Embedded Edge Computing for Real-time Smart Meter Data Analytics

As part of smart grid upgrades, traditional electricity meters are being replaced with smart meters that can improve accuracy, efficiency, and visibility in electrical energy consumption patterns and measurements. However, in most of the deployments, smart meters are only used to digitally measure the energy usage of consumer premises and transmit those data to the utility providers. Despite this, smart meter data can be leveraged into numerous potential applications such as demand side management and energy savings via consumer load identification and abnormality detection. Anyhow, these features are not enabled in most deployments due to high sampling rate requirements, lack of affordable communication bandwidth and resource constraints in analyzing a huge amount of data. This paper demonstrates the suitability of the embedded edge computing paradigm which not only enriches the functionalities but also overcome the limitations of smart meters. It achieves significant improvements in accuracy, latency and bandwidth requirement on smart grid applications via pushing the data analytics into the smart meters. Furthermore, this paper exposes the impact of sampling frequency and digitization resolution in the smart meter data analytics. The experiments are conducted using National Instruments (NI) embedded hardware and the results are reported.