Installations of advanced metering infrastructure (AMS) continue to grow among power utilities as the benefits of smart meter communications with grid operators become increasingly evident. However, determining the quality of a smart meter installation is often difficult without physical site presence and local electrical testing. Misconnections could result in lost utility revenue, while earth faults could create shock hazards, and TN-grid
A European power utility faced a regulatory mandate to install AMS across its customer base. In the process of installing these smart meters, it realized it lacked processes for discovering the quality of meter installations. Arundo worked with this utility to deploy machine learning models through Arundo Composer and Arundo Fabric to monitor the quality of its installations based on pre- and post-AMS consumption, GIS, and electrical data. In addition, using Composer, the company was able to easily deploy 3rd party consumption analysis predictions built with TensorFlow.