| Summary: Today, many environmental problems arise due to fossil fuel consumption, which is used to meet the increasing energy need. In order to prevent this situation, there is a worldwide trend toward renewable energy sources. Photovoltaic solar panels are the most common among the systems that generate electricity from renewable energy. In this study, the effects of changing radiation value, wind speed, and ambient temperature on PV panel temperature and electricity generation are modeled using both theoretical models and artificial neural networks. In addition, M-Si, P-Si, Ge and Cd-Te materials, which are widely used as PV panel cells, were selected and their interaction with environmental conditions was investigated. The results showed that the increase in wind speed, at lower ambient temperature, and higher amount of radiation significantly increased the electricity production in all PV cells. In addition, it has been shown that silicon doped P-Si and M-Si materials are minimally affected by environmental conditions. In addition, the highest electricity generation was obtained for PV cells with M-Si material, while the lowest electricity generation was obtained for PV cells with Ge material. Finally, an artificial neural network model was created, which gives close results to the theoretical model, so that the created model can be easily used by the user and fast results can be obtained. |
