Optimizing AMI Control Centres through Machine Learning: A Review

Abstract

Modernizing electrical grids requires advanced metering infrastructure (AMI), which gives users and suppliers energy usage data and boosts grid efficiency. Utility smart meter operation centres monitor and analyse these benefits to maintain them. However, the massive data quantities make manual data management impractical. This paper discusses how data analytics and machine learning (ML) can automate and optimize this process to improve control centre decision-making. Our research examines global smart meter implementation and how ML helps operators with identifying problems, preventive maintenance, network selection and cybersecurity. These applications decrease manual labour, enhance accuracy and boost productivity. We also discuss recent AMI trends to help utilities, governments and regulators plan energy. This article shows ML's disruptive potential in smart meter management by focusing on network dependability, operational safety, maintenance optimization and cybersecurity. Our findings show how ML permits utilities to provide a seamless, robust and customer-centred experience, bolstering AMI as a modern electric grid basis.