Meta-learning-based few-shot identification for novel loads

In recent years, load identification technology has received great attention as the value of real-time load-side electricity information has gradually emerged. There are several ways to precisely identify the different types of loads. However, practical situations with novel load types and little labeled data are seldom considered. For this reason, this paper proposes a few-shot identification method for novel loads based on the Model-Agnostic Meta-Learning (MAML). It uses the Adaptive Weighted Recurrence Graphs (AWRG) model as the base learner, which has the best performance in load identification, and pre-trains the model with existing data. The proposed method uses meta-training to get initial parameters that are generalized across multiple load types to improve the learning ability of the model on few-shot tasks with novel loads. Compared with transfer learning methods commonly used for generalized load identification, the results on the WHITED dataset show that the proposed method can improve the scalability of the load identification for practical applications.