Prediction of solar ultraviolet intensity by using Fuzzy Logic in the north-west of Iran

Document Type : Conference Proceedings

Authors

1 Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran

2 Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

3 Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

4 Department of Physics, University of Tabriz, Tabriz, Iran

5 Department of Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Abstract

Introduction:
Solar energy is one of the free sources, clean and environmentally friendly energy. Sun is the most important source of natural ultraviolet radiation that has a major role in the life of living beings. Industrial and medical applications of ultraviolet radiation have been clearly proven, like the production of vitamin D or treatment of many diseases, and also harmful effects such as diseases related to skin and eyes. Therefore, prediction of UV exposure reaching the surface of the earth is an important subject in health, ecosystem and economy related concerns, which can affect efficiency, and increase the use of renewable energy sources. In this study fuzzy logic has been used to predict the amount of UV exposure in Tabriz.
Materials and Methods:
Intensity of solar UV radiation type A, B and C have been measured for a whole year from sunrise to sunset in Tabriz during 2016-2017. These data then were given to fuzzy logic model, along with sunny hours of day and moths of the year, as input to simulate and predict the solar UV exposure. Two statistical indexes, RMSE and R2, have been used to evaluate the presented model.
Results:
Considering the results of the proposed model with the experimental data, this model can predict the solar exposure accurately. Average errors obtained for simulation was RMSE=0.001 with R2=0.99.
Conclusion:

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History

  • Receive Date: 21 May 2018
  • Revise Date: 28 April 2019
  • Accept Date: 06 July 2018

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