The worldwide generation of solar energy has grown at an unprecedented rate over the last decade. However, several challenges are still associated with solar energy. One of which is the unreliability of this renewable energy source due to the imperfect prediction of weather conditions. As such, accurate solar irradiance predictors are necessary to improve the reliability of solar energy. Such an accurate predictor can be embedded into an energy management system for renewable power generation to perform early prediction of Solar irradiance. This helps during power generation planning and supporting cost reduction strategies in the energy industry. This thesis proposes an accurate and fast solar irradiance predictor using a data-driven deep learning model. The model is based on the Long Short-Term Memory (LSTM) architecture. The model was developed using a complex machine learning algorithm with three inputs (i.e., Wind Sustained Speed, Temperature Dry, and Solar irradiance) and one output (Solar irradiance). The model was trained and tested using actual weather measurements from the Thumrait area, Sultanate of Oman. Furthermore, an additional dataset from Marmul was used to explore if the proposed model can be used to predict irradiance at locations other than Thumrait. The simulation results show that the proposed model outperforms the ARIMA model in terms of prediction accuracy, where the former scored RMSE of 24 W/m2 compared to 279 W/m2 scored by the latter. Additionally, the proposed model runs faster compared to other contending models found in the literature. More specifically, the model can predict the solar irradiance of one year in only 23 seconds. Overall, the results show that the proposed model is more effective in dealing with time-series datasets.