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

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

Renewable Energy, 2022, vol. 195, issue C, 92-106

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.

Keywords: Global horizontal irradiance; CAMS-Rad; Clear sky detection; Site-adaptation; Supervised machine learning algorithms; Goodness-of-fit statistics (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:195:y:2022:i:c:p:92-106

DOI: 10.1016/j.renene.2022.06.043

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