Latent abstraction bridge transformer for generalizable nonintrusive load monitoring
Liu, Yuan, Kong, Zhengmin, Huang, Tao, Yang, Yang, Song, Chenjie, Han, Qing Long, and Huang, Boyang (2026) Latent abstraction bridge transformer for generalizable nonintrusive load monitoring. Scientific Reports, 16 (1). 20046.
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Abstract
Nonintrusive load monitoring (NILM) is an effective approach for energy management that disaggregates the total power measured at the main power inlet into appliance-level power signals. NILM algorithms have achieved remarkable progress in recent years. However, accurately reconstructing appliance-level power signals from unseen, complex, and diverse aggregated data remains a formidable challenge. To address this challenge, this article proposes a novel hybrid load disaggregation model, the Latent Abstraction Bridge (LAB) Transformer, built on a sequence-to-sequence (S2S) framework that integrates a convolutional neural network (CNN) and a Transformer architecture with an embedding-constrained generative network termed LAB. The LAB effectively balances local discrete details and global information by leveraging a soft vector-quantized variational autoencoder (SoftVQ-VAE) and a beta-variational autoencoder (Beta-VAE) to constrain the encoder’s output representations, thereby considerably improving the model’s ability to generalize and discriminate in latent space. Moreover, we use parameter-free linear interpolation to recover the lengths of Beta-VAE output vectors, preserving essential global information while suppressing unnecessary local details, thereby substantially reducing the parameter count. The effectiveness of the proposed model is validated on two datasets: UK-DALE and REFIT. Experimental results indicate that it achieves the best F1 score, while lowering the mean absolute error (MAE) and signal aggregation error (SAE) by 22.8% and 24.7%, respectively, compared to several recent state-of-the-art models.
| Item ID: | 92710 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2045-2322 |
| Keywords: | Beta-variational autoencoder, Latent abstraction bridge (LAB), Nonintrusive load monitoring, Soft vector-quantized variational autoencoder, Transformer |
| Copyright Information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
| Date Deposited: | 19 Aug 2026 22:58 |
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