A multi-tier architecture for data analytics in smart metering systems

Abstract

With the proliferation of smart meters in smart grids, new challenges have emerged in the energy sector and applications are continuously developed, mainly concerning data analytics to address those challenges. Traditionally, data analytics in smart grid systems is performed in server-side tier; however, it is necessary to process data analytics close to the smart meter to achieve better performance. In order to process data effectively, it is also necessary to implement methodologies to facilitate the integration of data analysis processes in the Advanced Metering Infrastructure (AMI). This paper presents a novel architecture for data analytics in Smart Metering Systems based on an edge-fog-cloud computing architecture that permits different types of data analytics in a multi-tier context. The proposed architecture has the capability of learning and adapting to different contexts in smart metering systems using a reinforcement learning approach. The architecture was tested with three different analytic applications: forecasting energy consumption, prediction of power quality and prediction of energy theft. The results indicate that the methodology can be feasible solution for direct implementation in Smart Metering Systems.