A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid
Author:
Publisher
Springer Science and Business Media LLC
Subject
Energy (miscellaneous),Building and Construction
Link
https://link.springer.com/content/pdf/10.1007/s12273-023-1074-5.pdf
Reference69 articles.
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3. Alami M, Decock J, Kaddah R, et al. (2022). Conv-NILM-Net, a causal and multi-appliance model for energy source separation. In: Proceedings of European Conference on Machine Learning (ECML), MLBEM Workshop.
4. Barsim KS, Yang B (2015). Toward a semi-supervised non-intrusive load monitoring system for event-based energy disaggregation. In: Proceedings of 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, USA.
5. Çavdar İH, Faryad V (2019). New design of a supervised energy disaggregation model based on the deep neural network for a smart grid. Energies, 12: 1217.
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