IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i9p2583-d547419.html
   My bibliography  Save this article

The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa

Author

Listed:
  • Brighton Mabasa

    (Research & Development Division, South African Weather Service, Pretoria 0001, South Africa
    Department of Physics, University of South Africa, UNISA Preller Street, Muckleneuk, Pretoria 0001, South Africa)

  • Meena D. Lysko

    (Department of Physics, University of South Africa, UNISA Preller Street, Muckleneuk, Pretoria 0001, South Africa
    Move Beyond Consulting (Pty) Ltd., Pretoria 0001, South Africa)

  • Henerica Tazvinga

    (Research & Development Division, South African Weather Service, Pretoria 0001, South Africa)

  • Nosipho Zwane

    (Research & Development Division, South African Weather Service, Pretoria 0001, South Africa)

  • Sabata J. Moloi

    (Department of Physics, University of South Africa, UNISA Preller Street, Muckleneuk, Pretoria 0001, South Africa)

Abstract

This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE), relative Root Mean Square Error (rRMSE) and the coefficient of determination (R 2 ) are used to categorize model performance. The results show that each of the models performed differently across the 13 stations and in different climatic regions. The Bird model was overall the best in all regions, with an rMBE of 1.87%, rRMSE of 4.11% and R 2 of 0.998. The Bird model can therefore be used with quantitative confidence as a basis for solar energy applications when all the required model inputs are available.

Suggested Citation

  • Brighton Mabasa & Meena D. Lysko & Henerica Tazvinga & Nosipho Zwane & Sabata J. Moloi, 2021. "The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa," Energies, MDPI, vol. 14(9), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2583-:d:547419
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/9/2583/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/9/2583/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Badescu, Viorel & Gueymard, Christian A. & Cheval, Sorin & Oprea, Cristian & Baciu, Madalina & Dumitrescu, Alexandru & Iacobescu, Flavius & Milos, Ioan & Rada, Costel, 2013. "Accuracy analysis for fifty-four clear-sky solar radiation models using routine hourly global irradiance measurements in Romania," Renewable Energy, Elsevier, vol. 55(C), pages 85-103.
    2. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2019. "Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 550-570.
    3. Reno, Matthew J. & Hansen, Clifford W., 2016. "Identification of periods of clear sky irradiance in time series of GHI measurements," Renewable Energy, Elsevier, vol. 90(C), pages 520-531.
    4. Brighton Mabasa & Meena D. Lysko & Henerica Tazvinga & Sophie T. Mulaudzi & Nosipho Zwane & Sabata J. Moloi, 2020. "The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa," Energies, MDPI, vol. 13(20), pages 1-15, October.
    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. Nosipho Zwane & Henerica Tazvinga & Christina Botai & Miriam Murambadoro & Joel Botai & Jaco de Wit & Brighton Mabasa & Siphamandla Daniel & Tafadzwanashe Mabhaudhi, 2022. "A Bibliometric Analysis of Solar Energy Forecasting Studies in Africa," Energies, MDPI, vol. 15(15), pages 1-23, July.
    2. Rahimat O. Yakubu & Maame T. Ankoh & Lena D. Mensah & David A. Quansah & Muyiwa S. Adaramola, 2022. "Predicting the Potential Energy Yield of Bifacial Solar PV Systems in Low-Latitude Region," Energies, MDPI, vol. 15(22), pages 1-17, November.

    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. Nollas, Fernando M. & Salazar, German A. & Gueymard, Christian A., 2023. "Quality control procedure for 1-minute pyranometric measurements of global and shadowband-based diffuse solar irradiance," Renewable Energy, Elsevier, vol. 202(C), pages 40-55.
    2. Bright, Jamie M. & Sun, Xixi & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2020. "Bright-Sun: A globally applicable 1-min irradiance clear-sky detection model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    3. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Bai, Xinyu & Acord, Brendan & Wang, Peng, 2021. "Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2019. "Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 550-570.
    5. Xu, Luting & Long, Enshen & Wei, Jincheng & Cheng, Zhu & Zheng, Hanjie, 2021. "A new approach to determine the optimum tilt angle and orientation of solar collectors in mountainous areas with high altitude," Energy, Elsevier, vol. 237(C).
    6. Paulescu, Eugenia & Paulescu, Marius, 2021. "A new clear sky solar irradiance model," Renewable Energy, Elsevier, vol. 179(C), pages 2094-2103.
    7. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    8. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    9. Paulescu, Marius & Badescu, Viorel & Budea, Sanda & Dumitrescu, Alexandru, 2022. "Empirical sunshine-based models vs online estimators for solar resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    10. Chen, Shanlin & Li, Mengying, 2022. "Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications," Renewable Energy, Elsevier, vol. 189(C), pages 259-272.
    11. 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.
    12. Almorox, Javier & Voyant, Cyril & Bailek, Nadjem & Kuriqi, Alban & Arnaldo, J.A., 2021. "Total solar irradiance's effect on the performance of empirical models for estimating global solar radiation: An empirical-based review," Energy, Elsevier, vol. 236(C).
    13. Ruiz-Arias, José A., 2023. "SPARTA: Solar parameterization for the radiative transfer of the cloudless atmosphere," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    14. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    15. Ruiz-Arias, José A., 2022. "Spectral integration of clear-sky atmospheric transmittance: Review and worldwide performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Dorian Esteban Guzman Razo & Björn Müller & Henrik Madsen & Christof Wittwer, 2020. "A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning," Energies, MDPI, vol. 13(24), pages 1-20, December.
    17. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    18. Salazar, Germán & Raichijk, Carlos, 2014. "Evaluation of clear-sky conditions in high altitude sites," Renewable Energy, Elsevier, vol. 64(C), pages 197-202.
    19. Han, Jen-Yu & Vohnicky, Petr, 2022. "An optimized approach for mapping solar irradiance in a mid-low latitude region based on a site-adaptation technique using Himawari-8 satellite imageries," Renewable Energy, Elsevier, vol. 187(C), pages 603-617.
    20. Zagouras, Athanassios & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods," Renewable Energy, Elsevier, vol. 78(C), pages 203-218.

    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:gam:jeners:v:14:y:2021:i:9:p:2583-:d:547419. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.