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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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 84306 - 84315
Main Authors: Zhou, Xiao, Li, Shujian, Liu, Chengxi, Zhu, Haojun, Dong, Nan, Xiao, Tianying
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3087696