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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Vydáno v:	IEEE access Ročník 9; s. 84306 - 84315
Hlavní autoři:	Zhou, Xiao, Li, Shujian, Liu, Chengxi, Zhu, Haojun, Dong, Nan, Xiao, Tianying
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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 learning model and a new accuracy calculation method. The data processing step consists of a multi-feature and high-dimensional method (MFHDM) and a pre-training process. The deep learning model consists of a convolutional neural network (CNN), a long-term short-term memory network (LSTM) and a random-forest (RF) algorithm. The proposed method addresses the label correlation problem and the class-imbalance problem. To test the proposed method, the Reference Energy Disaggregation Dataset (REDD) and the Pecan Street dataset (PSD) are used. A comparative analysis with several models shows that the proposed method can effectively improve electrical appliance recognition accuracy and realize NILM.
AbstractList	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 learning model and a new accuracy calculation method. The data processing step consists of a multi-feature and high-dimensional method (MFHDM) and a pre-training process. The deep learning model consists of a convolutional neural network (CNN), a long-term short-term memory network (LSTM) and a random-forest (RF) algorithm. The proposed method addresses the label correlation problem and the class-imbalance problem. To test the proposed method, the Reference Energy Disaggregation Dataset (REDD) and the Pecan Street dataset (PSD) are used. A comparative analysis with several models shows that the proposed method can effectively improve electrical appliance recognition accuracy and realize NILM.
Author	Xiao, Tianying Zhou, Xiao Liu, Chengxi Zhu, Haojun Li, Shujian Dong, Nan
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SubjectTerms	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
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Title	Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance
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