Fault prediction of smart meter based on spatio-temporal convolution neural network
The faults of smart meters are sudden, complex and multifaceted. A fault prediction method based on spatio-temporal convolutional neural network(ST-CNN) is proposed. Firstly, the sliding window is used to integrate the time information into the characteristic variables, and the input matrix with spa...
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2022-03-01
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| Series: | Dianzi Jishu Yingyong |
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| Online Access: | http://www.chinaaet.com/article/3000147054 |
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| _version_ | 1849420423495680000 |
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| author | Gao Wenjun Xue Binbin Pang Zhenjiang |
| author_facet | Gao Wenjun Xue Binbin Pang Zhenjiang |
| author_sort | Gao Wenjun |
| collection | DOAJ |
| description | The faults of smart meters are sudden, complex and multifaceted. A fault prediction method based on spatio-temporal convolutional neural network(ST-CNN) is proposed. Firstly, the sliding window is used to integrate the time information into the characteristic variables, and the input matrix with space-time characteristics is constructed. Then, combined with CNN, the fault prediction model of smart meter is established, and the model parameters are optimized by adaptive momentum estimation (Adam) algorithm. Finally, the actual field data are used to simulate the fault prediction model of smart meter based on ST-CNN. The results show that this method has high prediction accuracy and strong generalization ability. |
| format | Article |
| id | doaj-art-32d4310b40ac481dbc6aadd7f6bd0c4a |
| institution | Kabale University |
| issn | 0258-7998 |
| language | zho |
| publishDate | 2022-03-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj-art-32d4310b40ac481dbc6aadd7f6bd0c4a2025-08-20T03:31:45ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982022-03-01483596310.16157/j.issn.0258-7998.2122893000147054Fault prediction of smart meter based on spatio-temporal convolution neural networkGao Wenjun0Xue Binbin1Pang Zhenjiang2Beijing Zhixin Microelectronics Technology Co.，Ltd.，Beijing 102299，ChinaBeijing Zhixin Microelectronics Technology Co.，Ltd.，Beijing 102299，ChinaBeijing Zhixin Microelectronics Technology Co.，Ltd.，Beijing 102299，ChinaThe faults of smart meters are sudden, complex and multifaceted. A fault prediction method based on spatio-temporal convolutional neural network(ST-CNN) is proposed. Firstly, the sliding window is used to integrate the time information into the characteristic variables, and the input matrix with space-time characteristics is constructed. Then, combined with CNN, the fault prediction model of smart meter is established, and the model parameters are optimized by adaptive momentum estimation (Adam) algorithm. Finally, the actual field data are used to simulate the fault prediction model of smart meter based on ST-CNN. The results show that this method has high prediction accuracy and strong generalization ability.http://www.chinaaet.com/article/3000147054smart meterfault predictioncnnspatio-temporal |
| spellingShingle | Gao Wenjun Xue Binbin Pang Zhenjiang Fault prediction of smart meter based on spatio-temporal convolution neural network Dianzi Jishu Yingyong smart meter fault prediction cnn spatio-temporal |
| title | Fault prediction of smart meter based on spatio-temporal convolution neural network |
| title_full | Fault prediction of smart meter based on spatio-temporal convolution neural network |
| title_fullStr | Fault prediction of smart meter based on spatio-temporal convolution neural network |
| title_full_unstemmed | Fault prediction of smart meter based on spatio-temporal convolution neural network |
| title_short | Fault prediction of smart meter based on spatio-temporal convolution neural network |
| title_sort | fault prediction of smart meter based on spatio temporal convolution neural network |
| topic | smart meter fault prediction cnn spatio-temporal |
| url | http://www.chinaaet.com/article/3000147054 |
| work_keys_str_mv | AT gaowenjun faultpredictionofsmartmeterbasedonspatiotemporalconvolutionneuralnetwork AT xuebinbin faultpredictionofsmartmeterbasedonspatiotemporalconvolutionneuralnetwork AT pangzhenjiang faultpredictionofsmartmeterbasedonspatiotemporalconvolutionneuralnetwork |