Urban Photovoltaic Efficiency Prediction Using Artificial Intelligence: Solar Irradiance Analysis of Tehran

The efficiency of photovoltaic systems is critical, especially in urban environments where weather and climate conditions vary significantly. Therefore, accurate estimation of solar cell efficiency at urban scale is essential for optimal system design, deployment, and planning. In this study, an artificial intelligence (AI)-based framework is proposed to assess the impact of urban solar radiation on photovoltaic efficiency, with a specific case study conducted for Tehran city. The method integrates high-resolution solar radiation and meteorological data with advanced machine learning models to predict key photovoltaic performance parameters, including open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), and power conversion efficiency (n). The results show that the AI models effectively capture the nonlinear relationship between radiation variations and photovoltaic performance and significantly improve the prediction accuracy compared to conventional empirical approaches. Furthermore, the proposed framework enables reliable assessment of city-specific efficiency under realistic environmental conditions, facilitating informed decision-making for photovoltaic system planning and optimization. This study highlights the potential of AI as a powerful and scalable tool for enhancing solar energy performance assessment in complex urban environments.

کلیدواژه ها:

Solar irradiance ، AI ، Efficiency ، Solar cell ، Tehran