AI-Driven Optimization of Solar Panel Efficiency Considering Environmental Factors

The global push for sustainable energy solutions has intensified the need to enhance the efficiency of solar panels, which are inherently affected by dynamic environmental factors such as weather conditions and sunlight angles. This study aims to investigate the application of Artificial Intelligence (AI) techniques to optimize solar panel performance under varying environmental conditions. Focusing on key variables including weather conditions, sunlight angle, and AI-based optimization strategies, the research employs a quantitative modeling approach using SmartPLS-SEM to analyze their direct and interaction effects on solar panel efficiency. Empirical data comprising environmental measurements and performance metrics were collected and modeled to assess the influence of these factors. The results indicate that both sunlight angle and weather conditions significantly impact solar panel efficiency, with sunlight angle showing a stronger direct effect. Importantly, AI-driven optimization demonstrates a significant moderating effect, enhancing solar panel adaptability and efficiency amidst environmental variability. The proposed model exhibits robust predictive capability, with an R-squared value of 0.993 for solar panel efficiency. However, the AI optimization construct requires further refinement to improve indicator reliability. These findings highlight the potential of AI integration in advancing solar energy systems, offering practical implications for improving renewable energy adoption and supporting global energy transition efforts.