Analysis of Electricity Production in Solar Power Plants in the Aegean Region and Modeling Using Artificial Intelligence Methods

Today, global energy demand is constantly increasing due to population growth and technological developments. This growing demand has encouraged research into alternative and renewable energy sources. Among these sources, solar energy has gained significant importance as an environmentally friendly option. Thanks to advances in solar energy technologies, solar radiation can be captured by photovoltaic (PV) panels and converted into electrical energy, thereby meeting a significant portion of energy needs in a sustainable manner. In this study, the amount of electricity generated by solar power plants in different provinces of the Aegean Region under varying climatic conditions was determined by matching meteorological data. To evaluate the accuracy of the obtained data and develop a prediction model, an artificial intelligence algorithm was trained to predict the power output of PV panels based on parameters such as temperature, humidity, wind speed, and solar radiation. Among the evaluated provinces, it was observed that the model predicted daily energy production with an accuracy rate of approximately 96% in the province of Denizli. The study examines the effect of climatic variability on the efficient use of solar energy in the Aegean Region, identifies factors affecting measurement efficiency in field studies, and presents energy modeling results obtained using artificial intelligence techniques. The integration and interpretation of the findings are expected to make significant contributions to the literature on the performance and capacity analysis of solar power plants in Turkey.