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

Jie Fu Huang, Geng Hua Zhang, Sun Yuan Hsieh

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)10097-10116
Number of pages20
JournalJournal of Supercomputing
Volume77
Issue number9
DOIs
Publication statusPublished - 2021 Sep

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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