Real-time energy data compression strategy for reducing data traffic based on smart grid AMI networks

In the future, the Internet of things (IoT) may provide huge volumes of data. Smart grids are a class of IoT electricity distribution systems that can control bidirectional energy flows between consumers and service providers (Barman et al. “IOT Based Smart Energy Meter for Efficient Energy Utilizat...

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Bibliographic Details
Published in:The Journal of supercomputing 2021-09, Vol.77 (9), p.10097-10116
Main Authors: Huang, Jie-Fu, Zhang, Geng-Hua, Hsieh, Sun-Yuan
Format: Article
Language:English
Subjects:
Advanced metering infrastructure
Algorithms
Compilers
Computer Science
Concentrators
Data compression
Data management
Electric power distribution
Electric power grids
Electricity meters
Energy flow
Energy utilization
Internet of Things
Interpreters
Messages
Processor Architectures
Programming Languages
Smart grid
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Summary:In the future, the Internet of things (IoT) may provide huge volumes of data. Smart grids are a class of IoT electricity distribution systems that can control bidirectional energy flows between consumers and service providers (Barman et al. “IOT Based Smart Energy Meter for Efficient Energy Utilization in Smart Grid”, 978-1-5386-4769-1 1831.00, 2018 IEEE). A typical smart grid features an advanced metering infrastructure (AMI), which automatically collects meter data from widely distributed sensors. A utility company that intends to use AMI must deploy smart meters, data concentrator units, and a meter data management system (MDMS). Concentrators collect ubiquitous messages from smart meters and transmit aggregated data to their MDMS. Although ubiquitous messages may enhance the efficiency of some electricity grids, any excessive volume of messages causes data congestion. This research considers the Reference Energy Disaggregation Data Set. Our proposed algorithm can compress meter data efficiently. Our contributions are as follows: First, thus far, numerous researchers have attempted to address smart grid problems with AMI systems, and here, we provide a relatively complete review of these attempts. Second, this paper presents a strategy to analyze this problem and propose a compression algorithm to compress meter data. This proposed algorithm combines several existing compression algorithms and operates from 2 to 10% more efficiently than previously published algorithms.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-020-03557-8