Gaussian approximation based lossless compression of smart meter readings

Abuadbba, A, Khalil, I and Yu, X 2017, 'Gaussian approximation based lossless compression of smart meter readings', IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5047-5056.

Document type: Journal Article
Collection: Journal Articles

Title Gaussian approximation based lossless compression of smart meter readings
Author(s) Abuadbba, A
Khalil, I
Yu, X
Year 2017
Journal name IEEE Transactions on Smart Grid
Volume number 9
Issue number 5
Start page 5047
End page 5056
Total pages 10
Publisher IEEE
Abstract Automation metering services, load forecasting and energy feedback are among the great benefits of smart meters. These meters are usually connected using Narrowband power Line Communication (PLC) to transmit the collected waveform readings. The huge volume of these streams, the limited-bandwidth, energy and required storage space pose a unique management challenge. Compression of these streams has a significant opportunity to solve these issues. Therefore, this paper proposes a new lossless smart meter readings compression algorithm. The uniqueness is in representing smart meter streams using few parameters. This is effectively achieved using Gaussian approximation based on dynamic-nonlinear learning technique. The margin space between the approximated and the actual readings is measured. The significance is that the compression will be only for margin space limited points rather than the entire stream of readings. The margin space values are then encoded using Burrow-Wheeler Transform followed by Move-To-Front and Run-Length to eliminate the redundancy. Entropy encoding is finally applied. Both mathematical and empirical experiments have been thoroughly conducted to prove the significant enhancement of the entropy (i.e. almost reduced by half) and the resultant compression ratio (i.e. 3.8:1) which is higher than any known lossless algorithm in this domain.
Subject Networking and Communications
Distributed and Grid Systems
Ubiquitous Computing
Keyword(s) Smart Grid
ISSN 1949-3053
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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
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