Enhancing Energy Systems for Sustainable Power Generation
Abstract
This paper explores the enhancement of energy systems for sustainable power generation through AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional energy production practices, including renewable energy integration, grid optimization, and energy storage management. The study highlights the application of AI techniques such as predictive analytics, optimization algorithms, and reinforcement learning in improving energy efficiency, reducing carbon emissions, and enhancing grid reliability. Additionally, it discusses the integration of AI with smart grids, renewable energy forecasting, and demand response systems to enable real-time monitoring, dynamic control, and demand-side management. The paper also addresses challenges such as grid resilience, cybersecurity, and policy alignment in the deployment of AI-driven engineering solutions in energy systems. It emphasizes the importance of cross-sector collaboration, regulatory support, and public engagement in leveraging AI's potential to accelerate the transition towards a clean, resilient, and sustainable energy future.
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References
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