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Characterizing measurements campaigns for an innovative calibration approach of the global horizontal irradiation estimated by HelioClim-3

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  • Vernay, Christophe
  • Blanc, Philippe
  • Pitaval, Sébastien

Abstract

This study explores the possibility to calibrate the estimation of the global horizontal irradiation provided by HelioClim-3, a satellite-based surface solar irradiation database (available at www.soda-is.com). The main objective of this work is to refine such an estimation whose performances differ from one site to another. A first processing of the long-term measurements provided by nine weather stations located in Provence-Alpes-Côte d'Azur Region (South France) leads to the characterization of the clearness index error variability for that Region: this parameter is made up of a bias, a drift and 3 sinusoids with periods respectively equal to the astronomical year, half a year and one third of a year. We show that the phase of the dominant frequency (365 days) is similar whatever the tested site. We propose a simple calibration procedure based on a linear regression whose performances, in terms of mean bias error and root mean square error, depend on the beginning and the duration of the measurement campaign; to illustrate this point, the mean bias error on the global horizontal irradiation for nine sites considered systematically goes below 3% when considering a 6-month measurement campaign starting in May. We also show that the performances of the proposed calibration are also applicable to another site in the same Region for which the initial error exceeds 13%. A graphical representation allows visualizing the characterization of these measurement campaigns depending on the expected accuracy.

Suggested Citation

  • Vernay, Christophe & Blanc, Philippe & Pitaval, Sébastien, 2013. "Characterizing measurements campaigns for an innovative calibration approach of the global horizontal irradiation estimated by HelioClim-3," Renewable Energy, Elsevier, vol. 57(C), pages 339-347.
    • Handle: RePEc:eee:renene:v:57:y:2013:i:c:p:339-347
    DOI: 10.1016/j.renene.2013.01.049
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    Citations

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    Cited by:

    1. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    2. Kam, Olle Michel & Noël, Stéphane & Ramenah, Harry & Kasser, Pierre & Tanougast, Camel, 2021. "Comparative Weibull distribution methods for reliable global solar irradiance assessment in France areas," Renewable Energy, Elsevier, vol. 165(P1), pages 194-210.
    3. Psiloglou, B.E. & Kambezidis, H.D. & Kaskaoutis, D.G. & Karagiannis, D. & Polo, J.M., 2020. "Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece," Renewable Energy, Elsevier, vol. 146(C), pages 1372-1391.
    4. 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.
    5. Polo, J. & Téllez, F.M. & Tapia, C., 2016. "Comparative analysis of long-term solar resource and CSP production for bankability," Renewable Energy, Elsevier, vol. 90(C), pages 38-45.
    6. Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.
    7. Cervone, A. & Carbone, G. & Santini, E. & Teodori, S., 2016. "Optimization of the battery size for PV systems under regulatory rules using a Markov-Chains approach," Renewable Energy, Elsevier, vol. 85(C), pages 657-665.
    8. 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.

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