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Comparison of daily diffuse radiation models in regions of China without solar radiation measurement

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  • Yang, Liu
  • Cao, Qimeng
  • Yu, Ying
  • Liu, Yan

Abstract

Considering diffuse solar radiation is essential for thermal environment construction and solar energy applications. Due to lack of solar radiation measurement in China, developing appropriate models for data estimation is becoming more and more important. In areas that lack solar radiation measurement, using estimated global solar radiation values or extraterrestrial solar radiation is a key problem. In this study, data obtained between 2000 and 2017 from 17 stations were selected to develop and verify 18 diffuse solar radiation models and three global solar radiation models. The models were evaluated using the mean bias error, mean absolute bias error, root mean squared error, and Nash-Sutcliffe efficiency coefficient. The results showed that the diffuse fraction models based on measured and calculated global solar radiation performed better than diffuse coefficient models. Models containing both clearness index and relative sunshine duration and four parameters (clearness index, relative sunshine duration, mean air temperature and relative humidity) obtained the best performance among all of models. Choosing a precise and suitable global radiation model is essential for estimating daily diffuse radiation in areas without global radiation measurement, especially in areas with low clearness index. A simplified model based only on clearness index can be applied to calculate daily diffuse radiation in Northern China. In areas with lower clearness index and those in Southern China, global radiation models that include multiple parameters (clearness index, temperature difference, and atmospheric pressure) are the optimal options.

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  • 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).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322662
    DOI: 10.1016/j.energy.2019.116571
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    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. 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.
    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. Wang, Lunche & Lu, Yunbo & Zou, Ling & Feng, Lan & Wei, Jing & Qin, Wenmin & Niu, Zigeng, 2019. "Prediction of diffuse solar radiation based on multiple variables in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 151-216.
    5. Cao, Fei & Li, Huashan & Yang, Tian & Li, Yan & Zhu, Tianyu & Zhao, Liang, 2017. "Evaluation of diffuse solar radiation models in Northern China: New model establishment and radiation sources comparison," Renewable Energy, Elsevier, vol. 103(C), pages 708-720.
    6. Sabzpooshani, Majid & Mohammadi, Kasra, 2014. "Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran," Energy, Elsevier, vol. 69(C), pages 571-577.
    7. Karakoti, Indira & Das, Prasun Kumar & Singh, S.K., 2012. "Predicting monthly mean daily diffuse radiation for India," Applied Energy, Elsevier, vol. 91(1), pages 412-425.
    8. Achour, Lazhar & Bouharkat, Malek & Assas, Ouarda & Behar, Omar, 2017. "Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria)," Energy, Elsevier, vol. 135(C), pages 526-539.
    9. 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.
    10. Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
    11. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    12. Gopinathan, K.K. & Soler, Alfonso, 1995. "Diffuse radiation models and monthly-average, daily, diffuse data for a wide latitude range," Energy, Elsevier, vol. 20(7), pages 657-667.
    13. Pandey, Chanchal Kumar & Katiyar, A.K., 2009. "A comparative study to estimate daily diffuse solar radiation over India," Energy, Elsevier, vol. 34(11), pages 1792-1796.
    14. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    15. Li, Huashan & Bu, Xianbiao & Long, Zhen & Zhao, Liang & Ma, Weibin, 2012. "Calculating the diffuse solar radiation in regions without solar radiation measurements," Energy, Elsevier, vol. 44(1), pages 611-615.
    16. Kaplanis, S. & Kumar, Jatin & Kaplani, E., 2016. "On a universal model for the prediction of the daily global solar radiation," Renewable Energy, Elsevier, vol. 91(C), pages 178-188.
    17. Kariuki, Boniface Wainaina & Sato, Tomonori, 2018. "Interannual and spatial variability of solar radiation energy potential in Kenya using Meteosat satellite," Renewable Energy, Elsevier, vol. 116(PA), pages 88-96.
    18. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2013. "Zero energy buildings and sustainable development implications – A review," Energy, Elsevier, vol. 54(C), pages 1-10.
    19. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
    20. 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.
    21. Fan, Junliang & Chen, Baiquan & Wu, Lifeng & Zhang, Fucang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions," Energy, Elsevier, vol. 144(C), pages 903-914.
    22. 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.
    23. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    24. Younes, S. & Claywell, R. & Muneer, T., 2005. "Quality control of solar radiation data: Present status and proposed new approaches," Energy, Elsevier, vol. 30(9), pages 1533-1549.
    25. Wan, Kevin K.W. & Tang, H.L. & Yang, Liu & Lam, Joseph C., 2008. "An analysis of thermal and solar zone radiation models using an Angstrom–Prescott equation and artificial neural networks," Energy, Elsevier, vol. 33(7), pages 1115-1127.
    26. Mazorra Aguiar, L. & Polo, J. & Vindel, J.M. & Oliver, A., 2019. "Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty," Renewable Energy, Elsevier, vol. 135(C), pages 98-107.
    27. Zhao, Qun & Yao, Wanxiang & Zhang, Chunxiao & Wang, Xiao & Wang, Yan, 2019. "Study on the influence of fog and haze on solar radiation based on scattering-weakening effect," Renewable Energy, Elsevier, vol. 134(C), pages 178-185.
    28. 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.
    29. Bakirci, Kadir, 2015. "Models for the estimation of diffuse solar radiation for typical cities in Turkey," Energy, Elsevier, vol. 82(C), pages 827-838.
    30. Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
    31. Muneer, T. & Younes, S. & Munawwar, S., 2007. "Discourses on solar radiation modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(4), pages 551-602, May.
    32. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    33. Jamil, Basharat & Akhtar, Naiem, 2017. "Estimation of diffuse solar radiation in humid-subtropical climatic region of India: Comparison of diffuse fraction and diffusion coefficient models," Energy, Elsevier, vol. 131(C), pages 149-164.
    34. Song, Zhe & Ren, Zhigang & Deng, Qinli & Kang, Xin & Zhou, Mi & Liu, Daoru & Chen, Xin, 2020. "General models for estimating daily and monthly mean daily diffuse solar radiation in China's subtropical monsoon climatic zone," Renewable Energy, Elsevier, vol. 145(C), pages 318-332.
    35. Peng, Jinqing & Lu, Lin & Wang, Meng, 2019. "A new model to evaluate solar spectrum impacts on the short circuit current of solar photovoltaic modules," Energy, Elsevier, vol. 169(C), pages 29-37.
    36. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 329-355.
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