Estimation of atmospheric turbidity coefficient β over Zhengzhou, China during 1961–2013 using an improved hybrid model
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DOI: 10.1016/j.renene.2015.09.043
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References listed on IDEAS
- Wang, Lunche & Gong, Wei & Li, Chen & Lin, Aiwen & Hu, Bo & Ma, Yingying, 2013. "Measurement and estimation of photosynthetically active radiation from 1961 to 2011 in Central China," Applied Energy, Elsevier, vol. 111(C), pages 1010-1017.
- Ellouz, F. & Masmoudi, M. & Medhioub, K., 2013. "Study of the atmospheric turbidity over Northern Tunisia," Renewable Energy, Elsevier, vol. 51(C), pages 513-517.
- Wang, Lunche & Salazar, Germán Ariel & Gong, Wei & Peng, Simao & Zou, Ling & Lin, Aiwen, 2015. "An improved method for estimating the Ångström turbidity coefficient β in Central China during 1961–2010," Energy, Elsevier, vol. 81(C), pages 67-73.
- Li, Danny H.W & Lam, Joseph C, 2002. "A study of atmospheric turbidity for Hong Kong," Renewable Energy, Elsevier, vol. 25(1), pages 1-13.
- Janjai, S. & Kumharn, W. & Laksanaboonsong, J., 2003. "Determination of Angstrom’s turbidity coefficient over Thailand," Renewable Energy, Elsevier, vol. 28(11), pages 1685-1700.
- Salazar, Germán & Utrillas, Pilar & Esteve, Anna & Martínez-Lozano, José & Aristizabal, Mariana, 2013. "Estimation of daily average values of the Ångström turbidity coefficient β using a Corrected Yang Hybrid Model," Renewable Energy, Elsevier, vol. 51(C), pages 182-188.
- Hyndman, Rob J. & Koehler, Anne B., 2006.
"Another look at measures of forecast accuracy,"
International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
- Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
- Malik, A.Q., 2000. "A modified method of estimating Ångström’s turbidity coefficient for solar radiation models," Renewable Energy, Elsevier, vol. 21(3), pages 537-552.
- 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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Cited by:
- Zou, Ling & Wang, Lunche & Xia, Li & Lin, Aiwen & Hu, Bo & Zhu, Hongji, 2017. "Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems," Renewable Energy, Elsevier, vol. 106(C), pages 343-353.
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Keywords
Global solar radiation; Yang hybrid model; Ångström turbidity coefficient; Clearness index; Zhengzhou;All these keywords.
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