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

S. Shanmuga Priya, Lisa Maria Ubbenjans, I. Thirunavukkarasu

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)88-93
Number of pages6
JournalInternational Journal of Engineering and Technology(UAE)
Volume7
Issue number2
DOIs
Publication statusPublished - 01-01-2018

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Solar Energy
Fuzzy inference
Solar radiation
Energy conversion
Solar energy
Radiation
Neural networks
Weather
Germany
India
Software

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science (miscellaneous)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Engineering(all)
  • Hardware and Architecture

Cite this

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