Artificial neural network and neuro fuzzy inference modelling of global solar radiation data using bayesian algorithm for design of solar energy conversion system

  • Authors

    • S Shanmuga Priya
    • Lisa Maria Ubbenjans
    • I Thirunavukkarasu
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.11842
  • Global Solar Radiation, Artificial Neural Networks, Fuzzy Inference Modelling, Root Mean Square Error, Regression Coefficient.

  • Abstract

    Measurement of global solar radiation is particularly required for proper design of solar energy conversion systems. This study investigates the use of software tools like neural networks and fuzzy inference systems for modelling so as to predict global solar radiation using different input parameters based on available weather data. Advantages include simplicity, speed and efficiency, to make short term predictions of global solar radiation at different locations in India, Germany and United Kingdom. It helps in estimation of effectiveness of the applied model which matches solar radiation and other meteorological parameters which are in a non-linear relationship. Bayesian Inference algorithm is used for the current study in estimation of global solar radiation.

     

     

  • References

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  • How to Cite

    Shanmuga Priya, S., Maria Ubbenjans, L., & Thirunavukkarasu, I. (2018). Artificial neural network and neuro fuzzy inference modelling of global solar radiation data using bayesian algorithm for design of solar energy conversion system. International Journal of Engineering & Technology, 7(2.21), 88-93. https://doi.org/10.14419/ijet.v7i2.21.11842

    Received date: 2018-04-21

    Accepted date: 2018-04-21

    Published date: 2018-04-20