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

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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