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Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation

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  • Marzo, A.
  • Trigo-Gonzalez, M.
  • Alonso-Montesinos, J.
  • Martínez-Durbán, M.
  • López, G.
  • Ferrada, P.
  • Fuentealba, E.
  • Cortés, M.
  • Batlles, F.J.

Abstract

The estimation of daily solar radiation is needed in many studies related to solar power plant placements. To optimize photovoltaic (PV) systems, their placement must be as efficient as possible in terms of the prevailing meteorological conditions. There are situations where radiation data are not available, as in the case of desert areas, suitable for the operation of PV systems. In this work, daily global solar radiation has been estimated in desert areas using Artificial Neural Networks (ANN), where the inputs used are daily minimum and maximum temperatures and extraterrestrial radiation. The ANN model is validated with data from deserts in Chile, Israel, Saudi Arabia, South Africa and Australia. The results show that the average Relative Root-Mean-Square Deviation (RRMSD) value is 13%, the average Relative Mean Bias Difference (RMBE) value is less than 4% and the average correlation coefficient (r) value is about 0.8.

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  • Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:303-311
    DOI: 10.1016/j.renene.2017.01.061
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    8. Năstase, Gabriel & Șerban, Alexandru & Dragomir, George & Brezeanu, Alin Ionuț & Bucur, Irina, 2018. "Photovoltaic development in Romania. Reviewing what has been done," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 523-535.
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    10. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    11. Mohammad Mehdi Lotfinejad & Reza Hafezi & Majid Khanali & Seyed Sina Hosseini & Mehdi Mehrpooya & Shahaboddin Shamshirband, 2018. "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study," Energies, MDPI, vol. 11(5), pages 1-15, May.
    12. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    13. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    14. Jan K. Kazak & Małgorzata Świąder, 2018. "SOLIS—A Novel Decision Support Tool for the Assessment of Solar Radiation in ArcGIS," Energies, MDPI, vol. 11(8), pages 1-12, August.
    15. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.
    16. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.
    17. Ağbulut, Ümit & Gürel, Ali Etem & Biçen, Yunus, 2021. "Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Evseev, Efim G. & Kudish, Avraham I., 2020. "The extent to which a correlation between global irradiation and temperature developed for a single site can be applied to nearby sites: A case study for Israel," Renewable Energy, Elsevier, vol. 154(C), pages 949-954.
    19. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).

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