Estimation of Solar Radiation using Optimized Artificial Neural Network-Genetic Algorithm and Meteorological Parameters

Solar radiation is one of the key factors in the fields of agriculture, hydrology and meteorology and plays an essential role in various physical, biological and chemical processes such as snowmelt, evaporation, photosynthesis and crop production. Thus, accurate estimation of this parameter is very important. Accordingly, in this study, the amounts of daily solar radiation were estimated using artificial neural network and artificial neural network-genetic algorithm in six stations of Ardabil province including Ardabil, Bilehsavar, Sareyn, Germi, Meshgin Shahr and Nir. The data used in this research include maximum, minimum and average temperature, relative humidity and wind speed of the mentioned stations in a time period of two years (2017-2018) which are used in eight different combinations as input data of the models. Also, statistical indices of correlation coefficient, root mean square error, Wilmot index, Kling-Gupta efficiency and Taylor diagrams have been used to compare the obtained results. Generally, the obtained results indicated that among the artificial neural networks, the model of Bilehsavar station and among the artificial neural network-genetic algorithms, the model of Ardabil station recorded the most accurate results. Also, MLP-VIII model in Bilehsavar station with a correlation coefficient of 0.856, root mean square error of 0.319 (MJ/m2d), Kling-Gupta efficiency of 0.659 and Wilmot index of 0.893 have the best performance in the utilized models. Therefore, it is recommended to use artificial neural network-genetic algorithm method for estimation of solar radiation.