Smart Energy Meter Calibration: An Edge Computation Method: Poster

Abstract

Smart meters are the backbone of smart grids. They provide real time electricity consumption data and and are widely used for measuring, monitoring and analyzing energy consumption. Sometimes, they enable users to perform corrective actions. But, to facilitate proper data analysis, it is imperative that data be accurate or have minimum error. This paper presents an edge deployed smart meter error correction algorithm that utilises Clustering (using K-Means algorithm) and Feed-Forward Artificial Neural Networks (ANN). An edge device, a Raspberry Pi Module, connects smart meters to the internet. The algorithm maps (possibly erroneous) readings of our in-house developed meters to readings of calibrated standard off-the-shelf (Schneider) meters. Usage of Clustering with ANN has helped substantially improve the accuracy of the readings from a previously used linear regression designed for the same purpose. An accuracy of 70-75% was achieved while using linear regression, whereas the proposed algorithm obtains accuracy in the range of 84.47-88%. The neural networks are also less complex, making them suitable for deployment in Raspberry Pi 3B based embedded hardware systems. © 2021 ACM.