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Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate

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  • Bouchouicha, Kada
  • Hassan, Muhammed A.
  • Bailek, Nadjem
  • Aoun, Nouar

Abstract

Accurate predictions of the solar resource are essential tools for the design, performance analysis, and economic evaluation of various solar energy projects. This paper is proposed in an attempt to overcome the lack of solar radiation data and predicting models in Algeria. New and readjusted models have been developed to estimate the daily and monthly mean daily global solar irradiations over Algerian Big South, based on measured ground data of air temperature and sunshine hours, as well as the day/month number. After a careful evaluation of the models’ performances using the standard K-fold cross-validation method, it was found that only the sunshine-based models can offer excellent estimations in the daily time scale, with validation RMSEs in the range of 1.470–2.425 MJ/m2 day. Meanwhile, all types of models have superior estimations of the monthly mean global irradiation (RRMSE less than 10% in most cases). The merits of using the K-fold cross-validation method in optimizing the error estimates, stabilizing the performances, as well as selecting the appropriate models, have been demonstrated.

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  • Bouchouicha, Kada & Hassan, Muhammed A. & Bailek, Nadjem & Aoun, Nouar, 2019. "Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate," Renewable Energy, Elsevier, vol. 139(C), pages 844-858.
    • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:844-858
    DOI: 10.1016/j.renene.2019.02.071
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    1. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    2. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    3. Achour, Lazhar & Bouharkat, Malek & Assas, Ouarda & Behar, Omar, 2017. "Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria)," Energy, Elsevier, vol. 135(C), pages 526-539.
    4. Almorox, Javier & Bocco, Mónica & Willington, Enrique, 2013. "Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina," Renewable Energy, Elsevier, vol. 60(C), pages 382-387.
    5. Ertekin, Can & Yaldız, Osman, 1999. "Estimation of monthly average daily global radiation on horizontal surface for Antalya (Turkey)," Renewable Energy, Elsevier, vol. 17(1), pages 95-102.
    6. Bakirci, Kadir, 2009. "Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey," Energy, Elsevier, vol. 34(4), pages 485-501.
    7. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
    8. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    9. Marques Filho, Edson P. & Oliveira, Amauri P. & Vita, Willian A. & Mesquita, Francisco L.L. & Codato, Georgia & Escobedo, João F. & Cassol, Mariana & França, José Ricardo A., 2016. "Global, diffuse and direct solar radiation at the surface in the city of Rio de Janeiro: Observational characterization and empirical modeling," Renewable Energy, Elsevier, vol. 91(C), pages 64-74.
    10. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
    11. Gueymard, Christian A., 2014. "A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1024-1034.
    12. Samuel Chukwujindu, Nwokolo, 2017. "A comprehensive review of empirical models for estimating global solar radiation in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 955-995.
    13. Stambouli, A. Boudghene & Khiat, Z. & Flazi, S. & Kitamura, Y., 2012. "A review on the renewable energy development in Algeria: Current perspective, energy scenario and sustainability issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4445-4460.
    14. Stambouli, Amine Boudghene, 2011. "Promotion of renewable energies in Algeria: Strategies and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1169-1181, February.
    15. Zang, Haixiang & Xu, Qingshan & Bian, Haihong, 2012. "Generation of typical solar radiation data for different climates of China," Energy, Elsevier, vol. 38(1), pages 236-248.
    16. Khraief, Naceur & Shahbaz, Muhammad & Mallick, Hrushikesh & Loganathan, Nanthakumar, 2016. "Estimation of Electricity Demand Function for Algeria: Revisit of Time Series Analysis," MPRA Paper 74870, University Library of Munich, Germany, revised 01 Nov 2016.
    17. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    18. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Potential of four different machine-learning algorithms in modeling daily global solar radiation," Renewable Energy, Elsevier, vol. 111(C), pages 52-62.
    19. Mecibah, Mohamed Salah & Boukelia, Taqiy Eddine & Tahtah, Reda & Gairaa, Kacem, 2014. "Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 194-202.
    20. Bulut, Hüsamettin & Büyükalaca, Orhan, 2007. "Simple model for the generation of daily global solar-radiation data in Turkey," Applied Energy, Elsevier, vol. 84(5), pages 477-491, May.
    21. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
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    6. Makhloufi, Saida & Khennas, Smail & Bouchaib, Sami & Arab, Amar Hadj, 2022. "Multi-objective cuckoo search algorithm for optimized pathways for 75 % renewable electricity mix by 2050 in Algeria," Renewable Energy, Elsevier, vol. 185(C), pages 1410-1424.
    7. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    8. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
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