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Stochastic prediction of hourly global solar radiation for Patra, Greece

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  • Kaplanis, S.
  • Kaplani, E.

Abstract

This paper describes the stochastic prediction of the hourly profile of the intensity of the global solar radiation, I(h;Â nj) for any day nj at a site. The prediction model requires one, two, or three morning measurements of the global solar radiation in a day nj, makes use of a rich data bank of past years recorded data, and provides I(h;Â nj) values for the rest hours of the day. The model is validated by comparing the I(h;Â nj) profiles generated for Patra, Greece, with the solar radiation measurements recorded for Winter, Autumn and Spring days, when solar radiation fluctuations often appear to be strong, while also comparing with the predicted by the METEONORM package I(h;Â nj) profile. Conclusions are deducted for the predictive power of the model. The proposed model, which is developed in MATLAB for the purpose of this research, provides I(h;Â nj) profile predictions very close to the measured values and offers itself as a promising tool for a predictive on-line daily load management.

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  • Kaplanis, S. & Kaplani, E., 2010. "Stochastic prediction of hourly global solar radiation for Patra, Greece," Applied Energy, Elsevier, vol. 87(12), pages 3748-3758, December.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:12:p:3748-3758
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    1. Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C.G., 2008. "Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques," Renewable Energy, Elsevier, vol. 33(8), pages 1796-1803.
    2. Escobedo, João F. & Gomes, Eduardo N. & Oliveira, Amauri P. & Soares, Jacyra, 2009. "Modeling hourly and daily fractions of UV, PAR and NIR to global solar radiation under various sky conditions at Botucatu, Brazil," Applied Energy, Elsevier, vol. 86(3), pages 299-309, March.
    3. Kaplanis, S.N., 2006. "New methodologies to estimate the hourly global solar radiation; Comparisons with existing models," Renewable Energy, Elsevier, vol. 31(6), pages 781-790.
    4. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    5. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
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    Cited by:

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