Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach

The study investigates the ability of FG (fuzzy genetic) approach in modeling solar radiation of seven cities from Mediterranean region of Anatolia, Turkey. Latitude, longitude, altitude and month of the year data from the Adana, K. Maras, Mersin, Antalya, Isparta, Burdur and Antakya cities are used as inputs to the FG model to estimate one month ahead solar radiation. FG model is compared with ANNs (artificial neural networks) and ANFIS (adaptive neruro fuzzzy inference system) models with respect to RMSE (root mean square errors), MAE (mean absolute errors) and determination coefficient (R2) statistics. Comparison results indicate that the FG model performs better than the ANN and ANFIS models. It is found that the FG model can be successfully used for estimating solar radiation by using latitude, longitude, altitude and month of the year information. FG model with RMSE = 6.29 MJ/m2, MAE = 4.69 MJ/m2 and R2 = 0.905 in the test stage was found to be superior to the optimal ANN model with RMSE = 7.17 MJ/m2, MAE = 5.29 MJ/m2 and R2 = 0.876 and ANFIS model with RMSE = 6.75 MJ/m2, MAE = 5.10 MJ/m2 and R2 = 0.892 in estimating solar radiation. - Highlights: • SR (Solar radiation) of seven cities from Mediterranean region of Turkey is predicted. • FG (Fuzzy genetic) models are developed for accurately estimation of SR. • The ability of the FG models used in the study is found to be satisfactory. • FG models are compared with commonly used ANNs (artificial neural networks). • FG models are found to perform better than the ANNs models