Journal of System Simulation
Abstract
Abstract: Aiming at the nonlinear and non-stationary characteristics of electrical power consumption data, an abnormal electrical power consumption detection model based on deep autoencoder is proposed. Gated recurrent unit (GRU) network of the deep learning is combined with autoencoder structure, and the encoder and decoder parts of traditional autoencoder are realized by gated recurrent unit network, which gives full play to the data feature extraction capability of gated recurrent unit and the data reconstruction function of autoencoder structure. Based on the reconstruction error between original data and reconstructed data, abnormal data points of the electrical power consumption are detected. By applying the proposed method to actual workshop electrical power consumption data set, it is shown that the proposed method can detect the abnormal points of power consumption data, and the detection effect is better.
Recommended Citation
Sun, Ningke; Wang, Yan; and Ji, Zhicheng
(2022)
"Anomaly Detection Method of Electrical Power Consumption Based on Deep Autoencoder,"
Journal of System Simulation: Vol. 34:
Iss.
12, Article 5.
DOI: 10.16182/j.issn1004731x.joss.22-FZ0931
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss12/5
First Page
2557
Revised Date
2022-09-26
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.22-FZ0931
Last Page
2565
CLC
TP391.9
Recommended Citation
Ningke Sun, Yan Wang, Zhicheng Ji. Anomaly Detection Method of Electrical Power Consumption Based on Deep Autoencoder[J]. Journal of System Simulation, 2022, 34(12): 2557-2565.
DOI
10.16182/j.issn1004731x.joss.22-FZ0931
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