application of artificial intelligence in optimizing the performance of solar panels for sustainable energy supply and air quality improvement

The global transition toward sustainable energy sources is essential for addressing climate change, reducing dependency on fossil fuels, and improving air quality. solar photovoltaic (pv) systems, as one of the most abundant and promising renewable energy technologies, are increasingly deployed worldwide; however, their performance is significantly affected by environmental and operational factors such as air pollution, temperature variations, soiling, and gradual panel degradation. recent advances in artificial intelligence (ai), particularly through machine learning (ml) algorithms including artificial neural networks (ann), support vector machines (svm), random forests (rf), and optimization methods such as ant colony optimization (aco), have demonstrated substantial potential in enhancing pv system performance through predictive modeling, adaptive optimization, fault detection, and operational management. this review comprehensively analyzes ai applications in pv optimization, integrating comparative evaluation of different algorithms, and provides a framework for improving energy efficiency, operational reliability, and indirectly enhancing air quality by reducing fossil fuel reliance.