Non-iterative Neural-like Predictor for Solar Energy in Libya

In this paper, a new method for predicting the solar radiation potential in Libya was developed. It is constructed on the basis of the combined use of RBF and non-iterative paradigm of the artificial neural networks construction the Successive Geometric Transformations Model. This method has the advantages of both approaches the high prediction accuracy from RBF characteristics and fast non-iterative learning provided by the Successive Geometric Transformations Model. A series of practical experiments were conducted. The training model contained 1440 vectors of the monthly solar radiation, which recorded in 25 Libya's cities from 2010 to 2015. The test model contained 360 data’s vectors. Comparison of the proposed method with existing ones is presented. The proposed method showed the best prediction results (MAPE, RMSE) compared to SVM, Linear Regression, the linear Neural-like structure of the Successive Geometric Transformation Model (SGTM), and the RBF based on the NLS SGTM. The proposed approach can be used in different areas, such as e-commerce, material science, images processing and others, especially in Big Data cases.

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