The International Journal of Energetica

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Browsing The International Journal of Energetica by Author "Ammi, Yamina"

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    Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance
    (جامعة الوادي Université ofEl-Oued, 2023-05-06) Dahmani, Abdennasser; Ammi, Yamina; Hanini, Salah; Driss, Zied
    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).
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    Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study
    (جامعة الوادي - university of el oued, 2022-11-06) Dahmani, Abdennasser; Ammi, Yamina; Hanini, Salah
    Global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the western region of Algeria. The neural network-optimal model was trained and tested using 80 %, and 20 % of the whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feed forward neural network with Regularization Bayesienne (trainbr) training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and a coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions. Keywords: Prediction, Global Solar Radiation, Artificial Neural Networks