New hybrid deep learning models for multi-target NILM disaggregation
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Energy
Link
https://link.springer.com/content/pdf/10.1007/s12053-023-10161-1.pdf
Reference62 articles.
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2. Alkhulaifi, A., & Aljohani, A. J. (2020). Investigation of Deep Learning-Based Techniques for Load Disaggregation, Low-Frequency Approach. International Journal of Advanced Computer Science and Applications, 11(1).
3. Athanasiadis, C. L., Papadopoulos, T. A., & Doukas, D. I. (2021). Real-Time Non-Intrusive Load Monitoring : A Light-Weight and Scalable Approach. Energy & Buildings, 253, 111523. https://doi.org/10.1016/j.enbuild.2021.111523
4. Ayub, M., & El-Alfy, E.-S. (2020). Multi-Target Energy Disaggregation Using Convolutional Neural Networks. International Journal of Advanced Computer Science and Applications, 11(10), 684–693.
5. Bonfigli, R., Principi, E., Fagiani, M., Severini, M., & Squartini, S. (2017). Non-Intrusive Load Monitoring by Using Active and Reactive Power in Additive Factorial Hidden Markov Models. Applied Energy, 208, 1590–1607. https://doi.org/10.1016/j.apenergy.2017.08.203
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