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• W3166670750 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." @default.
• W3166670750 created "2021-06-22" @default.
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• W3166670750 date "2021-01-01" @default.
• W3166670750 modified "2023-10-02" @default.
• W3166670750 title "Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-Imbalance" @default.
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• W3166670750 doi "https://doi.org/10.1109/access.2021.3087696" @default.
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