LAPSE:2023.25625
Published Article
LAPSE:2023.25625
Evaluation Model of Operation State Based on Deep Learning for Smart Meter
Qingsheng Zhao, Juwen Mu, Xiaoqing Han, Dingkang Liang, Xuping Wang
March 29, 2023
The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.
Keywords
deep learning, energy load forecasting, operation state, recurrent neural networks, smart grid, smart meter, transfer learning
Suggested Citation
Zhao Q, Mu J, Han X, Liang D, Wang X. Evaluation Model of Operation State Based on Deep Learning for Smart Meter. (2023). LAPSE:2023.25625
Author Affiliations
Zhao Q: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China
Mu J: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China
Han X: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China
Liang D: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan 030024, China
Wang X: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Journal Name
Energies
Volume
14
Issue
15
First Page
4674
Year
2021
Publication Date
2021-08-01
Published Version
ISSN
1996-1073
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PII: en14154674, Publication Type: Journal Article
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LAPSE:2023.25625
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doi:10.3390/en14154674
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