A new NIALM system design based on neural network architecture and adaptive springy particle swarm optimization algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Energy
Link
https://link.springer.com/content/pdf/10.1007/s12053-023-10125-5.pdf
Reference40 articles.
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3. Azaza, M., & Wallin, F. (2017). Evaluation of classification methodologies and features selection from smart meter data. Energy Procedia, 142, 2250–2256.
4. Basu, K. (2014). Classifcation techniques for non-intrusive load monitoring and prediction of residential loads. PhD thesis.
5. Bonfigli, R., Felicetti, A., Princip, E., Fagiani, M., Squartini, S., & Piazza, F. (2018). Denoising autoencoders for non-intrusive load monitoring: Improvements and comparative evaluation. Energy and Buildings, 158, 1461–1474.
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