A Non-Intrusive Load Decomposition Model Based on Multiple Electrical Parameters to Point

Meng Yang, Zhiyou Cheng () and Xinyuan Liu
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Meng Yang: School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
Zhiyou Cheng: Education Ministry Key Laboratory of Power Quality and Energy Storage Research Center, Anhui University, Hefei 230601, China
Xinyuan Liu: Education Ministry Key Laboratory of Power Quality and Energy Storage Research Center, Anhui University, Hefei 230601, China

Energies, 2024, vol. 17, issue 17, 1-25

Abstract: The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-frequency industrial load data, in this paper, we propose a vertical non-intrusive load disaggregation model that is different from the sliding window method. By training multiple electrical parameters at a single point on the bus end with the corresponding load data at the branch end, the proposed method, called multiple electrical parameters to point (Mep2point), takes the electrical parameter data sampled at a single point on the bus end as its input and outputs the load data of the target device sampled at the corresponding point. First, the electrical parameters of the bus end are processed, and each item is normalized to the range from 0–1. Then, the electrical parameters are vertically arranged by their time point, and a convolutional neural network (CNN) is used to train the model. The proposed method is analyzed on low-frequency industrial user data sampled at a frequency of 1/120 Hz in the real world. We compare our method with three advanced sliding window methods, achieving an average improvement ranging from 9.23% to 22.51% in evaluation metrics, while showing substantial superiority in the actual decomposed images. Compared with three classical machine learning algorithms, our model, using the same amount of data, significantly outperforms these methods. Finally, we also compared our method with the multi-channel low window sequence-to-point (MLSP) method, which also selects multiple electrical parameters. Our model’s complexity is much less than that of the MLSP model, and its performance remains high. The superiority of our model, as presented in this paper, is fully verified by experimental analysis, which can produce better actual load decomposition results from each branch and contribute to the analysis and monitoring of loads in industrial environments.

Keywords: non-invasive load decomposition; multiple electrical parameters to point; low-frequency load decomposition; load decomposition of industrial equipment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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