A dynamic coupled model between the local airflow field and photovoltaic system for photovoltaic performance prediction

Solar photovoltaic (PV) panels are among the most viable options for urban carbon neutrality. Current PV conversion efficiency, often under 20%, leads to excess heat release, which affects local environmental conditions. Microclimatic variables, such as air temperature and wind speed, affect PV heat transfer and conversion efficiency. These effects can further exacerbate urban temperatures and reduce PV conversion efficiency. While existing research predominantly concentrates on the impact of environmental variables on PV performance, the intricate interplay of local airflow field and PV system needs further investigation. This research aims to develop a novel coupled model integrating the internal heat transfer and electricity generation processes of PV panels with the local airflow field, providing a more accurate prediction of PV system performance under varying environmental conditions. A scaled physical model was constructed to validate parameters encompassing PV power output, front and back PV surface temperatures, local airflow temperatures, and wind speeds under the PV panels. An error analysis was conducted based on parameter characteristics and detailed conclusions were drawn. The root mean square error for PV surface temperature and local air temperatures were below 1.65℃ and 3.51℃, respectively. The mean relative error for PV power output was below 8.58%. The mean bias error for wind speeds under PV panels was −3.20% to 9.97%. Results demonstrate the proposed model outperforms traditional approaches, offering more reliable predictions for PV system performance in urban environments. This study contributes to more efficient PV system design and optimized deployment, supporting urban sustainability and energy efficiency goals.