AI-based Optimization in Renewable Energy Integration
https://doi.org/10.46610/JEPSE.2025.v011i03.005
Keywords:
Artificial intelligence, Energy forecasting, Machine learning, Optimization, Renewable energy, Smart grid, Sustainable power systemsAbstract
The global demand for clean and sustainable energy has accelerated the adoption of renewable energy sources, including solar, wind, and hydroelectric power. However, integrating these resources into modern power systems poses significant challenges due to their intermittent and unpredictable nature. Conventional optimization techniques often fail to deliver reliable forecasting, efficient scheduling, and stable grid management under uncertain conditions. Artificial Intelligence (AI) has emerged as a transformative solution to address these limitations by enabling advanced data-driven modeling, predictive analytics, and adaptive optimization strategies. Machine learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and bio-inspired algorithms provide accurate renewable generation forecasting, optimal energy storage utilization, and real-time decision-making for grid stability. This study explores the role of AI in optimizing renewable energy integration with a focus on energy forecasting, demand-side management, hybrid energy systems, and smart grid operations. Case studies demonstrate how AI enhances energy efficiency, reduces operational costs, and improves grid reliability. Furthermore, the paper analyzes existing research gaps and highlights future opportunities for AI-enabled renewable systems through the integration of Internet of Things (IoT), blockchain, and quantum computing technologies. The findings confirm that AI-based optimization not only strengthens the resilience of modern power systems but also accelerates the global transition towards sustainable and intelligent energy infrastructure.
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