Reimagining music pedagogy through game design and interactive platforms

The integration of Artificial Intelligence (AI) into solar energy systems has revolutionized the way we predict, optimize, and manage photovoltaic (PV) infrastructure. This review comprehensively explores the advancements in AI techniques including machine learning, deep learning, hybrid models, and metaheuristics used for solar irradiance forecasting, fault detection, output prediction, and system optimization over the past decade. Experimental comparisons reveal that deep learning models like LSTM and CNN consistently outperform traditional algorithms, while hybrid approaches such as CNN-LSTM yield the most accurate results across volatile environments. The review also proposes a modular theoretical framework to unify AI integration in solar systems and outlines the challenges of interpretability, data availability, and real-time deployment. The study concludes with a forward-looking perspective, emphasizing the potential of edge computing, federated learning, and interpretable AI to address existing limitations and support a more sustainable and intelligent energy future.