Statistical Learning for Service Quality Estimation in Broadband PLC AMI

Dong Sik Kim, Beom Jin Chung and Young Mo Chung
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Dong Sik Kim: Department of Electronics Engineering, Hankuk University of Foreign Studies, Gyeonggi-do 17035, Korea
Beom Jin Chung: Department of Electronics Engineering, Hankuk University of Foreign Studies, Gyeonggi-do 17035, Korea
Young Mo Chung: Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Korea

Energies, 2019, vol. 12, issue 4, 1-20

Abstract: 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.

Keywords: advanced metering infrastructure (AMI); network management system (NMS); power line communication (PLC); service quality analysis; statistical learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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