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- W4382363122 abstract "Abstract Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information for each appliance in the user's home without installing additional hardware sensors. As deep learning has grown quickly, many deep learning algorithms have been used to solve NILM problems, and have achieved good load identification results. However, many supervised learning algorithms need to be based on a large amount of labeled data, which is time-consuming and laborious to obtain, and difficult to implement in actual deployment. In this paper, we propose a semi-supervised learning algorithm that combines consistency regularization and pseudo-labels to help identify appliances with a small amount of labeled data and some unlabeled data. In addition, given the different learning difficulties of different appliances among users, for example, the feature learning difficulty of multi-state appliances is often higher than that of single-state appliances. Therefore, we employ that the threshold values of different appliance classes be changed in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be passed. We have carried out simulation experiments on public dataset, and the experimental results show that the proposed algorithm achieves better appliance identification results than the state-of-the-art methods." @default.
- W4382363122 created "2023-06-29" @default.
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- W4382363122 date "2023-06-28" @default.
- W4382363122 modified "2023-10-18" @default.
- W4382363122 title "Non-intrusive load identification based on semi-supervised learning with flexible threshold and V-I trajectory signature" @default.
- W4382363122 doi "https://doi.org/10.21203/rs.3.rs-3100298/v1" @default.
- W4382363122 hasPublicationYear "2023" @default.
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