Python-Based Hybrid Ai Models For Real-Time Grid Stability Analysis In Solar Energy Networks

Abstract

The growing reliance on renewable energy, particularly solar power, presents significant challenges to grid stability due to the variability and intermittency of energy generation. Hybrid Artificial Intelligence (AI) models have emerged as a transformative solution, integrating multiple AI techniques to address these challenges effectively. This study systematically reviewed 130 peer-reviewed articles, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a transparent and rigorous process. The review highlights how hybrid AI models, by combining neural networks, reinforcement learning, evolutionary algorithms, and advanced architectures like transformers, achieve superior performance in predictive modeling, fault detection, load balancing, and scalability. Predictive modeling for solar power variability saw up to a 30% improvement in mean absolute error (MAE), while fault detection systems exhibited enhanced precision and recall, diagnosing multiple simultaneous faults in real time. Hybrid AI models also excel in load balancing and demand forecasting, reducing energy wastage by 20% and enabling dynamic resource allocation. Furthermore, their scalability and adaptability make them ideal for large-scale, distributed energy networks, addressing the complexities of modern smart grids. Despite these advancements, persistent challenges such as real-time data integration, standardization, and deployment barriers remain. The study underscores the need for future research to address these limitations through innovations like federated learning and decentralized AI frameworks. This comprehensive review demonstrates the critical role of hybrid AI in advancing grid stability, optimizing renewable energy integration, and paving the way for sustainable, resilient energy infrastructures.