Prediction of the production data of Göle Municipality solar energy power plant based on meteorological data using artificial intelligence methods

With technological developments day by day, the continuous increase in energy demand, the depletion of fossil fuels and the increasing monitoring of growth, the need for alternative energy sources has gradually increased. Especially solar energy has shown significant development recently. For this purpose, the general characteristics of solar energy and the production parts of solar power plants can be predicted using artificial intelligence methods on a meteorological basis. In the study, solar panel production was estimated using multilayer artificial neural networks (MLNN) and adaptive neural network extraction system (ANFIS). In the predictions made with the ÇKYSA method, the best results are in countries with Levenberg-Marquardt (trainlm) students and the elderly. In the ANFIS method, it was determined that the highest performance was shown in the recording time model with the Dsigmf recording function type and the 2-2-2 capacity. It is clearly observed from the publication graphs that the predictions made with both models show a high accuracy rate with the measurement data. This can be effectively applied in estimating the production data of power plants in the energy sector