Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance

Non-Intrusive Load Monitoring (NILM) is particularly important for demand response. This paper proposes an innovative method based on a deep learning model to recognize the working state of electrical appliances using low frequency load data. The approach includes a data processing step, a deep lear...

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Veröffentlicht in:	IEEE access Jg. 9; S. 84306 - 84315
Hauptverfasser:	Zhou, Xiao, Li, Shujian, Liu, Chengxi, Zhu, Haojun, Dong, Nan, Xiao, Tianying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks class-imbalance CNN Correlation Data models Data processing Datasets Deep learning Electric appliances Electrical products Feature extraction Hidden Markov models Load modeling LSTM Machine learning Model accuracy Monitoring multi-label classification NILM
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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