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AI Data Driven Approach-Based Endogenous Inputs for Global Solar Radiation Forecasting

Received: 16 November 2019 Accepted: 3 January 2020 Controlling the random nature of renewable energy sources such as solar radiation at ground, allows electric grid operators to better integrate it. In this paper, an intelligent datadriven model based on artificial neural network with autoregressive input sequence is developed to forecast the global solar radiation (GSR) time series on a half hour resolution in the site of Agdal, Marrakesh, Morocco. The database that is used to create this model was divided into two subsets. The first subset is used for training the proposed model on the data measured during the year 2008 by adopting three efficient optimizers (levenbergmarquardt, resilient backpropagation, and scaled conjugate gradient). The second subset is used for testing the efficiency and the robustness of the developed model to generate accurate predictions during the next six years (from 2009 to 2014). The obtained results demonstrate the accuracy and the stability of the proposed data-driven model to perform prediction in case of GSR measurements intermittence or sensor damage.