Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance

Abdennasser Dahmani, Yamina Ammi, Salah Hanini, Zied Driss


This research study examines the use of two models of artificial intelligence based on a single neural network (SNN) and bootstrap aggregated neural networks (BANN) for the prediction value of hourly global horizontal irradiance (GHI) received over one year in Tamanrasset City (Southern Algeria). The SNN and BANN were created using overall data points. To improve the accuracy and durability of neural network models generated with a limited amount of training data, stacked neural networks are developed. To create many subsets of training data, the training dataset is re-sampled using bootstrap re-sampling with replacement. A neural network model is created for each set of training datasets. A stacked neural network is created by combining multiple individual neural networks (INN). For the testing phase, higher correlation coefficients (R = 0.9580) were discovered when experimental global horizontal irradiance (GHI) was compared to predicted global horizontal irradiance (GHI). The performance of the models (INN, BANN, and SNN) demonstrates that models generated with BANN are more accurate and robust than models built with individual neural networks (INN) and (SNN).


Horizontal irradiance, Single neural networks, Bootstrap aggregated, Prediction.

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