AI Methods Used in Solar Energy Optimization Over the Last Decade

Abstract

In light of the increased demand for renewable energy solutions, the application of Artificial Intelligence in solar energy has garnered attention and experienced development. In the last decade, AI methods like machine learning, deep learning, and reinforcement learning have improved solar power forecasting, solar predictive maintenance, and solar control systems to achieve heightened levels of efficiency and reliability. This review systematically integrates recent advances made in the context of applying AI to photovoltaic systems and pronounces major applications, challenges, and performance results. Although AI research has made helpful strides, other issues await solution, such as data heterogeneity, interpretability of AI systems, and real-time adaptability. The paper ends with a brief conclusion and suggestions regarding future work to resolve these issues and take full advantage of the possibilities endowed by AI in solar energy systems.