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Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China

Author

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  • Zang, Haixiang
  • Cheng, Lilin
  • Ding, Tao
  • Cheung, Kwok W.
  • Wang, Miaomiao
  • Wei, Zhinong
  • Sun, Guoqiang

Abstract

Day of the year-based (DYB) models can achieve great accuracy in daily global solar radiation estimation without specific meteorological elements. Many empirical models (EMs) and machine learning (ML) methods have been proposed for DYB models. However, the number of their comparative studies based on diverse climates is limited. In this study, a grand total of 14 DYB models are established to estimate daily global solar radiation based on measured data from 1994 to 2015 at 35 meteorological stations in six climate zones of China. Detailed tasks are as follows: (1) Seven EMs and seven ML models are trained for solar radiation estimation. (2) A new EM and two novel ML models are proposed, i.e. hybrid 3rd order polynomial and sine wave model, adaptive neuro-fuzzy inference system (ANFIS) optimized by chaotic firefly algorithm (CFA) and ANFIS optimized by whale optimization algorithm with simulated annealing and roulette wheel selection (WOASAR). (3) Four statistical indicators are utilized to compare those models, and the best model for each station is decided. (4) We discuss the model parameters and climate variances of six specific stations in different climate zones. The comparison results demonstrate superb estimation precision and climate adaptability of the newly proposed models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:984-1003
    DOI: 10.1016/j.renene.2018.12.065
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    as
    1. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    2. Bakirci, Kadir, 2009. "Models of solar radiation with hours of bright sunshine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2580-2588, December.
    3. 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.
    4. 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.
    5. Zang, Haixiang & Xu, Qingshan & Bian, Haihong, 2012. "Generation of typical solar radiation data for different climates of China," Energy, Elsevier, vol. 38(1), pages 236-248.
    6. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    7. Baser, Furkan & Demirhan, Haydar, 2017. "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation," Energy, Elsevier, vol. 123(C), pages 229-240.
    8. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    9. 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.
    10. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    11. Guo, L.J. & Zhao, L. & Jing, D.W. & Lu, Y.J. & Yang, H.H. & Bai, B.F. & Zhang, X.M. & Ma, L.J. & Wu, X.M., 2009. "Solar hydrogen production and its development in China," Energy, Elsevier, vol. 34(9), pages 1073-1090.
    12. 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.
    13. Fister, Iztok & Perc, Matjaž & Kamal, Salahuddin M. & Fister, Iztok, 2015. "A review of chaos-based firefly algorithms: Perspectives and research challenges," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 155-165.
    14. Yang, Liu & Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "A new method to develop typical weather years in different climates for building energy use studies," Energy, Elsevier, vol. 36(10), pages 6121-6129.
    15. 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.
    16. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    17. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
    18. Kocifaj, Miroslav & Kómar, Ladislav, 2016. "Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation," Applied Energy, Elsevier, vol. 166(C), pages 117-127.
    19. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    20. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    21. 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.
    22. 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.
    23. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    24. 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.
    25. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
    26. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
    27. Liu, Li-qun & Wang, Zhi-xin & Zhang, Hua-qiang & Xue, Ying-cheng, 2010. "Solar energy development in China--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 301-311, January.
    28. Urban, Frauke & Geall, Sam & Wang, Yu, 2016. "Solar PV and solar water heaters in China: Different pathways to low carbon energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 531-542.
    29. Li, Zhi-Sheng & Zhang, Guo-Qiang & Li, Dong-Mei & Zhou, Jin & Li, Li-Juan & Li, Li-Xin, 2007. "Application and development of solar energy in building industry and its prospects in China," Energy Policy, Elsevier, vol. 35(8), pages 4121-4127, August.
    30. 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.
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