Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

Abstract:

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various performance metrics. The results show that SA-ELM achieves the highest accuracy with an R² of 0.97, outperforming ELM and WELM by 4.6% and 15.4% respectively in terms of R². SA-ELM also has the lowest MPE, RMSE and RRMSE values, indicating a higher accuracy in predicting global radiation. Furthermore, comparison with previously employed prediction techniques solidifies SA-ELM’s superiority, evident in its 0.275 RMSE.The study explores different input combinations for predicting global radiation in the study region, concluding that incorporating all relevant inputs yields optimal performance, although reduced input scenarios can still provide practical accuracy when data availability is limited. These results highlight the effectiveness of the SA-ELM model in accurately predicting global radiation, which is expected to have significant implications for renewable energy applications in the region. However, further testing and evaluation of the models in different regions and under different weather conditions is recommended to improve the generalizability and robustness of the results.

• Periodical: International Journal of Engineering Research in Africa (Volume 68)
• Pages: 151-164
• DOI: https://doi.org/10.4028/p-VH0u4y
• Online since: April 2024
• Authors: Mohammed Benatallah, Nadjem Bailek*, Kada Bouchouicha, Alireza Sharifi, Yasser Abdel-Hadi, Samuel C. Nwokolo, Nadhir Al-Ansari, Ilhami Colak, Laith Abualigah, El-Sayed M. El-kenawy
• Keywords: Algeria, Feasibility Evaluation, Hybrid Extreme Machine-Based Techniques, Machine Learning Models, Renewable Energy, Sa-ELM, Solar Radiation Prediction, WELM

References

[1] B. Haddad, P. Díaz-Cuevas, P. Ferreira, A. Djebli, and J. P. Pérez, "Mapping concentrated solar power site suitability in Algeria," Renew. Energy, vol. 168, p.838–853, 2021.

DOI: 10.1016/j.renene.2020.12.081

[2] K. Bouchouicha, N. Bailek, A. Razagui, M. EL-Shimy, M. Bellaoui, et al., "Comparison of artificial intelligence and empirical models for energy production estimation of 20 MWp solar photovoltaic plant at the Saharan Medium of Algeria," Int. J. Energy Sect. Manag., 2020.

DOI: 10.1108/ijesm-12-2019-0017

[3] S. B. D. Saiah and A. B. Stambouli, "Prospective analysis for a long-term optimal energy mix planning in Algeria: Towards high electricity generation security in 2062," Renew. Sustain. Energy Rev., vol. 73, p.26–43, 2017.

DOI: 10.1016/j.rser.2017.01.023

[4] A. B. Stambouli, "Algerian renewable energy assessment: The challenge of sustainability," Energy Policy, vol. 39, no. 8, p.4507–4519, 2011.

DOI: 10.1016/j.enpol.2010.10.005

[5] B. M. K. Khaider, G. Mohammed, and B. Meriem, "Renewable Energy in Algeria Reality and Perspective," J. Inf. Syst. Technol. Manag, vol. 3, no. 10, p.1–19, 2018.

[6] P. Díaz-Cuevas, B. Haddad, and M. Fernandez-Nunez, "Energy for the future: Planning and mapping renewable energy. The case of Algeria," Sustain. Energy Technol. Assessments, vol. 47, p.101445, 2021.

DOI: 10.1016/j.seta.2021.101445

[7] K. Abdeladim, S. Bouchakour, A. H. Arab, S. O. Amrouche, and N. Yassaa, "Promotion of renewable energy in some MENA region countries," in IOP Conference Series: Earth and Environmental Science, 2018, vol. 154, no. 1, p.12003.

DOI: 10.1088/1755-1315/154/1/012003

[8] K. Bouchouicha, A. Razagui, N. I. Bachari, and N. Aoun, "Mapping and geospatial analysis of solar resource in Algeria," Int. J. Energy, Environ. Econ., vol. 23, no. 6, 2015.

[9] M. EL-Shimy, H. Balcioglu, K. Soyer, M. A. Abdelraheem, M. Said, et al., "Economics of Variable Renewable Sources for Electric Power Production." Lambert Academic Publishing / Omniscriptum Gmbh & Company Kg, Editor …, 2017.

[10] A. B. Stambouli and H. Koinuma, "The Sahara Solar Breeder (SSB) project contributes to global sustainable energy production and resource conservation: an overview," Environ. Sustain. Role Green Technol., p.107–119, 2014.

DOI: 10.1007/978-81-322-2056-5_6

[11] N. Bailek, K. Bouchouicha, Z. Al-Mostafa, M. El-Shimy, N. Aoun, et al., "A new empirical model for forecasting the diffuse solar radiation over Sahara in the Algerian Big South," Renew. Energy, vol. 117, p.530–537, 2018.

DOI: 10.1016/j.renene.2017.10.081

[12] P. Singla, M. Duhan, and S. Saroha, "A comprehensive review and analysis of solar forecasting techniques," Front. Energy, p.1–37, 2021.

DOI: 10.1007/s11708-021-0722-7

[13] P. Nikolaidis, "Solar energy harnessing technologies towards de-carbonization: A systematic review of processes and systems," Energies, vol. 16, no. 17, p.6153, 2023.

DOI: 10.3390/en16176153

[14] F. P. Marinho, P. A. C. Rocha, A. R. R. Neto, and F. D. V Bezerra, "Short-term solar irradiance forecasting using CNN-1D, LSTM, and CNN-LSTM deep neural networks: A case study with the Folsom (USA) dataset," J. Sol. Energy Eng., vol. 145, no. 4, p.41002, 2023.

DOI: 10.1115/1.4056122

[15] R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renew. Sustain. Energy Rev., vol. 124, no. February, p.109792, 2020.

DOI: 10.1016/j.rser.2020.109792

[16] K. J. Iheanetu, "Solar Photovoltaic Power Forecasting: A Review," Sustain. 2022, Vol. 14, Page 17005, vol. 14, no. 24, p.17005, Dec. 2022.

DOI: 10.3390/su142417005

[17] M. Guermoui, K. Bouchouicha, S. Benkaciali, K. Gairaa,"New soft computing model for multi-hours forecasting of global solar radiation," Eur. Phys. J. Plus, vol. 137, no. 1, p.162, 2022.

DOI: 10.1140/epjp/s13360-021-02263-5

[18] K. Bouchouicha, N. Bailek, M. Bellaoui, B. Oulimar, and D. Benatiallah, "ANN-based correction model of radiation and temperature for solar energy application in South of Algeria," in Artificial Intelligence and Renewables Towards an Energy Transition 4, 2021, p.584–591.

DOI: 10.1007/978-3-030-63846-7_55

[19] A. Razagui, K. Abdeladim, K. Bouchouicha, N. Bachari, S. Semaoui, et al., "A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures," Sol. Energy, vol. 221, p.148–161, Jun. 2021.

DOI: 10.1016/j.solener.2021.04.024

[20] L. Zou, L. Wang, A. Lin, H. Zhu, Y. Peng, et al., "Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China," J. Atmos. Solar-Terrestrial Phys., vol. 146, p.110–122, 2016.

DOI: 10.1016/j.jastp.2016.05.013

[21] A. Rahimikhoob, "Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment," Renew. Energy, vol. 35, no. 9, p.2131–2135, 2010.

DOI: 10.1016/j.renene.2010.01.029

[22] T. Krishnaiah, S. S. Rao, and K. Madhumurthy, "Solar Stirling Dish Power Generation Atlas of India," Cogener. & Distrib. Gener. J., vol. 24, no. 2, p.35–50, 2009.

DOI: 10.1080/15453660909509007

[23] Ö. A. Karaman, T. T. Ağır, and İ. Arsel, "Estimation of solar radiation using modern methods," Alexandria Eng. J., vol. 60, no. 2, p.2447–2455, 2021.

DOI: 10.1016/j.aej.2020.12.048

[24] S. Salcedo-Sanz, C. Casanova-Mateo, A. Pastor-Sánchez, and M. Sánchez-Girón, "Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization–Extreme Learning Machine approach," Sol. Energy, vol. 105, p.91–98, 2014.

DOI: 10.1016/j.solener.2014.04.009

[25] L. Olatomiwa, S. Mekhilef, S. Shamshirband, K. Mohammadi, D. Petković, et al., "A support vector machine–firefly algorithm-based model for global solar radiation prediction," Sol. Energy, vol. 115, p.632–644, 2015.

DOI: 10.1016/j.solener.2015.03.015

[26] A. Aybar-Ruiz, S. Jiménez-Fernández, L. Cornejo-Bueno, C. Casanova-Mateo, J. Sanz-Justo, et al., "A novel Grouping Genetic Algorithm–Extreme Learning Machine approach for global solar radiation prediction from numerical weather models inputs," Sol. Energy, vol. 132, p.129–142, 2016.

DOI: 10.1016/j.solener.2016.03.015

[27] Y. Feng, W. Hao, H. Li, N. Cui, D. Gong, et al., "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renew. Sustain. Energy Rev., vol. 118, p.109393, 2020.

DOI: 10.1016/j.rser.2019.109393

[28] A. Benhamrouche, D. Boucherf, R. Hamadache, L. Bendahmane, J. Martin-Vide, et al., "Spatial distribution of the daily precipitation concentration index in Algeria," Nat. Hazards Earth Syst. Sci., vol. 15, no. 3, p.617–625, 2015.

DOI: 10.5194/nhess-15-617-2015

[29] N. Bailek, K. Bouchouicha, M. El-Shimy, and A. Slimani, "Updated status of renewable and sustainable energy projects in Algeria," Econ. Var. Renew. sources Electr. power Prod., p.519–528, 2017.

[30] A. J. Annema, K. Hoen, and H. Wallinga, "Precision requirements for single-layer feedforward neural networks," in Microelectronics for Neural Networks and Fuzzy Systems, 1994., Proceedings of the Fourth International Conference on, 1994, p.145–151.

DOI: 10.1109/icmnn.1994.593243

[31] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: a new learning scheme of feedforward neural networks," in Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, 2004, vol. 2, p.985–990.

DOI: 10.1109/ijcnn.2004.1380068

[32] S. Salcedo-Sanz, S. Jiménez-Fernández, A. Aybar-Ruiz, C. Casanova-Mateo, J. Sanz-Justo, et al., "A CRO-species optimization scheme for robust global solar radiation statistical downscaling," Renew. Energy, vol. 111, p.63–76, 2017.

DOI: 10.1016/j.renene.2017.03.079

[33] W. Zong, G.-B. Huang, and Y. Chen, "Weighted extreme learning machine for imbalance learning," Neurocomputing, vol. 101, p.229–242, 2013.

DOI: 10.1016/j.neucom.2012.08.010

[34] K. Li, X. Kong, Z. Lu, L. Wenyin, and J. Yin, "Boosting weighted ELM for imbalanced learning," Neurocomputing, vol. 128, p.15–21, 2014.

DOI: 10.1016/j.neucom.2013.05.051

[35] B. Nahvi, J. Habibi, K. Mohammadi, S. Shamshirband, and O. S. Al Razgan, "Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature," Comput. Electron. Agric., vol. 124, p.150–160, 2016.

DOI: 10.1016/j.compag.2016.03.025

[36] G.-G. Wang, M. Lu, Y.-Q. Dong, and X.-J. Zhao, "Self-adaptive extreme learning machine," Neural Comput. Appl., vol. 27, no. 2, p.291–303, 2016.

DOI: 10.1007/s00521-015-1874-3

[37] L. Wang, O. Kisi, M. Zounemat-Kermani, and H. Li, "Pan evaporation modeling using six different heuristic computing methods in different climates of China," J. Hydrol., vol. 544, p.407–427, 2017.

DOI: 10.1016/j.jhydrol.2016.11.059

[38] M. A. Hassan, H. Salem, N. Bailek, and O. Kisi, "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, 2023.

DOI: 10.3390/su15021503

[39] M. H. Yehia, M. A. Hassan, N. Abed, A. Khalil, and N. Bailek, "Combined Thermal Performance Enhancement of Parabolic Trough Collectors Using Alumina Nanoparticles and Internal Fins," in International Journal of Engineering Research in Africa, 2022, vol. 62, p.107–132.

DOI: 10.4028/p-63cdb1

[40] N. Bailek and M. Saber, "Prediction Of Diseases in Smart Healthcare System Using Machine Learning," J. Artif. Intell. Metaheuristics, vol. 3, p.48–55, 2023.

[41] M. A. Hassan, A. Khalil, S. Kaseb, and M. A. Kassem, "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Appl. Energy, vol. 203, p.897–916, 2017.

DOI: 10.1016/j.apenergy.2017.06.104

[42] K. Bouchouicha, M. A. Hassan, N. Bailek, and N. Aoun, "Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate," Renew. energy, vol. 139, p.844–858, 2019.

DOI: 10.1016/j.renene.2019.02.071

[43] A. Khezazna, H. Amarchi, O. Derdous, and F. Bousakhria, "Drought monitoring in the Seybouse basin (Algeria) over the last decades," J. water L. Dev., vol. 33, no. 1, p.79, 2017.

DOI: 10.1515/jwld-2017-0022

[44] B. Jamil, K. Irshad, A. Algahtani, S. Islam, M. A. Ali, et al., "On the calibration and applicability of global solar radiation models based on temperature extremities in India," Environ. Prog. Sustain. Energy, p. e13236.

DOI: 10.1002/ep.13236

[45] K. Bouchouicha, N. Bailek, M. E.-S. Mahmoud, J. A. Alonso, A. Slimani, et al., "Estimation of Monthly Average Daily Global Solar Radiation Using Meteorological-Based Models in Adrar, Algeria," Appl. Sol. Energy, vol. 54, no. 6, p.448–455, 2018.

DOI: 10.3103/s0003701x1806004x

[46] K. Ouali and R. Alkama, "A new model of global solar radiation based on meteorological data in Bejaia City (Algeria)," Energy Procedia, vol. 50, p.670–676, 2014.

DOI: 10.1016/j.egypro.2014.06.082

[47] B. Jamil and N. Akhtar, "Estimation of diffuse solar radiation in humid-subtropical climatic region of India: Comparison of diffuse fraction and diffusion coefficient models," Energy, vol. 131, p.149–164, 2017.

DOI: 10.1016/j.energy.2017.05.018

[48] Z. Ramedani, M. Omid, A. Keyhani, S. Shamshirband, and B. Khoshnevisan, "Potential of radial basis function based support vector regression for global solar radiation prediction," Renew. Sustain. Energy Rev., vol. 39, p.1005–1011, 2014.

DOI: 10.1016/j.rser.2014.07.108

[49] Z. Ramedani, M. Omid, A. Keyhani, B. Khoshnevisan, and H. Saboohi, "A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran," Sol. Energy, vol. 109, p.135–143, 2014.

DOI: 10.1016/j.solener.2014.08.023