Unified AI/ML Framework For The Optimization Of Renewable Energy Systems: Enhancing Efficiency, Sustainability, And Economic Viability

Abstract

The global transition to renewable energy is hampered by the inherent intermittency and complexity of these sources. This research proposes and validates a comprehensive intelligent energy system framework leveraging Artificial Intelligence (AI) and Machine Learning (ML) to address these challenges. We developed a multi-faceted methodology involving hybrid AI/ML algorithms for predictive forecasting, real-time grid management, and multi-objective optimization. Our key findings demonstrate that the AI-driven system achieves a 95% accuracy in renewable energy forecasting (a 40% error reduction), a 32% improvement in battery lifecycle management, a 25% reduction in peak demand, and an 18% decrease in operational costs. The framework contributed to a 45% reduction in carbon emissions and a 30% reduction in operational expenses. The study concludes that AI/ML-driven optimization is transformative for renewable energy, significantly enhancing efficiency, reliability, and sustainability while providing a viable pathway toward achieving net-zero emissions goals.