Optimization of Energy Usage in Data Centers Using AI Techniques

Abstract

Data center energy optimization has come to be a very pressing research problem due to the active evolution of the digital infrastructure and the increasing environmental issues. The existing data centers are highly computing-intensive, with cooling and power distribution thus high operational costs and carbon emissions. The given paper examines the most critical energy consumption characteristics of data centres and discusses more advanced approaches to AI to improve energy efficiency. It discusses AI-based models, such as deep learning, reinforcement learning, and hybrid optimization methods, in predictive workloads, cooling strategies optimization, and better power management. The hybridization of AI with the energy management system, edge-cloud architecture, and smart monitoring systems is also discussed. It puts emphasis on performance measures such as the PUE, CUE, and system reliability. Based on the findings, AI-based optimization of the next-generation data centers can be made much more energy efficient, robust, and sustainable.