A novel smart meter data compression method via stacked convolutional sparse auto-encoder
•An efficient and lightweight DNN for smart meter data compression is proposed.•The reconstruction error and computation time is significantly reduced.•The lightweight model is suitable for running on embedded devices.•Grouping compression is proposed to further improve the compression effect. With...
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| Veröffentlicht in: | International journal of electrical power & energy systems Jg. 118; S. 105761 |
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| ISSN: | 0142-0615 |
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| Abstract | •An efficient and lightweight DNN for smart meter data compression is proposed.•The reconstruction error and computation time is significantly reduced.•The lightweight model is suitable for running on embedded devices.•Grouping compression is proposed to further improve the compression effect.
With the wide deployment of smart meters in distribution systems, a new challenge emerges for the storage and transmission of huge volume of power consumption data collected by smart meters. In this paper, a deep-learning-based compression method for smart meter data is proposed via stacked convolutional sparse auto-encoder (SCSAE). An efficient and lightweight auto-encoder structure is first designed by leveraging the unique characteristics of smart meter readings. Specifically, the encoder is designed based on 2D separable convolution layers and the decoder is based on transposed convolution layers. Compared with the existing auto-encoder method and traditional methods, the proposed structure is redesigned, and the parameters and reconstruction errors are efficiently reduced. In addition, cluster-based indexes are used to represent the regularity of power consumption behavior and the relationship between electricity consumption behavior and compression effect is studied. Case studies illustrate that the proposed method can attain significant enhancement in model size, computational efficiency, and reconstruction error reduction while maintaining the most abundant details. And grouping compression considering users’ electricity consumption rules can further improve the compression effect. |
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| AbstractList | •An efficient and lightweight DNN for smart meter data compression is proposed.•The reconstruction error and computation time is significantly reduced.•The lightweight model is suitable for running on embedded devices.•Grouping compression is proposed to further improve the compression effect.
With the wide deployment of smart meters in distribution systems, a new challenge emerges for the storage and transmission of huge volume of power consumption data collected by smart meters. In this paper, a deep-learning-based compression method for smart meter data is proposed via stacked convolutional sparse auto-encoder (SCSAE). An efficient and lightweight auto-encoder structure is first designed by leveraging the unique characteristics of smart meter readings. Specifically, the encoder is designed based on 2D separable convolution layers and the decoder is based on transposed convolution layers. Compared with the existing auto-encoder method and traditional methods, the proposed structure is redesigned, and the parameters and reconstruction errors are efficiently reduced. In addition, cluster-based indexes are used to represent the regularity of power consumption behavior and the relationship between electricity consumption behavior and compression effect is studied. Case studies illustrate that the proposed method can attain significant enhancement in model size, computational efficiency, and reconstruction error reduction while maintaining the most abundant details. And grouping compression considering users’ electricity consumption rules can further improve the compression effect. |
| ArticleNumber | 105761 |
| Author | Wang, Shouxiang Chen, Haiwen Wu, Lei Wang, Jianfeng |
| Author_xml | – sequence: 1 givenname: Shouxiang surname: Wang fullname: Wang, Shouxiang email: sxwang@tju.edu.cn organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China – sequence: 2 givenname: Haiwen surname: Chen fullname: Chen, Haiwen email: haiwen.c@icloud.com organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China – sequence: 3 givenname: Lei surname: Wu fullname: Wu, Lei email: lei.wu@stevens.edu organization: ECE Department, Stevens Institute of Technology, Hoboken, NJ 07030, USA – sequence: 4 givenname: Jianfeng surname: Wang fullname: Wang, Jianfeng organization: State Grid Tianjin Electric Power Company, Tianjin 300000, China |
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| Cites_doi | 10.1016/j.ijepes.2016.03.051 10.1109/TSG.2014.2364686 10.1007/s00429-013-0687-3 10.3390/en12040653 10.1016/j.rser.2015.07.128 10.1109/ISET-India.2011.6145362 10.1109/TSG.2015.2456979 10.1126/science.1127647 10.1109/TSG.2017.2679111 10.1109/TII.2018.2799855 10.1109/TSG.2013.2293957 10.1016/j.rser.2018.03.088 10.1109/TSG.2016.2544883 10.1109/TSG.2015.2513900 10.1016/j.neucom.2015.08.104 10.1126/science.1136800 10.1038/nn.3331 10.1080/03610927408827101 10.1016/0377-0427(87)90125-7 10.1145/1553374.1553463 10.1109/TPWRS.2016.2604389 |
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| SubjectTerms | Auto-encoder Lossy compression Separable convolution Smart meter |
| Title | A novel smart meter data compression method via stacked convolutional sparse auto-encoder |
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