A semi-supervised load identification method with class incremental learning.

With the proposal of the carbon peaking and neutrality targets, non-intrusive load monitoring (NILM) is crucial for energy saving and demand response. How to achieve accurate load identification (LI) is the key to the application of NILM. However, it faces a major challenge: how to identify loads accurately from massive incremental unlabeled data streams. To tackle this challenge, we propose a novel method that combines class incremental learning (CIL) and semi-supervised learning (SSL). Our method prevents catastrophic forgetting by preserving samples, distilling knowledge and aligning weights in incremental tasks. Moreover, our method leverages a semi-supervised learning structure called the Temporal Ensembling to exploit unlabeled data and overcome the semi-supervised problem in incremental learning. We test our method on PLAID and WHITED public datasets and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]