AI-Driven Energy-Efficient Edge Cloud Architecture with Renewable Energy Integration

This research provides a comprehensive AI-driven framework that integrates edge cloud computing and renewable energy to support sustainable cloud architectures . These are the research’s primary objectives. First. Develop an AI-powered sustainable framework that optimizes energy distribution workload management and resource utilization in edge cloud environments. Two. Develop and implement multi-layered AI techniques (supervised unsupervised and reinforcement learning) for all-encompassing energy optimization. Three. Reduce carbon emissions and grid dependency by 20 to 35 percent by optimizing the integration of renewable energy. Four. System performance and latency have improved by 15–25% for real-time applications. Fifth. Verify the frameworks environmental and economic benefits through comprehensive testing. These are a few of the main findings of the study. An inventive four-layer architecture that integrates renewable energy management edge cloud processing AI optimization and real-time monitoring. hybrid AI systems that dynamically balance energy efficiency and cost optimization system performance. The practical implementation strategies for a variety of edge environments from small-scale IoT networks to large geographically distributed infrastructures. Lastly comprehensive experimental validation demonstrates a 35.2 percent decrease in energy consumption and a 120 ms latency improvement .