Artificial Neural Network and Neuro Fuzzy Inference modelling of Global Solar Radiation data using Bayesian Algorithm for design of solar energy conversion system

Shanmugapriya, S and Ubbenjans, Lisa Maria and Thirunavukkarasu, I (2017) Artificial Neural Network and Neuro Fuzzy Inference modelling of Global Solar Radiation data using Bayesian Algorithm for design of solar energy conversion system. In: CISCON 2013, 03/11/2017, MIT, Manipal.

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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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Global solar radiation; artificial neural networks; fuzzy inference modelling; root mean square error; regression coefficient
Subjects: Engineering > MIT Manipal > Chemical
Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 13 Dec 2017 09:08
Last Modified: 13 Dec 2017 09:08
URI: http://eprints.manipal.edu/id/eprint/150244

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