IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v41y2012icp394-400.html
   My bibliography  Save this article

General formula for the estimation of global solar radiation on earth’s surface around the globe

Author

Listed:
  • Korachagaon, Iranna
  • Bapat, V.N.

Abstract

The data such as global solar radiation, air temperature, relative humidity, wind and moisture, was collected from 875 stations around the globe. Of which data from 210 stations fairly spread on the earth surface was used to develop the formula for estimating the monthly average daily global radiation on a horizontal surface. In this study, using air temperature, relative humidity, wind, moisture and few derived parameters as independent variables, the most accurate equations have been obtained. The results show that the general formula developed could be used for the estimation of solar radiation with the local site parameters. Thus developed models have been validated with remaining 665 data sites. Finally two candidate models have been proposed. These models are capable of covering 50% of the land area on earth surface between latitude ±30°, enabling estimation accuracy to 93% of sites, with an estimation error (RMSE) limiting to 15%. Thus it is envisaged that, the proposed equations (models) can be used to estimate the monthly average daily global solar radiation in area where the radiation data is missing or not available. This helps in assessing the solar energy potential over necessitated area.

Suggested Citation

  • Korachagaon, Iranna & Bapat, V.N., 2012. "General formula for the estimation of global solar radiation on earth’s surface around the globe," Renewable Energy, Elsevier, vol. 41(C), pages 394-400.
  • Handle: RePEc:eee:renene:v:41:y:2012:i:c:p:394-400
    DOI: 10.1016/j.renene.2011.11.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148111006045
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2011.11.002?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. Trabea, A.A. & Shaltout, M.A.Mosalam, 2000. "Correlation of global solar radiation with meteorological parameters over Egypt," Renewable Energy, Elsevier, vol. 21(2), pages 297-308.
    2. Akinoǧlu, B.G., 1991. "A review of sunshine-based models used to estimate monthly average global solar radiation," Renewable Energy, Elsevier, vol. 1(3), pages 479-497.
    3. Toğrul, İnci Türk & Toğrul, Hasan & Evin, Dugyu, 2000. "Estimation of monthly global solar radiation from sunshine duration measurement in Elaziğ," Renewable Energy, Elsevier, vol. 19(4), pages 587-595.
    4. Bahel, V. & Bakhsh, H. & Srinivasan, R., 1987. "A correlation for estimation of global solar radiation," Energy, Elsevier, vol. 12(2), pages 131-135.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rivero, M. & Orozco, S. & Sellschopp, F.S. & Loera-Palomo, R., 2017. "A new methodology to extend the validity of the Hargreaves-Samani model to estimate global solar radiation in different climates: Case study Mexico," Renewable Energy, Elsevier, vol. 114(PB), pages 1340-1352.
    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. 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.
    4. Kaplani, E. & Kaplanis, S. & Mondal, S., 2018. "A spatiotemporal universal model for the prediction of the global solar radiation based on Fourier series and the site altitude," Renewable Energy, Elsevier, vol. 126(C), pages 933-942.
    5. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    6. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    7. Bilbao, J. & Miguel, A., 2013. "Contribution to the study of UV-B solar radiation in Central Spain," Renewable Energy, Elsevier, vol. 53(C), pages 79-85.
    8. 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).
    9. 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).
    10. Güçlü, Yavuz Selim & Dabanlı, İsmail & Şişman, Eyüp & Şen, Zekai, 2015. "HARmonic–LINear (HarLin) model for solar irradiation estimation," Renewable Energy, Elsevier, vol. 81(C), pages 209-218.
    11. El Ouderni, Ahmed Ridha & Maatallah, Taher & El Alimi, Souheil & Ben Nassrallah, Sassi, 2013. "Experimental assessment of the solar energy potential in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 155-168.
    12. Ozoegwu, C.G. & Mgbemene, C.A. & Ozor, P.A., 2017. "The status of solar energy integration and policy in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 457-471.

    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. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong & Zhao, Liang, 2011. "Global solar radiation estimation with sunshine duration in Tibet, China," Renewable Energy, Elsevier, vol. 36(11), pages 3141-3145.
    2. Makade, Rahul G. & Jamil, Basharat, 2018. "Statistical analysis of sunshine based global solar radiation (GSR) models for tropical wet and dry climatic Region in Nagpur, India: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 87(C), pages 22-43.
    3. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
    4. Katiyar, A.K. & Pandey, Chanchal Kumar, 2010. "Simple correlation for estimating the global solar radiation on horizontal surfaces in India," Energy, Elsevier, vol. 35(12), pages 5043-5048.
    5. 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.
    6. 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.
    7. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    8. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    9. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong, 2010. "Estimating daily global solar radiation by day of year in China," Applied Energy, Elsevier, vol. 87(10), pages 3011-3017, October.
    10. 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.
    11. 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.
    12. 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.
    13. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    14. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    15. Yao, Wanxiang & Zhang, Chunxiao & Hao, Haodong & Wang, Xiao & Li, Xianli, 2018. "A support vector machine approach to estimate global solar radiation with the influence of fog and haze," Renewable Energy, Elsevier, vol. 128(PA), pages 155-162.
    16. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    17. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    18. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    19. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    20. Gul Kaplan, Ayse & Alper Kaplan, Yusuf, 2020. "Developing of the new models in solar radiation estimation with curve fitting based on moving least-squares approximation," Renewable Energy, Elsevier, vol. 146(C), pages 2462-2471.

    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:renene:v:41:y:2012:i:c:p:394-400. 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/renewable-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.