AI Based Performance Boost in Solar PV Fuel Cell Hybrids

Technology with renewable energy have crucial Solar energy plays a crucial role in tackling the worldwide shift towards sustainable energy sources. Photovoltaic (PV) systems and fuel cells are two prominent sources of clean energy; however, they exhibit intermittent and variable power generation patterns, hindering their widespread adoption. This paper proposes a novel approach to improve performance of Hybrids (SPV-FCH) through the integration of Artificial Intelligence (AI) techniques. The synergy aims to create more reliable, continuous power generation system with joining nature renewable energy which includes consistent contribution of fuel cells. The integration of AI algorithms offers an intelligent control mechanism that optimizes the operation of the hybrid system, thereby overcoming fluctuations in irradiance of solar, the dynamic nature of energy demand. The AI-enabled control system employs predictive analytics and machine learning algorithms to forecast solar irradiance patterns, weather conditions, and energy consumption trends. By leveraging real-time data and historical patterns, the system can dynamically adjust both the components, optimizing their performance for maximum energy output, efficiency, and overall system reliability. Furthermore, the AI system enables proactive maintenance and fault detection, enhancing the overall resilience and longevity of the hybrid system. Through continuous learning and adaptation, the AI controller refines its predictions and control strategies, ensuring optimal performance under varying environmental conditions. This paper discusses the design and implementation of the AI-enabled control system for SPV-FCH hybrids, highlighting its effectiveness in achieving improved energy yield, grid stability, and cost-effectiveness. The proposed approach not only addresses the intermittent challenges associated with solar PV but also maximizes the utilization of both technologies, contributing advancement sustainable including resilient power solutions. The findings presented in this paper contribute valuable insights into the integration of AI in renewable energy systems, paving the way for smarter and more efficient hybrid power generation technologies.