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Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty

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  • Mazorra Aguiar, L.
  • Polo, J.
  • Vindel, J.M.
  • Oliver, A.

Abstract

Electrical energy production using renewable energies is one of the most important challenges in recent years. Among renewable energies, it is worth highlighting photovoltaic and thermoelectric systems due to their adaptation to the Canary Islands. One of the most important issues to ensure the stability for solar power systems, mostly in insular grids as Canary Islands, is the precise knowledge of solar radiation. In this paper, we focus in Gridded Satellite data suitability for modelling Global Horizontal Irradiation (GHI) in islands with complicated orography, as Canary Islands. Solar radiation data retrieved from CM SAF and McClear model were analysed and compared with 22 ground measurement stations in Canary Islands. Moreover, this analysis presents the results of including a site-adaptation methodology for improving satellite suitability. We used different procedures to perform this site adaptation depending on the solar radiation conditions (clear sky or cloudy sky hours), the location of the measurement station (we establish two clusters according to the climate conditions) and the season. This study could provide information about satellite models suitability in islands and a better knowledge of solar radiation behavior. Furthermore, accurate satellite radiation data for wide spatial and temporal coverage could improve solar radiation modelling and forecasting.

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  • Mazorra Aguiar, L. & Polo, J. & Vindel, J.M. & Oliver, A., 2019. "Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty," Renewable Energy, Elsevier, vol. 135(C), pages 98-107.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:98-107
    DOI: 10.1016/j.renene.2018.11.099
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    References listed on IDEAS

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    1. Antonanzas-Torres, F. & Cañizares, F. & Perpiñán, O., 2013. "Comparative assessment of global irradiation from a satellite estimate model (CM SAF) and on-ground measurements (SIAR): A Spanish case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 248-261.
    2. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198.
    3. Polo, J. & Antonanzas-Torres, F. & Vindel, J.M. & Ramirez, L., 2014. "Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models," Renewable Energy, Elsevier, vol. 68(C), pages 785-792.
    4. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935.
    5. Polo, J. & Martín, L. & Vindel, J.M., 2015. "Correcting satellite derived DNI with systematic and seasonal deviations: Application to India," Renewable Energy, Elsevier, vol. 80(C), pages 238-243.
    6. 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.
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    Cited by:

    1. Yang, Liu & Cao, Qimeng & Yu, Ying & Liu, Yan, 2020. "Comparison of daily diffuse radiation models in regions of China without solar radiation measurement," Energy, Elsevier, vol. 191(C).
    2. 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.
    3. Papadopoulos, Agis M., 2020. "Renewable energies and storage in small insular systems: Potential, perspectives and a case study," Renewable Energy, Elsevier, vol. 149(C), pages 103-114.
    4. Barbón, A. & Fortuny Ayuso, P. & Bayón, L. & Fernández-Rubiera, J.A., 2020. "Predicting beam and diffuse horizontal irradiance using Fourier expansions," Renewable Energy, Elsevier, vol. 154(C), pages 46-57.
    5. 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.
    6. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    7. Paredes, Paula & Trigo, Isabel & de Bruin, Henk & Simões, Nuno & Pereira, Luis S., 2021. "Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products," Agricultural Water Management, Elsevier, vol. 248(C).

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