IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v94y2018icp168-182.html
   My bibliography  Save this article

Evaluation of sunshine-based models for predicting diffuse solar radiation in China

Author

Listed:
  • Feng, Lan
  • Lin, Aiwen
  • Wang, Lunche
  • Qin, Wenmin
  • Gong, Wei

Abstract

Accurate observation and understanding of diffuse radiation is of vital importance for solar energy applications. Numerous empirical models have been developed for estimating solar radiation in regional and global scales, owing to the relatively sparse radiation measurements. The main objective of this study was to conduct a comprehensive evaluation of 15 typical empirical models for estimating diffuse radiation in different climate zones over mainland China. The result showed that the model in form of second order polynomial performed superior than other models, with mean MBE, MAE, MARE, RMSE, RRMSE, t-stat, STD, and R at all 17 CMA stations were − 0.125 MJ m−2day−1, 1.331 MJ m−2 day−1, 0.208 MJ m−2 day−1,1.807 MJ m−2 day−1, 24.889%, 10.866, 0.941 MJ m−2 day−1, and 0.792, respectively. By contrast, the model in form of fractional first order polynomial showed the poorest performance than other models, with mean MAE, MARE, RMSE, RRMSE, t-stat, STD, and R of − 0.699 MJ m−2day−1, 2.508 MJ m−2day−1, 0.397 MJ m−2 day−1, 6.779 MJ m−2 day−1, 102.716%, 6.709, 1.773 MJ m−2 day−1, and 0.519, respectively. All models generally showed poor accuracies in arid areas with warm-temperate climate, due to the frequent dust occurrences in the air. The estimation errors in Qinghai-Tibet Plateau were also relatively larger, owing to the strong heating atmosphere there. This study would assist in the selection of the most appropriate models for solar energy applications.

Suggested Citation

  • Feng, Lan & Lin, Aiwen & Wang, Lunche & Qin, Wenmin & Gong, Wei, 2018. "Evaluation of sunshine-based models for predicting diffuse solar radiation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 168-182.
  • Handle: RePEc:eee:rensus:v:94:y:2018:i:c:p:168-182
    DOI: 10.1016/j.rser.2018.06.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032118304477
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2018.06.009?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. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
    2. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    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. Khorasanizadeh, Hossein & Mohammadi, Kasra, 2016. "Diffuse solar radiation on a horizontal surface: Reviewing and categorizing the empirical models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 338-362.
    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. Shamshirband, Shahaboddin & Mohammadi, Kasra & Khorasanizadeh, Hossein & Yee, Por Lip & Lee, Malrey & Petković, Dalibor & Zalnezhad, Erfan, 2016. "Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 428-435.
    7. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    8. Badescu, Viorel & Gueymard, Christian A. & Cheval, Sorin & Oprea, Cristian & Baciu, Madalina & Dumitrescu, Alexandru & Iacobescu, Flavius & Milos, Ioan & Rada, Costel, 2012. "Computing global and diffuse solar hourly irradiation on clear sky. Review and testing of 54 models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1636-1656.
    9. 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.
    10. 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.
    11. Wattan, Rungrat & Janjai, Serm, 2016. "An investigation of the performance of 14 models for estimating hourly diffuse irradiation on inclined surfaces at tropical sites," Renewable Energy, Elsevier, vol. 93(C), pages 667-674.
    12. Salcedo-Sanz, Sancho & Deo, Ravinesh C. & Cornejo-Bueno, Laura & Camacho-Gómez, Carlos & Ghimire, Sujan, 2018. "An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia," Applied Energy, Elsevier, vol. 209(C), pages 79-94.
    13. Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
    14. 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.
    15. Khalil, Samy A. & Shaffie, A.M., 2013. "A comparative study of total, direct and diffuse solar irradiance by using different models on horizontal and inclined surfaces for Cairo, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 853-863.
    16. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    17. 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.
    18. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    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. Ramirez Camargo, Luis & Dorner, Wolfgang, 2016. "Comparison of satellite imagery based data, reanalysis data and statistical methods for mapping global solar radiation in the Lerma Valley (Salta, Argentina)," Renewable Energy, Elsevier, vol. 99(C), pages 57-68.
    21. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.
    22. Mohammadi, Kasra & Shamshirband, Shahaboddin & Petković, Dalibor & Khorasanizadeh, Hossein, 2016. "Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1570-1579.
    23. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
    24. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    25. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    26. 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.
    27. Paulescu, M. & Stefu, N. & Calinoiu, D. & Paulescu, E. & Pop, N. & Boata, R. & Mares, O., 2016. "Ångström–Prescott equation: Physical basis, empirical models and sensitivity analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 495-506.
    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. 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.
    2. 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.
    3. Kisi, Ozgur & Heddam, Salim & Yaseen, Zaher Mundher, 2019. "The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model," Applied Energy, Elsevier, vol. 241(C), pages 184-195.
    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. Bakirci, Kadir, 2021. "Prediction of diffuse radiation in solar energy applications: Turkey case study and compare with satellite data," Energy, Elsevier, vol. 237(C).
    6. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    7. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    8. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
    9. Liu, Peirong & Tong, Xiaojuan & Zhang, Jinsong & Meng, Ping & Li, Jun & Zhang, Jingru, 2020. "Estimation of half-hourly diffuse solar radiation over a mixed plantation in north China," Renewable Energy, Elsevier, vol. 149(C), pages 1360-1369.
    10. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.

    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. 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.
    2. 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.
    3. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    4. 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.
    5. 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.
    6. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    7. 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.
    8. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Wang, Xiukang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 732-747.
    9. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    10. 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).
    11. 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.
    12. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    13. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    14. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
    15. Wang, Hong & Sun, Fubao & Wang, Tingting & Liu, Wenbin, 2018. "Estimation of daily and monthly diffuse radiation from measurements of global solar radiation a case study across China," Renewable Energy, Elsevier, vol. 126(C), pages 226-241.
    16. Liu, Peirong & Tong, Xiaojuan & Zhang, Jinsong & Meng, Ping & Li, Jun & Zhang, Jingru, 2020. "Estimation of half-hourly diffuse solar radiation over a mixed plantation in north China," Renewable Energy, Elsevier, vol. 149(C), pages 1360-1369.
    17. Moretón, R. & Lorenzo, E. & Pinto, A. & Muñoz, J. & Narvarte, L., 2017. "From broadband horizontal to effective in-plane irradiation: A review of modelling and derived uncertainty for PV yield prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 886-903.
    18. Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
    19. Saioa Etxebarria Berrizbeitia & Eulalia Jadraque Gago & Tariq Muneer, 2020. "Empirical Models for the Estimation of Solar Sky-Diffuse Radiation. A Review and Experimental Analysis," Energies, MDPI, vol. 13(3), pages 1-23, February.
    20. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.

    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:rensus:v:94:y:2018:i:c:p:168-182. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.