Abstract
It is well known that the kingdom of Saudi Arabia is a vast natural potential for developing solar energy, there so solar power generation is growing rapidly. Solar energy depends on different weather and meteorological factors. Moreover, solar radiation variations throughout the year are considered an obstacle for predicting the solar energy. There so it is needed to develop predictive model that gives a precise estimation of the solar energy. The paper proposes effective model to give a reliable prediction of the global horizontal irradiance for the next day in Hail city. Estimating global horizontal irradiance has its influence on different outcomes for solar energy generation companies. It helps in reducing electricity production costs. The developed model applies subtractive clustering algorithm for generating initial model. Afterwards, it applies adaptive neuro-fuzzy inference system for tuning the parameters of the membership functions of the generated model. The simulation results reveal that subtractive clustering algorithm made initially an acceptable solution for adaptive neuro-fuzzy inference system to initiate the search with, instead of starting the search from the beginning. The performance of the final model developed by subtractive clustering algorithm and tuned by adaptive neuro-fuzzy inference system is much better than the initial model.