Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance
No Thumbnail Available
Date
2023-05-06
Journal Title
Journal ISSN
Volume Title
Publisher
جامعة الوادي Université ofEl-Oued
Abstract
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).
Description
Article
Keywords
Horizontal irradiance, Single neural networks, Bootstrap aggregated, Prediction
Citation
Dahmani ,Abdennasser. Amm ,Yamina i. Hanin ,Salah i. Driss, Zied. Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance. The International Journal of Energetica. Vo8. No 01.04/05/2023.faculty of technology. university of el oued. [visited in ../../….]. available from [copy the link here