Hybrid Data Compression Mechanism for Smart Meters Data Based on Edge Computing

Cyber-physical seamless networks form the foundation of smart grids. Generally, smart grids are heavily reliant on data-driven components. With the rollout of smart meters, the overall size of energy consumption data has surged dramatically. This creation of big data in smart grids presents numerous challenges, including slower response times, increased telecommunication system loads, and a heightened need for data storage solutions. To address these challenges, various methods have been proposed, with data compression and edge computing being among the most effective. This study focuses on the role of edge computing in smart grid data compression. The primary goal of this paper is to compress energy consumption data, such as electricity usage data captured by smart meters, in edge level. In this context, a new hybrid data compression method is introduced, which requires minimal computing resources and can be implemented at the edge devices. On the other hand, a new smart electricity meter that able to implement the proposed compression algorithm will also be introduced. The performance of this method is evaluated across different hardware and scenarios. Simulation results show that this mechanism reduces data transfer size through the smart meter network by over ۷۰%. Additionally, this mechanism can be used in various section of smart grids, including data storage systems and gateways.

Keywords:

Data Compression , AMI , Edge Computing , Smart Meters , Smart grid