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Estimation of monthly global solar radiation from sunshine duration measurement in Elaziğ

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  • Toğrul, İnci Türk
  • Toğrul, Hasan
  • Evin, Dugyu

Abstract

In this study, the relation between a and b terms and n̄/N̄ was investigated to estimate the global solar radiation in Elaziğ by using different regression analyses. Equations which contain the whole year and two different periods, winter and summer, were developed. Eqs. (5)–(7) which represent second, third and fourth degree equations respectively, gave the best results in all of the equations having an error less than 1%. Contrary to expectations, the equations developed for summer and winter periods had higher errors than the others.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:19:y:2000:i:4:p:587-595
    DOI: 10.1016/S0960-1481(99)00084-1
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    2. 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.
    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. Sözen, Adnan & Arcaklioglu, Erol, 2005. "Solar potential in Turkey," Applied Energy, Elsevier, vol. 80(1), pages 35-45, January.
    5. 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.
    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. Sözen, Adnan & Arcaklıoğlu, Erol & Özalp, Mehmet & Çağlar, Naci, 2005. "Forecasting based on neural network approach of solar potential in Turkey," Renewable Energy, Elsevier, vol. 30(7), pages 1075-1090.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

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