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Modeling solar still production using local weather data and artificial neural networks

Author

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  • Santos, Noe I.
  • Said, Aly M.
  • James, David E.
  • Venkatesh, Nanda H.

Abstract

A study has been performed to predict solar still distillate production from single examples of two different commercial solar stills that were operated for a year and a half. The purpose of this study was to determine the effectiveness of modeling solar still distillate production using artificial neural networks (ANNs) and local weather data. The study used the principal weather variables affecting solar still performance, which are the daily total insolation, daily average wind velocity, daily average cloud cover, daily average wind direction and daily average ambient temperature. The objectives of the study were to assess the sensitivity of the ANN predictions to different combinations of input parameters as well as to determine the minimum amount of inputs necessary to accurately model solar still performance. It was found that 31–78% of ANN model predictions were within 10% of the actual yield depending on the input variables that were selected. By using the coefficient of determination, it was found that 93–97% of the variance was accounted for by the ANN model. About one half to two thirds of the available long term input data were needed to have at least 60% of the model predictions fall within 10% of the actual yield. Satisfactory results for two different solar stills suggest that, with sufficient input data, the ANN method could be extended to predict the performance of other solar still designs in different climate regimes.

Suggested Citation

  • Santos, Noe I. & Said, Aly M. & James, David E. & Venkatesh, Nanda H., 2012. "Modeling solar still production using local weather data and artificial neural networks," Renewable Energy, Elsevier, vol. 40(1), pages 71-79.
    • Handle: RePEc:eee:renene:v:40:y:2012:i:1:p:71-79
    DOI: 10.1016/j.renene.2011.09.018
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    References listed on IDEAS

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    1. Toure, Siaka & Meukam, Pierre, 1997. "A numerical model and experimental investigation for a solar still in climatic conditions in Abidjan (Côte d'Ivoire)," Renewable Energy, Elsevier, vol. 11(3), pages 319-330.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
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    Cited by:

    1. Feng-Ming Tsai & Linda J.W. Huang, 2017. "Using artificial neural networks to predict container flows between the major ports of Asia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5001-5010, September.
    2. Rao K, D.V. Siva Krishna & Premalatha, M. & Naveen, C., 2018. "Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 248-258.
    3. Mousa, Hasan & Gujarathi, Ashish M., 2016. "Modeling and analysis the productivity of solar desalination units with phase change materials," Renewable Energy, Elsevier, vol. 95(C), pages 225-232.

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