IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v162y2018icp126-135.html
   My bibliography  Save this article

Estimation of daily global solar radiation using deep learning model

Author

Listed:
  • Kaba, Kazım
  • Sarıgül, Mehmet
  • Avcı, Mutlu
  • Kandırmaz, H. Mustafa

Abstract

Solar radiation (SR) is an important data for various applications such as climate, energy and engineering. Because of this, determination and estimation of temporal and spatial variability of SR has critical importance in order to make plans and organizations for the present and the future. In this study, a deep learning method is employed for estimating the SR over 30 stations located in Turkey. The astronomical factor, extraterrestrial radiation and climatic variables, sunshine duration, cloud cover, minimum temperature and maximum temperature were used as input attributes and the output was obtained as SR. The datasets of 34 stations, spanning the dates from 2001 to 2007, were used for training and testing the model, respectively, and simulated values were compared with ground-truth values. The overall coefficient of determination, root mean square error and mean absolute error were calculated as 0.980, 0.78 MJm−2day−1 and 0.61 MJm−2day−1, respectively. Consequently, DL model has yielded very precise and comparable results for estimating daily global SR. These results are generally better than or they are comparable to many previous studies reported in literature, so one can conclude that the method can be a good alternative and be successfully applied to similar regions.

Suggested Citation

  • Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:126-135
    DOI: 10.1016/j.energy.2018.07.202
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218315007
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.07.202?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Almorox, J. & Hontoria, C. & Benito, M., 2011. "Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain)," Applied Energy, Elsevier, vol. 88(5), pages 1703-1709, May.
    5. Pashiardis, S. & Kalogirou, S.A. & Pelengaris, A., 2017. "Statistical analysis for the characterization of solar energy utilization and inter-comparison of solar radiation at two sites in Cyprus," Applied Energy, Elsevier, vol. 190(C), pages 1138-1158.
    6. 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.
    7. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    8. De Souza, José Leonaldo & Nicácio, Rosilene Mendonça & Moura, Marcos Antonio Lima, 2005. "Global solar radiation measurements in Maceió, Brazil," Renewable Energy, Elsevier, vol. 30(8), pages 1203-1220.
    9. Badescu, Viorel, 1999. "Correlations to estimate monthly mean daily solar global irradiation: application to Romania," Energy, Elsevier, vol. 24(10), pages 883-893.
    10. Aksoy, Bülent, 1997. "Estimated monthly average global radiation for Turkey and its comparison with observations," Renewable Energy, Elsevier, vol. 10(4), pages 625-633.
    11. Nihal Ata Tutkun & Gamze Özel, 2016. "Assessing the influence of climate change characteristics on the rainfall duration of Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2265-2277, December.
    12. 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.
    13. Ododo, J.C. & Sulaiman, A.T. & Aidan, J. & Yuguda, M.M. & Ogbu, F.A., 1995. "The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria," Renewable Energy, Elsevier, vol. 6(7), pages 751-763.
    14. 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.
    15. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
    3. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    4. Prieto, Jesús-Ignacio & García, David, 2022. "Global solar radiation models: A critical review from the point of view of homogeneity and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    5. 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).
    6. 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.
    7. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    8. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    9. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    10. Bayrakçı, Hilmi Cenk & Demircan, Cihan & Keçebaş, Ali, 2018. "The development of empirical models for estimating global solar radiation on horizontal surface: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2771-2782.
    11. Yang, Liu & Cao, Qimeng & Yu, Ying & Liu, Yan, 2020. "Comparison of daily diffuse radiation models in regions of China without solar radiation measurement," Energy, Elsevier, vol. 191(C).
    12. Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    13. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    14. Dariusz Czekalski & Paweł Obstawski & Tomasz Bakoń, 2020. "Possibilities to Estimate Daily Solar Radiation on 2-Axis Tracking Plane Using a Model Based on Temperature Amplitude," Sustainability, MDPI, vol. 12(23), pages 1-19, November.
    15. Anton Vernet & Alexandre Fabregat, 2023. "Evaluation of Empirical Daily Solar Radiation Models for the Northeast Coast of the Iberian Peninsula," Energies, MDPI, vol. 16(6), pages 1-18, March.
    16. 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.
    17. Yıldırım, H. Başak & Teke, Ahmet & Antonanzas-Torres, Fernando, 2018. "Evaluation of classical parametric models for estimating solar radiation in the Eastern Mediterranean region of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2053-2065.
    18. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    19. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    20. Stephan Schlüter & Fabian Menz & Milena Kojić & Petar Mitić & Aida Hanić, 2022. "A Novel Approach to Generate Hourly Photovoltaic Power Scenarios," Sustainability, MDPI, vol. 14(8), pages 1-16, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:162:y:2018:i:c:p:126-135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.