Statistical Learning for Service Quality Estimation in Broadband PLC AMI

被引：9

作者：

Kim, Dong Sik ^{[1
]}

Chung, Beom Jin ^{[1
]}

Chung, Young Mo ^{[2
]}

机构：

[1] Hankuk Univ Foreign Studies, Dept Elect Engn, Gyeonggi Do 17035, South Korea

[2] Hansung Univ, Dept Elect & Informat Engn, Seoul 02876, South Korea

来源：

ENERGIES | 2019年 / 12卷 / 04期

关键词：

advanced metering infrastructure (AMI); network management system (NMS); power line communication (PLC); service quality analysis; statistical learning;

D O I：

10.3390/en12040684

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, we propose a method to estimate communication performance for the advanced metering infrastructure that employs the power line communication (PLC) technology. Using bit-per-symbol signals from the PLC network management system, we estimate a PLC model quality in terms of packet success rate based on statistical learning. We also verify the accuracy of the estimations by comparing them with measured communication test results at test sites. Finally, from the packet success rate estimate, the qualities of services, such as meter readings and time-of-use pricing data downloading under several metering protocol sequences, are investigated through a mathematical analysis, and numerical results are provided.

引用

页数：20

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