Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia
In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia. The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems. The novelty of the proposed approach is that it employs only the historical values of the GHI itself as explanatory variables and a fast training algorithm (resilient-propagation). In terms of performance metrics, the rp-trained FFANNs provided better results than Quasi-Newton (bfg) algorithm trained FFANNs for almost all the studied combinations of the FFANN structure. It has been also shown that increasing the number of neurons per layer didn’t improve the performance. Medium structures with fast training algorithms are recommended.
Keywords:global horizontal irradiation (GHI), forecasting, feed-forward artificial neural network (FFANN), resilient-propagation (rp) training algorithm
Y. Zhang, N. Cui, Y. Feng, D. Gong, X. Hu, “Comparison of BP, PSO-BP and statistical models for predicting daily global solar radiation in arid Northwest China”, Computers and Electronics in Agriculture, Vol. 164, Article ID 104905, 2019 DOI: https://doi.org/10.1016/j.compag.2019.104905
S. P. Durrani, S. Balluff, L. Wurzer, S. Krautter, “Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks”, Journal of Modern Power Systems and Clean Energy, Vol. 6, No. 2, pp. 255–267, 2018 DOI: https://doi.org/10.1007/s40565-018-0393-5
L. S. Saoud, F. Rahmoune, V. Tourtchine, K. Baddari, “A novel method to forecast 24 h of global solar Irradiation”, Energy Systems, Vol. 9, pp. 171–193, 2018 DOI: https://doi.org/10.1007/s12667-016-0218-4
V. Z. Antonopoulos, D. M. Papamichail, V. G. Aschonitis, A. V. Antonopoulos, “Solar radiation estimation methods using ANN and empirical models”, Computers and Electronics in Agriculture, Vol. 160, pp. 160–167, 2019 DOI: https://doi.org/10.1016/j.compag.2019.03.022
C. G. Ozoegwu, “Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number”, Journal of Cleaner Production, Vol. 216, pp. 1-13, 2019 DOI: https://doi.org/10.1016/j.jclepro.2019.01.096
H. Citakoglu, “Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation”, Computers and Electronics in Agriculture, Vol. 118, pp. 28-37, 2015 DOI: https://doi.org/10.1016/j.compag.2015.08.020
S. Hussain, A. AlAlili, “A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks”, Applied Energy, Vol. 208, pp. 540-550, 2017 DOI: https://doi.org/10.1016/j.apenergy.2017.09.100
M. Benghanem, 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, pp. 3751-3762, 2010 DOI: https://doi.org/10.1016/j.energy.2010.05.024
A. Khosravi , R. O. Nunes, M. A. H. Assad, L. Machado, “Comparison of artificial intelligence methods in estimation of daily global solar radiation”, Journal of Cleaner Production, Vol. 194, pp. 342-358, 2018 DOI: https://doi.org/10.1016/j.jclepro.2018.05.147
M. Almaraachi, “Investigating the impact of feature selection on the prediction of solar radiation in different loactions in Saudi Arabia”, Applied Soft Computing, Vol. 66, pp. 250-263, 2018 DOI: https://doi.org/10.1016/j.asoc.2018.02.029
X. Xue, “Prediction of daily diffuse solar radiation using artificial neural networks”, International Journal of Hydrogen Energy, Vol. 42, pp. 28214-28221, 2017 DOI: https://doi.org/10.1016/j.ijhydene.2017.09.150
S. Boubaker, “Identification of nonlinear Hammerstein system using mixed integer-real coded particle swarm optimization: application to the electric daily peak-load forecasting”, Nonlinear Dynamics, Vol. 90, No. 2, pp. 797-814, 2017 DOI: https://doi.org/10.1007/s11071-017-3693-9
S. Boubaker, “Identification of monthly municipal water demand system based on Autoregressive Integrated Moving Average model tuned by Particle Swarm Optimization”, Journal of Hydroinformatics, Vol. 19, No. 2, pp. 261-281, 2017 DOI: https://doi.org/10.2166/hydro.2017.035
S. Shah, H. N. Nagraja, J. Chakravorty, “ANN and ANFIS for short term load forecasting”, Engineering, Technology & Applied Science Research, Vol. 8, No. 2, pp. 2818-2820, 2018 DOI: https://doi.org/10.48084/etasr.1968
F. Mavromatakis, Y. Franghiadakis, F. Vignola, “Modeling Photovoltaic Power”, Engineering, Technology & Applied Science Research, Vol. 6, No. 5, pp. 1115-1118, 2016 DOI: https://doi.org/10.48084/etasr.612
How to Cite
MetricsAbstract Views: 598
PDF Downloads: 398
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.