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The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

Author

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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)

  • Sophie T. Mulaudzi

    (Department of Physics, School of Mathematical and Natural Sciences, University of Venda, Thohoyandou 0950, 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

The South African Weather Service (SAWS) manages an in situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network, located in all six macroclimate zones of South Africa. A sparsely distributed radiometric network over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b , of the Ångström–Prescott (AP) model, which can be used to estimate global horizontal irradiance ( GHI ) from observed sunshine hours, were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macroclimate zones of South Africa using the observation data that span 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily GHI . The maximum annual relative Mean Bias Error ( rMBE ) was 0.371%, relative Mean Absolute Error ( rMAE ) was 0.745%, relative Root Mean Square Error ( rRMSE ) was 0.910%, and the worst-case correlation coefficient (R 2 ) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated GHI values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where daily observation sunshine duration data are available.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5418-:d:429315
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    References listed on IDEAS

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    1. Garg, H.P. & Garg, S.N., 1993. "Measurement of solar radiation—II. calibration and standardization," Renewable Energy, Elsevier, vol. 3(4), pages 335-348.
    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. Almorox, J. & Benito, M. & Hontoria, C., 2005. "Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain," Renewable Energy, Elsevier, vol. 30(6), pages 931-936.
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    1. 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.

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