Solar radiation data is needed by engineers, architects and scientists in the framework of studies on photovoltaic or thermal solar systems. A stochastic model for simulating global solar radiation is useful in reliable power systems calculations. The main objective of this paper is to present an algorithm to predict hourly solar radiation in the short/medium term, combining information about cloud coverage level and historical solar radiation registers, which increased the performance and the accuracy of the forecasting model. The use of Artificial Neural Networks (ANN) model is an efficient method to forecast solar radiation during cloudy days by one day ahead. The results of three statistical indicators - Mean Bias Error (MBE), Root Mean Square Error (RMSE), and t-statistic (TS) - performed with estimated and observed data, validate the good performance accuracy of the proposed three indicators.