Non-intrusive load decomposition method based on CNN-LSTM model

Gangyi Xun; Guangwei Yan

doi:10.1117/12.2624706

13 December 2021 Non-intrusive load decomposition method based on CNN-LSTM model

Gangyi Xun, Guangwei Yan

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871T (2021) https://doi.org/10.1117/12.2624706
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

Non-intrusive load monitoring (NILM) is a practical method to provide equipment-level power consumption information, which can be used to improve a variety of application scenarios in smart grids. This paper proposes a CNN-LSTM-based NILM decomposition method, which overcomes the problem of insufficient feature extraction in the existing methods for power decomposition. First, a convolutional neural network (CNN) is used to extract the local features of the aggregated power data. Then, the long short term memory (LSTM) network is introduced to perform global feature extraction on the basis of extracting local features to achieve the fusion of local features and global features. In this way, the proposed method can refer to more comprehensive features when performing power decomposition, which facilitate the decomposition of appliances with different power level. In the simulation experiment on the public data set UKDALE, the average accuracy, recall, and F1 values of the proposed method on multiple electrical appliances of different power levels reached 0.81, 0.94, and 0.86, respectively. At the same time, the MSE and SAE indexes of appliances with simple state were reduced to 1.67 and 1.24, respectively, which fully verifies the effectiveness and advancement of the method proposed in this paper.

Citation Download Citation

Gangyi Xun and Guangwei Yan "Non-intrusive load decomposition method based on CNN-LSTM model", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871T (13 December 2021); https://doi.org/10.1117/12.2624706

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