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Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques

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  • Salamalikis, Vasileios
  • Tzoumanikas, Panayiotis
  • Argiriou, Athanassios A.
  • Kazantzidis, Andreas

Abstract

Satellite and reanalysis-derived solar products have gained great attention due to the inadequate number of radiometric stations worldwide, however, they are associated with considerable uncertainties. This study deals with the ground-based validation of Global Horizontal Irradiance from CAMS radiation service (GHICAMS) and the application of supervised machine learning algorithms (MLAs) to site-adapt GHICAMS. The validation of GHICAMS against measurements shows significant systematic and dispersion errors for all-sky (nMBE = 4.9% and nRMSE = 15.7%) and cloudy conditions (nMBE = 17.6% and nRMSE = 38.8%). Under clear skies, CAMS performs adequately (nMBE <1% and nRMSE <5%). All MLAs lead to reduced errors for the site-adapted irradiances. MBE is improved by more than 50%, accompanied by significant reductions in RMSE for various solar zenith angles and cloud fractions. The best results are revealed for the tree-based MLAs and especially for Random Forests.

Suggested Citation

  • Salamalikis, Vasileios & Tzoumanikas, Panayiotis & Argiriou, Athanassios A. & Kazantzidis, Andreas, 2022. "Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques," Renewable Energy, Elsevier, vol. 195(C), pages 92-106.
    • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:92-106
    DOI: 10.1016/j.renene.2022.06.043
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    References listed on IDEAS

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