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An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high costs of electricity. In this paper, the authors proposed a new hybrid approach to forecast hourly GHI for the Al-Hoceima city, Morocco. For this purpose, a deep long short-term memory network is trained on a combination of the hourly GHI ground measurements from the meteorological station of Al-Hoceima and the satellite-derived GHI from the neighbouring pixels of the point of interest. Xgboost, Random Forest, and Recursive Feature Elimination with cross-validation were used to select the most relevant features, the lagged satellite-derived GHI around the point of interest, as input to the proposed model where the best forecasting model is selected using the Grid Search algorithm. The simulation and results showed that the proposed approach gives high performance and outperformed other benchmark approaches.

A Proposed Model to Forecast Hourly Global Solar Irradiation Based on Satellite Derived Data, Deep Learning and Machine Learning Approaches