Article 7317

Title of the article



Nikolaev Aleksandr Anatolevich, Candidate of geographical sciences, associate professor, sub-department of meteorology, climatology and atmosphere ecology, Kazan (Volga region)  Federal University (18 Kremlyovskaya street, Kazan, Russia),
Ismagilov Nail Vagizovich, Candidate of geographical sciences, associate professor, sub-department of meteorology, climatology and atmosphere ecology, Kazan (Volga region) Federal University (18 Kremlyovskaya street, Kazan, Russia),

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Background. Solar radiation is the main factor in many physical, chemical and biological processes on the earth’s surface. However, solar flux data are not always available for a variety of reasons, e.g. a lack of meteorological stations or solar radiation observations on them. Thus, calculation of solar radiation fluxes appears to be a topical question. The main purpose of this study is to review artificial neural networks (ANN), in order to identify suitable models for calculation of solar radiation and to reveal research gaps.
Materials and methods. The data from the Meteorological Observatory of Kazan Federal University were taken as the initial research material.
Results. Studying of literary resources indicates that artificial neural network methods of solar radiation prediction are more accurate compared to conventional methods. Ten models with different input parameters have been developed and the best of them have been revealed.
Conclusions. The results indicate that the use of neural network models for calculation of solar radiation fluxes is a promising area of research. These models allow us to calculate solar radiation characteristics with the use of meteorological parameters. 

Key words

neural networks, total radiation, prediction

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Дата создания: 11.01.2018 15:45
Дата обновления: 12.01.2018 10:09