Carbon Emission Reduction through AI-Based Energy Optimization in Data Centers

Data centers are at the heart of today’s digital infrastructure, supporting everything from cloud computing to enterprise applications. However, their rapid growth has led to increasing energy consumption and a substantial carbon footprint. This research proposes an AI-based framework for real-time energy optimization in data centers, aiming to significantly reduce power usage and associated carbon emissions. The framework integrates machine learning for workload prediction, decision-tree algorithms for thermal-aware workload scheduling, and deep reinforcement learning for intelligent cooling control. Implemented and validated in a simulated environment using CloudSim and realtime data emulation, the system demonstrated a total energy saving of 22.5% and a carbon emission reduction of approximately 972 kg CO₂e over a one-week test cycle. Comparative analysis against baseline methods confirmed significant improvements in server utilization, cooling efficiency, and IT power consumption. The results illustrate that AI can be a transformative tool for sustainable data center operations, offering a practical pathway toward greener and more efficient digital ecosystems.