IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v154y2020icp949-954.html
   My bibliography  Save this article

The extent to which a correlation between global irradiation and temperature developed for a single site can be applied to nearby sites: A case study for Israel

Author

Listed:
  • Evseev, Efim G.
  • Kudish, Avraham I.

Abstract

Solar irradiation is the critical parameter required for the design of a solar conversion system but is not always available for the site under consideration. Empirical equations based upon data available at a second nearby site have, at times, been applied to fill this void. The aim of this study is to evaluate the accuracy of an empirical correlation to estimate solar irradiation, based upon a single commonly measured parameter, average daily dry bulb temperature, and the calculated extraterrestrial irradiation, developed for a single site and applied to nearby sites. The per cent mean bias error (%MBE) for the five sites studied is in the range of −6.59 – 1.68%, whereas the per cent root mean square error (%RMSE) is in the range of 13.58–16.95%. The prediction accuracy of the empirical correlation as given by the mean absolute percentage error (MAPE) is in the range of 14.53–21.09% and the correlation coefficient is in the range of 0.917–0.925. The prediction accuracy of this relatively simple, single parameter correlation developed for a single site and then applied to five nearby sites compares favorably with that for more complicated multi-parameter generalized correlations.

Suggested Citation

  • Evseev, Efim G. & Kudish, Avraham I., 2020. "The extent to which a correlation between global irradiation and temperature developed for a single site can be applied to nearby sites: A case study for Israel," Renewable Energy, Elsevier, vol. 154(C), pages 949-954.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:949-954
    DOI: 10.1016/j.renene.2020.03.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120303773
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.03.052?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. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    7. Dos Santos, Cícero Manoel & De Souza, José Leonaldo & Ferreira Junior, Ricardo Araujo & Tiba, Chigueru & de Melo, Rinaldo Oliveira & Lyra, Gustavo Bastos & Teodoro, Iêdo & Lyra, Guilherme Bastos & Lem, 2014. "On modeling global solar irradiation using air temperature for Alagoas State, Northeastern Brazil," Energy, Elsevier, vol. 71(C), pages 388-398.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    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. Zhigao Zhou & Aiwen Lin & Lijie He & Lunche Wang, 2022. "Evaluation of Various Tree-Based Ensemble Models for Estimating Solar Energy Resource Potential in Different Climatic Zones of China," Energies, MDPI, vol. 15(9), pages 1-23, May.
    4. 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).
    5. 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.
    6. 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).
    7. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    15. 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.
    16. 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.
    17. Ağbulut, Ümit & Gürel, Ali Etem & Biçen, Yunus, 2021. "Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    19. 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.
    20. 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.

    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:154:y:2020:i:c:p:949-954. 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.