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Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology

Author

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  • Torres-Ramírez, M.
  • Elizondo, D.
  • García-Domingo, B.
  • Nofuentes, G.
  • Talavera, D.L.

Abstract

This work is aimed at verifying that in sunny inland locations artificial intelligence techniques may provide an estimation of the spectral irradiance with adequate accuracy for photovoltaic applications. An ANN (artificial neural network) based method was developed, trained and tested to model the spectral distributions between wavelengths ranging from 350 to 1050 nm. Only commonly available input data such as geographical information regarding location, specific date and time together with horizontal global irradiance and ambient temperature are required. Historical information from a 24-month experimental campaign carried out in Jaén (Spain) provided the necessary data to train and test the ANN tool. A Kohonen self-organized map was used as innovative technique to classify the whole input dataset and build a small and representative training dataset. The shape of the spectral irradiance distribution, the in-plane global irradiance (GT) and irradiation (HT) and the APE (average photon energy) values obtained through the ANN method were statistically compared to the experimental ones. In terms of shape distribution fitting, the mean relative deformation error stays below 4.81%. The root mean square percentage error is around 6.89% and 0.45% when estimating GT and APE, respectively. Regarding HT, errors lie below 3.18% in all cases.

Suggested Citation

  • Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
    • Handle: RePEc:eee:energy:v:86:y:2015:i:c:p:323-334
    DOI: 10.1016/j.energy.2015.04.037
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    1. García, R. & Torres-Ramírez, M. & Muñoz-Cerón, E. & de la Casa, J. & Aguilera, J., 2017. "Spectral characterization of the solar resource of a sunny inland site for flat plate and concentrating PV systems," Renewable Energy, Elsevier, vol. 101(C), pages 1169-1179.
    2. Koussa, Mustapha & Saheb-Koussa, Djohra & Hadji, Seddik, 2017. "Experimental investigation of simple solar radiation spectral model performances under a Mediterranean Algerian's climate," Energy, Elsevier, vol. 120(C), pages 751-773.
    3. del Campo-Ávila, J. & Piliougine, M. & Morales-Bueno, R. & Mora-López, L., 2019. "A data mining system for predicting solar global spectral irradiance. Performance assessment in the spectral response ranges of thin-film photovoltaic modules," Renewable Energy, Elsevier, vol. 133(C), pages 828-839.

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