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

Bin Liu; Zhukui Tan; Zhongxiao Cong; Yong Zhu; Jin Li

doi:10.1117/12.2670309

8 March 2023 Meta-learning-based few-shot identification for novel loads

Bin Liu, Zhukui Tan, Zhongxiao Cong, Yong Zhu, Jin Li

Proceedings Volume 12586, Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022); 1258618 (2023) https://doi.org/10.1117/12.2670309
Event: Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022), 2022, Xiangtan, China

Abstract

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.

Citation Download Citation

Bin Liu, Zhukui Tan, Zhongxiao Cong, Yong Zhu, and Jin Li "Meta-learning-based few-shot identification for novel loads", Proc. SPIE 12586, Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022), 1258618 (8 March 2023); https://doi.org/10.1117/12.2670309

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KEYWORDS

Machine learning

Data modeling

Feature extraction

Power grids

Statistical modeling

Deep learning

Matrices

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