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121 results on '"Bouchouicha, Kada"'

11. ANFIS Technique to Estimate Daily Global Solar Radiation by Day in Southern Algeria

Author

Babahadj, Abdeldjbbar, Rahmani, Lakhdar, Bouchouicha, Kada, Brahim, Berbaoui, Necaibia, Ammar, Mebrouk, Bellaoui, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Hatti, Mustapha, editor

Published
2020
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20. Models for Obtaining the Daily Global Solar Radiations in Clear Sky in Sahara

Author

Benatiallah, Djelloul, Benatiallah, Ali, Nasri, Bahous, Bouchouicha, Kada, Abdalla, Hassan, Series editor, Abdul Mannan, Md., Series editor, Alalouch, Chaham, Series editor, Attia, Sahar, Series editor, Boemi, Sofia Natalia, Series editor, Bougdah, Hocine, Series editor, Bozonnet, Emmanuel, Series editor, De Bonis, Luciano, Series editor, Hawkes, Dean, Series editor, Kostopoulou, Stella, Series editor, Mahgoub, Yasser, Series editor, Mesbah Elkaffas, Saleh, Series editor, Mohareb, Nabil, Series editor, O. Gawad, Iman, Series editor, Oostra, Mieke, Series editor, Pignatta, Gloria, Series editor, Pisello, Anna Laura, Series editor, Rosso, Federica, Series editor, Kallel, Amjad, editor, Ksibi, Mohamed, editor, Ben Dhia, Hamed, editor, and Khélifi, Nabil, editor

Published
2018
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28. Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

Author

Benatallah, Mohammed, Bailek, Nadjem, Bouchouicha, Kada, Sharifi, Alireza, Abdel-Hadi, Yasser, Nwokolo, Samuel C., Al-Ansari, Nadhir, Colak, Ilhami, Abualigah, Laith, and M. El-kenawy, El-Sayed

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.

Published
2024
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31. Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Author

Jamei, Mehdi, Bailek, Nadjem, Bouchouicha, Kada, Hassan, Muhammed A., Elbeltagi, Ahmed, Kuriqi, Alban, Al-Ansari, Nadhir, Almorox, Javier, El-kenawy, El-Sayed M., Jamei, Mehdi, Bailek, Nadjem, Bouchouicha, Kada, Hassan, Muhammed A., Elbeltagi, Ahmed, Kuriqi, Alban, Al-Ansari, Nadhir, Almorox, Javier, and El-kenawy, El-Sayed M.

Abstract

Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968–0.988 and RMSE = 1.274–1.403 MJ/m2⋅d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR. Hence, this study recommends new effective methods to predict GSR in semi-arid regions., Validerad;2022;Nivå 2;2022-09-23 (joosat)

Published
2023
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36. MPPT Fuzzy Logic Control of a Variable Speed Wind Turbine

Author

Dahbi, Abdeldjalil, primary, Benmedjahed, Miloud, additional, Khelfaoui, Abderrahman, additional, Kadi, Sara, additional, Benlahbib, Boualam, additional, Guentri, Houcine, additional, Aoun, Nouar, additional, Harrag, Abdelghani, additional, Hamouda, Messaoud, additional, Bouraiou, Ahmed, additional, Slimani, Abdeldjalil, additional, Neçaibia, Ammar, additional, Chogueur, Djilali, additional, Tidjar, Boudjema, additional, Oudran, Abdellatif, additional, Bouchouicha, Kada, additional, Mouhadjer, Samir, additional, Touahri, Tahar, additional, and Kadri, Ahmed Yassine, additional

Published
2022
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43. Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria.

Author

El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, Bailek, Nadjem, Bouchouicha, Kada, Hassan, Muhammed A., Jamil, Basharat, and Al-Ansari, Nadhir

Subjects
RENEWABLE energy sources, STANDARD deviations, SWARM intelligence, ARID regions, STATISTICAL errors
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. [ABSTRACT FROM AUTHOR]

Published
2022
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44. Auto regression Integrated moving average (ARIMA) to estimate temporal series of global solar radiation from measured data

Author

Bellaoui Mebrouk, Bouchouicha Kada, Aoun Nouar, Oulimar Brahim, and Babahadj Abdeljabar

Subjects
Series (stratigraphy), Meteorology, 020209 energy, 02 engineering and technology, Expected value, Radiation, Latitude, Autoregressive model, Moving average, Physics::Space Physics, 0202 electrical engineering, electronic engineering, information engineering, Astrophysics::Solar and Stellar Astrophysics, Environmental science, 020201 artificial intelligence & image processing, Astrophysics::Earth and Planetary Astrophysics, Autoregressive integrated moving average, Longitude
Abstract

The Measurements of the solar radiation is important in choosing sites to install projects with solar systems. Adrar region is classified from the high potential solar areas in the world. In this study, we using the measurements of global solar radiation on horizontal surface in Adrar region and Auto regression Integrated moving average (ARIMA) model to estimate temporal series of global solar radiation. The results of estimation were validated with observed data of Adrar station (latitude=27.88 North, longitude = -0.28 EST). The correlation between predicted and expected values of global solar radiation is generally significant. But the comparisons with other model demonstrate less efficiently of the model.

Published
2020

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