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

Abdennasser Dahmani, Yamina Ammi, Salah Hanini, Zied Driss

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).


Keywords


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

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References


R. T. Nand and A. Raturi, "Feasibility study of a grid connected photovoltaic system for the central region of Fiji," Appl. Sol. Energy, vol. 49, no. 2, 2013, pp. 110.

C. A. Agostini, S. Nasirov, and C. Silva, "Solar PV planning toward sustainable development in Chile: challenges and recommendations," J. Environ. Dev., vol. 25, no. 1, 2016, pp. 25–46.

M. Ouria and H. Sevinc, "Evaluation of the potential of solar energy utilization in Famagusta, Cyprus," Sustain. cities Soc., vol. 37, 2018, pp. 189–202.

A. B. Stambouli, Z. Khiat, S. Flazi, and Y. Kitamura, "A review on the renewable energy development in Algeria: Current perspective, energy scenario and sustainability issues," Renewable and Sustainable Energy Reviews, vol. 16, no. 7, 2012, pp. 4445–4460. doi: 10.1016/j.rser.2012.04.031.

A. Dahmani, Y. Ammi, and S. Hanini, "Neural network for prediction solar radiation in Relizane region (Algeria)-Analysis study."

S. Rehman and M. Mohandes, "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, vol. 36, no. 2, 2008, pp. 571–576.

M. Benghanem and A. Mellit, "Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia," Energy, vol. 35, no. 9, 2010, pp. 3751–3762. doi: 10.1016/j.energy.2010.05.024.

H.-Y. Cheng, C.-C. Yu, K.-C. Hsu, C.-C. Chan, M.-H. Tseng, and C.-L. Lin, "Estimating solar irradiance on tilted surface with arbitrary orientations and tilt angles," Energies, vol. 12, no. 8, 2019, pp. 1427.

A. Koca, H. F. Oztop, Y. Varol, and G. O. Koca, "Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey," Expert Syst. Appl., vol. 38, no. 7, 2011, pp. 8756–8762.

K. Dahmani, R. Dizene, G. Notton, C. Paoli, C. Voyant, and M. L. Nivet, "Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model," Energy, vol. 70, 2014, pp. 374–381, doi: 10.1016/j.energy.2014.04.011.

M. Laidi, S. Hanini, A. Rezrazi, M. R. Yaiche, A. A. El Hadj, and F. Chellali, "Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)," Theor. Appl. Climatol., vol. 128, no. 1–2, 2016, pp. 439–451, doi: 10.1007/s00704-015-1720-7.

Z. Bounoua, L. O. Chahidi, and A. Mechaqrane, "Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations," Sustain. Mater. Technol., vol. 28, 2021, p. e00261.

E. F. Alsina, M. Bortolini, M. Gamberi, and A. Regattieri, "Artificial neural network optimization for monthly average daily global solar radiation prediction," Energy Convers. Manag., vol. 120, 2016, pp. 320–329.

O. M. Oyewola, T. E. Patchali, O. O. Ajide, S. Singh, and O. J. Matthew, "Global solar radiation predictions in Fiji Islands based on empirical models," Alexandria Eng. J., vol. 61, no. 11, Nov. 2022, pp. 8555–8571, doi: 10.1016/j.aej.2022.01.065.

L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 10, 1990, pp. 993–1001.

J. Zhou, Y. Wu, G. Yan, and Z. Ma, "Solar radiation estimation using artificial neural networks," Taiyangneng Xuebao/Acta Energiae Solaris Sin., vol. 26, no. 4, 2005, pp. 509–512.

D. V Sridhar, R. C. Seagrave, and E. B. Bartlett, "Process modeling using stacked neural networks," AIChE J., vol. 42, no. 9, 1996, pp. 2529–2539.

J. Zhang, E. B. Martin, A. J. Morris, and C. Kiparissides, "Inferential estimation of polymer quality using stacked neural networks," Comput. Chem. Eng., vol. 21, 1997, pp. S1025–S1030.

D. H. Wolpert, "Stacked generalization," Neural networks, vol. 5, no. 2, 1992, pp. 241–259.

N. Bailek et al., "A new empirical model for forecasting the diffuse solar radiation over Sahara in the Algerian Big South," Renew. Energy, vol. 117, 2018, pp. 530–537, doi: 10.1016/j.renene.2017.10.081.

L. Achour, M. Bouharkat, O. Assas, and O. Behar, "Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria)," Energy, vol. 135, 2017, pp. 526–539, doi: 10.1016/j.energy.2017.06.155.

Y. Ammi, L. Khaouane, and S. Hanini, "Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds," Neural Comput. Appl., vol. 33, no. 19, 2021, pp. 12429–12444, doi: 10.1007/s00521-021-05876-0.

M. E. Emiroglu, O. Bilhan, and O. Kisi, "Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel," Expert Syst. Appl., vol. 38, no. 1, 2011, pp. 867–874.

R. J. Tibshirani and B. Efron, "An introduction to the bootstrap," Monogr. Stat. Appl. Probab., vol. 57, 1993, pp. 1–436.

F. N. Osuolale and J. Zhang, "Exergetic optimisation of atmospheric and vacuum distillation system based on bootstrap aggregated neural network models," Exergy A Better Environ. Improv. Sustain. 1 Fundam., 2018, pp. 1033–1046.

Y. Ammi, L. Khaouane, and S. Hanini, "A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes," Arab. J. Sci. Eng., vol. 43, no. 11, 2018, pp. 6271–6284, doi: 10.1007/s13369-018-3484-8.




DOI: http://dx.doi.org/10.47238/ijeca.v8i1.214

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