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Springer Nature Switzerland
Reference6 articles.
1. Azizi, E., Beheshti, M.T., Bolouki, S.: A novel event-based non-intrusive load monitoring algorithm. arXiv preprint arXiv:2009.02656 (2020)
2. Precioso, D., Gómez-Ullate, D.: NILM as a regression versus classification problem: the importance of thresholding. arXiv preprint arXiv:2010.16050 (2020)
3. Saraswat, G., Lundstrom, B., Salapaka, M.V.: Scalable hybrid classification-regression solution for high-frequency nonintrusive load monitoring. arXiv preprint arXiv:2208.10638 (2022)
4. Naderian, S.: A novel hybrid deep learning approach for non-intrusive load monitoring of residential appliance based on long short term memory and convolutional neural networks. arXiv preprint arXiv:2104.07809 (2021)
5. Faustine, A., Pereira, L., Bousbiat, H., Kulkarni, S.: UNet-NILM: a deep neural network for multi-tasks appliances state detection and power estimation in NILM. In: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pp. 84–88 (2020)
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