Framework for Designing Highly Available Multi Zone Data Platforms on Google Cloud Kubernetes Engine for Solar Industry

The integration of Artificial Intelligence (AI) in solar energy systems over the past decade has opened new frontiers in renewable energy optimization.This review presents a comprehensive synthesis of AI methods including machine learning (ML), deep learning (DL), reinforcement learning (RL), and hybrid techniques used to enhance solar power forecasting, fault detection, energy output prediction, and intelligent control.Through comparative analysis of key studies, experimental results, and performance evaluations, it becomes evident that AI-driven models, particularly LSTM networks and convolutional neural networks (CNNs), outperform traditional methods in both accuracy and adaptability.However, critical challenges remain, such as data scarcity, interpretability, model generalization, and integration with real-time systems.This review not only outlines the evolution and current state of AI in solar energy optimization but also offers future directions to guide further interdisciplinary research.By bridging engineering, data science, and sustainability goals, AI has the potential to significantly accelerate the transition to smarter, greener solar energy infrastructures.