Multiscale Shapelet Contrastive Learning for Nonintrusive Load Monitoring
Yinghua Han, Yuan Li, Zilong Wang, Qiang Zhao · IEEE Transactions on Instrumentation and Measurement · 2025
Non-Intrusive 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 paper proposes a semi-supervised learning method based on multi-scale 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 paper proposes multi-scale 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 paper 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. Experimental results demonstrate that the proposed method not only effectively combines multi-scale features to improve load recognition performance but also exhibits good interpretability.