In this study, a new soft computing model Gaussian process regression (GPR) was evaluated for modeling the total solar radiation (TSR) and exergy (Ф) in Hakkari province (the region with the highest sunshine duration), Turkey. For this purpose, meteorological data include average, maximum and minimum temperature (Tave, Tmax, Tmin), relative humidity (H), sea level pressure (P), wind speed (W), and total sunbathing time (TST), wihch were used, and sensitivity analysis was applied for evaluating the results of TSR and Ф modeling. The results showed that all the input variables have significant impact on TSR and Ф modeling. Mean absolute percentage error and coefficient of determination (R2) for TSR and Ф predicted by GPR were 1.51–7.02% and 0.97–0.95, respectively. Application of five-fold cross validation method showed that GPR model is able to predict the TSR and Ф with a small size of data, but for more accuracy, it is suggested to use more than 70% of total data set for training the models. This research showed that GPR has a good ability for modeling the TSR and Ф with high accuracy, and so the engineers can use this method for the TSR and Ф prediction without using the solar radiation or exergy-to-energy ratio.

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