Estimation of solar radiation in different climates of Iran using hybrid machine learning methods

Solar radiation, the basic parameter required for solar energy programs, is not measured in all parts of the world due to technical and financial limitations, and it is necessary to determine with the accurate estimation methods. In this research, a model was developed to estimate the daily horizontal scattered solar radiation with the hybrid methods of Support Vector Regression, Gray Wolf Optimization and Harmony Search Algorithm. To determine the validity of the model, daily measured solar radiation data of different cities in the sunny part of Iran (Bandar Abbas, Kerman, Sanandaj, Semnan and Zahedan) were used. The input parameters of the models are the mean temperature, relative humidity, sunshine hour, evaporation and wind velocity. The comparison of the solar radiation of the hybrid model with the measured values shows the desired results based on statistical analysis, and the hybrid model is an efficient and accurate method compared to other models, especially experimental models. The average absolute bias error obtained, root mean square error and correlation coefficient for Zahedan station are respectively equal to 14.77 MJ⁄m^2, 26.85 MJ⁄m^2, and 0.68 for the experimental equation and the obtained values of the regression vector machine - Harmony respectively It was similar to 13.85MJ⁄m^2, 9.58 MJ⁄m^2 and 0.71. The research results showed that the regression-harmony vector machine model is an efficient method that is much more accurate than other models.