Tang Tao, Li Keke, Su Chang, Liu Zhiheng
College of Electrical and Information Engineering, Heilongjiang Institute of Technology, Harbin, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Heliyon. 2024 Jul 15;10(14):e34457. doi: 10.1016/j.heliyon.2024.e34457. eCollection 2024 Jul 30.
Non-intrusive load monitoring (NILM) can obtain fine-grained power consumption information for individual appliances within the user without installing additional hardware sensors. With the rapid development of the deep learning model, many methods have been utilized to address NILM problems and have achieved enhanced appliance identification performance. However, supervised learning models require a substantial volume of annotated data to function effectively, which is time-consuming, laborious, and difficult to implement in real scenarios. In this paper, we propose a novel semi-supervised learning method that combines consistency regularization and pseudo-labels to help identification of appliances with limited labeled data and an abundance of unlabeled data. In addition, given the different learning difficulties of various appliance categories, for example, feature learning is more difficult for multi-state appliances than two-state appliances, the thresholds employed for different appliances are adjusted in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be delivered. Experiments have been conducted on publicly available datasets, and the results indicate that the proposed method attains superior appliance identification performance compared to cutting-edge methods.
非侵入式负载监测（NILM）无需安装额外的硬件传感器，就能获取用户家中各个电器的细粒度功耗信息。随着深度学习模型的快速发展，许多方法已被用于解决NILM问题，并取得了提升的电器识别性能。然而，监督学习模型需要大量的标注数据才能有效运行，这既耗时又费力，且在实际场景中难以实现。在本文中，我们提出了一种新颖的半监督学习方法，该方法结合了一致性正则化和伪标签，以帮助在有限的标注数据和大量未标注数据的情况下识别电器。此外，鉴于不同电器类别的学习难度不同，例如，多状态电器的特征学习比双状态电器更困难，在每个时间步以灵活的方式调整不同电器所采用的阈值，以便能够传递信息丰富的未标注数据及其伪标签。我们在公开可用的数据集上进行了实验，结果表明，与前沿方法相比，所提出的方法具有卓越的电器识别性能。
