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.
Highlights
Today, the application of Non-Intrusive Load Monitoring (NILM) is significant because of its advances in communications technologies and artificial intelligence
Compared to the above approaches, this paper presents a novel method based on a convolutional neural network (CNN)-long-term short-term memory network (LSTM)-RF model approach for NILM
This study is based on the Reference Energy Disaggregation Data Set (REDD) [38] and the Pecan Street dataset (PSD) [39]
Summary
The application of Non-Intrusive Load Monitoring (NILM) is significant because of its advances in communications technologies and artificial intelligence. Consumers can have a more comprehensive understanding of their electricity behaviors and detailed bills, which can help them to develop an energy-aware behavior. With these insights, a reduction in operating costs from grid operators and electricity costs for ending consumers can be achieved. The first category is based on a transient analysis of high-frequency sampling signals. These methods identify electrical behaviors through improved measurement methods [1] or feature extracting methods [2]-[4]. Reviews on this category are available in [5]-[7]
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