IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v138y2019icp1232-1238.html
   My bibliography  Save this article

Improvement of the hourly global solar model and solar radiation for air-conditioning design in China

Author

Listed:
  • Chang, Kai
  • Zhang, Qingyuan

Abstract

Hourly global solar radiation is a significant parameter for building thermal simulation and building cooling design. Most of the solar radiation models focus on the monthly or daily values, but very few studies have been carried out on the hourly values. The Zhang and Huang model was developed for estimating the hourly solar radiation based on the dry-bulb temperature difference, relative humidity and cloud cover. In this study, a new model is proposed for all-sky conditions based on the hourly/daily radiation ratio retrieved from the Zhang and Huang model and the measured daily solar radiation dataset. Compared with the Zhang and Huang model, the proposed model is more accurate (the average of R2, 0.90) and the average of RMSE is improved by 29.54%. The latest hourly solar radiation dataset for 17 locations in China from 2006 to 2016 is subsequently established using the proposed model, which can be used to generate the latest Typical Meteorological Year (TMY). As one of the significant applications to buildings, two kinds of hourly solar radiation datasets (the frequency levels of 95% and 97.5%) for 17 locations are also developed, which are usable for air-conditioning design.

Suggested Citation

  • Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    • Handle: RePEc:eee:renene:v:138:y:2019:i:c:p:1232-1238
    DOI: 10.1016/j.renene.2019.02.069
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811930223X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.02.069?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. Parishwad, G.V. & Bhardwaj, R.K. & Nema, V.K., 1997. "Estimation of hourly solar radiation for India," Renewable Energy, Elsevier, vol. 12(3), pages 303-313.
    2. 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.
    3. Sungur, Cemil, 2009. "Multi-axes sun-tracking system with PLC control for photovoltaic panels in Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1119-1125.
    4. 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.
    5. Badescu, Viorel, 1999. "Correlations to estimate monthly mean daily solar global irradiation: application to Romania," Energy, Elsevier, vol. 24(10), pages 883-893.
    6. Kee Han Kim & John Kie-Whan Oh & WoonSeong Jeong, 2016. "Study on Solar Radiation Models in South Korea for Improving Office Building Energy Performance Analysis," Sustainability, MDPI, vol. 8(6), pages 1-14, June.
    7. Baig, A. & Akhter, P. & Mufti, A., 1991. "A novel approach to estimate the clear day global radiation," Renewable Energy, Elsevier, vol. 1(1), pages 119-123.
    8. 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.
    9. 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.
    10. Nijegorodov, N., 1996. "Improved ashrae model to predict hourly and daily solar radiation components in Botswana, Namibia, and Zimbabwe," Renewable Energy, Elsevier, vol. 9(1), pages 1270-1273.
    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. Guosheng Duan & Lifeng Wu & Fa Liu & Yicheng Wang & Shaofei Wu, 2022. "Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, June.
    2. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    3. Moldovan, Camelia Liliana & Păltănea, Radu & Visa, Ion, 2020. "Improvement of clear sky models for direct solar irradiance considering turbidity factor variable during the day," Renewable Energy, Elsevier, vol. 161(C), pages 559-569.
    4. Jiandong Liu & Yanbo Shen & Guangsheng Zhou & De-Li Liu & Qiang Yu & Jun Du, 2023. "Calibrating the Ångström–Prescott Model with Solar Radiation Data Collected over Long and Short Periods of Time over the Tibetan Plateau," Energies, MDPI, vol. 16(20), pages 1-16, October.
    5. Li, You & Zhou, Weisheng & Wang, Yafei & Miao, Sheng & Yao, Wanxiang & Gao, Weijun, 2025. "Interpretable deep learning framework for hourly solar radiation forecasting based on decomposing multi-scale variations," Applied Energy, Elsevier, vol. 377(PA).

    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. Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Qin, Shujing & Liu, Zhihe & Qiu, Rangjian & Luo, Yufeng & Wu, Jingwei & Zhang, Baozhong & Wu, Lifeng & Agathokleous, Evgenios, 2023. "Short–term global solar radiation forecasting based on an improved method for sunshine duration prediction and public weather forecasts," Applied Energy, Elsevier, vol. 343(C).
    10. Prieto, Jesús-Ignacio & García, David, 2022. "Global solar radiation models: A critical review from the point of view of homogeneity and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    11. 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.
    12. 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.
    13. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    14. Enrique A. Enríquez-Velásquez & Victor H. Benitez & Sergey G. Obukhov & Luis C. Félix-Herrán & Jorge de-J. Lozoya-Santos, 2020. "Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study," Energies, MDPI, vol. 13(24), pages 1-41, December.
    15. 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.
    16. Alberto Bocca & Luca Bergamasco & Matteo Fasano & Lorenzo Bottaccioli & Eliodoro Chiavazzo & Alberto Macii & Pietro Asinari, 2018. "Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa," Energies, MDPI, vol. 11(12), pages 1-17, December.
    17. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    18. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    19. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    20. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.

    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:renene:v:138:y:2019:i:c:p:1232-1238. 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.journals.elsevier.com/renewable-energy .

    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.