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Prediction models for renewable energy sources are frequently used to manage stand-alone micro grid systems. Such prediction models are important due to the high cost or even the unavailability of real-world data in many regions. Herein, a new technique based on the Feed-forward Back-propagation Artificial Neural Network (FBANN) model has been developed and used to predict both the hourly solar radiation and the wind speed simultaneously. The new model has been tested over the Northern and Southern regions of the Arabian Peninsula. The novelty of the model lies in the following characteristics: (i) a new integration between two different FBANN configurations has been established, (ii) only three input parameters are required for the model to run and (iii) solar radiation and wind speed are predicted simultaneously. The correlation coefficient (R) and the mean absolute percentage error (MAPE) have …