E3S Web Conf.
Volume 126, 2019International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE 2019)
|Number of page(s)||7|
|Published online||30 October 2019|
Application of the neural modelling method for the Solar radiation analysis in a number of cities in the Russian Federation for the solution of environmental problems
Volgograd State Technical University,
28 Lenin Avenue,
* Corresponding author: email@example.com
The paper gives the assessment of using the methods of data mining including the artificial neural networks (ANN) in researching solar radiation for various regions of the Russian Federation, in particular, such cities as: Astrakhan (latitude of 46.4) and Sochi (at the latitude 43.6) -located in the south, in Vladivostok (latitude 43.1), Yuzhno-Sakhalinsk (latitude of 47) - in the south-east of the country, PetropavlovskKamchatsky (latitude of 53.3) — in the east, Petrozavodsk (latitude of 61) -in the south-west, and in the Russian capital - Moscow (latitude of 55.7). A neural network model has been developed, the most significant 15 input variables have been determined, as well as hidden layers numberand the number of neurons. The most optimum functions were chosen, including the Bayesian Regularization as the training functions, the function of gradient descent with regard for moments as the Learning Function, the hyperbolic tangent activation function was taken as an activation function and the Mean Square Error was taken as an execution function. The feedforward backprop function was ap lied. The equations of regression and the correlation parameters were obtained for the calculation of solar radiation.The presented work can be useful for developers of different types of electric and solar heating systems to determine the requiered parameters for solar radiation with regard to the large bulk of meteorological and geographic data for improving the environmental situation including in civil engineering and municipal economy.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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