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Solar radiation estimation using artificial neural networks

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  • Dorvlo, Atsu S. S.
  • Jervase, Joseph A.
  • Al-Lawati, Ali

Abstract

Artificial Neural Network Methods are discussed for estimating solar radiation by first estimating the clearness index. Radial Basis Functions, RBF, and Multilayer Perceptron, MLP, models have been investigated using long-term data from eight stations in Oman. It is shown that both the RBF and MLP models performed well based on the root-mean-square error between the observed and estimated solar radiations. However, the RBF models are preferred since they require less computing power. The RBF model, obtained by training with data from the meteorological stations at Masirah, Salalah, Seeb, Sur, Fahud and Sohar, and testing with those from Buraimi and Marmul, was the best. This model can be used to estimate the solar radiation at any location in Oman.

Suggested Citation

  • Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
    • Handle: RePEc:eee:appene:v:71:y:2002:i:4:p:307-319
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    References listed on IDEAS

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    1. Dorvlo, Atsu S.S. & Ampratwum, David B., 2000. "Harmonic analysis of global irradiation," Renewable Energy, Elsevier, vol. 20(4), pages 435-443.
    2. Coppolino, S., 1994. "A new correlation between clearness index and relative sunshine," Renewable Energy, Elsevier, vol. 4(4), pages 417-423.
    3. Dorvlo, Atsu S.S. & Ampratwum, David B., 1998. "Summary climatic data for solar technology development in Oman," Renewable Energy, Elsevier, vol. 14(1), pages 255-262.
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