Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria
- El-Sayed M. El-Kenawy1,2, Abdelhameed Ibrahim3, Nadjem Bailek4,*, Kada Bouchouicha5, Muhammed A. Hassan6, Basharat Jamil7, Nadhir Al-Ansari8
1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
3 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
4 Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, 10034, Tamanghasset, Algeria
5 URERMS, Centre de Développement des Energies Renouvelables (CDER), 01000, Adrar, Algeria
6 Mechanical Power Engineering Department, Faculty of Engineering, Cairo University, Giza, 12613, Giza, Egypt
7 Department Computer Sciences, Universidad Rey Juan Carlos, Móstoles, 28933, Madrid, Spain
8 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden
* Corresponding Author: Nadjem Bailek. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
3 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
4 Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, 10034, Tamanghasset, Algeria
5 URERMS, Centre de Développement des Energies Renouvelables (CDER), 01000, Adrar, Algeria
6 Mechanical Power Engineering Department, Faculty of Engineering, Cairo University, Giza, 12613, Giza, Egypt
7 Department Computer Sciences, Universidad Rey Juan Carlos, Móstoles, 28933, Madrid, Spain
8 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden
* Corresponding Author: Nadjem Bailek. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
Received 01 September 2021; Accepted 09 October 2021; Issue published 14 January 2022
Abstract
In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach. The accuracy of the developed models was analyzed in terms of five statistical error metrics, as well as the Wilcoxon rank-sum and ANOVA test. Among the previously selected algorithms, K Neighbors Regressor and support vector regression exhibited good performances. However, the newly proposed ensemble algorithms exhibited even better performance. The proposed model showed relative root mean square errors lower than 1.448% and correlation coefficients higher than 0.999. This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms. It is concluded that the proposed algorithms are far superior to the commonly adopted ones.
Keywords
Renewable energy resources; hybrid modeling; tilted solar irradiation; arid region
Cite This Article
El-Kenawy, E. M., Ibrahim, A., Bailek, N., Bouchouicha, K., Hassan, M. A. et al. (2022). Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria. CMC-Computers, Materials & Continua, 71(3), 5837–5854.
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