Performance evaluation of two solar photovoltaic technologies under atmospheric exposure using artificial neural network models

• Performance evaluation of two different photovoltaic technologies.
• The modules were evaluated under natural environmental conditions.
• An artificial neural network was used to evaluate the electrical performance.
• Organic module showed superior behaviour at higher temperature and lower irradiance.

Experimental results are presented from monitoring the electrical power after exposure to external weather conditions of two different solar modules technologies, one of them a mono-crystalline 55 W silicon and the other a flexible organic solar module of 12.4 W. During the observation period the temperature, relative humidity, and irradiance were monitored. With these records an artificial neural network model was trained, validated and tested, delivering the electric power based on the three monitored parameters. These models were subjected to a sensitivity analysis with respect to the input variables and from the electrical point of view, a better performance for the organic flexible module was achieved specially under conditions of higher relative humidity, higher temperatures and lower irradiances. Finally this tool helps for prediction of the performance of these photovoltaic technologies at broad different environmental conditions.