Increasing energy demand along with decreasing environmental resources necessitates looking for alternative energy sources. With this respect, solar power has gained considerable importance over recent years. This study analyzes the forecasting problem for the amount of electric power generated by solar power plants. The amount of electric power generated by solar power plants is not constant and changes depending on several variables such as the weather conditions, seasonal effects, type of solar panel, etc. On the other hand, to meet the electric power demand and minimize electric transfer cost, forecasting the electric power generated by solar power plants is critical. We test several neural network models with various weather-related input parameters. Among these parameters, we choose the most promising ones (radiation, humidity, hour, month) for further analysis to forecast the electric power generated by solar power plants located in the Konya region. Our test results over the past data show that it is possible to forecast the electric power generated by solar panels in the Konya region with less than 5% error.