefore, this article proposes multiscale shapelets to enhance the discriminative features of different currents for the load and improve the consistency information between different scales, thereby enabling more effective learning of representative load shapelets. To reduce the reliance on a large amount of labeled data, this article adopts contrastive learning, which enhances sample views and performs contrastive optimization to maximize similarity within the same load and minimize similarity between different loads, guiding the model to learn more representative shapelets. Finally, a small amount of labeled data is used to guide the classifier to complete the load recognition task. The experimental results demonstrate that the proposed method not only effectively combines multiscale features to improve load recognition performance but also exhibits good interpretability.