Abstract
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.
Highlights
In accordance with “Russian Long-Term S & T Foresight 2030” among of Priority thematic S & T areas were included the following ones: Data processing and analysis technologies; Predictive modeling and simulation; Preservation of environment and environmental safety; Efficient utilization of renewable energy sources.Problems of ecology and environmental safety have recently acquired a global character
The elimination of some of them is possible by using solar radiation
Hydro-power plants, power stations working on the natural fuel, and atomic
Summary
In accordance with “Russian Long-Term S & T Foresight 2030” among of Priority thematic S & T areas (priority research areas) were included the following ones: Data processing and analysis technologies; Predictive modeling and simulation; Preservation of environment and environmental safety; Efficient utilization of renewable energy sources. The coming power from the sun as radiation can be used in such types of solar systems as both electric and thermal ones In this case, there is a possibility to preserve the purity of the environment, provide a balance between demand and supply of energy, and to solve at least a part of arising environmental problems. The accuracy of the prediction model for solar radiation depends largely upon the applied calculation algorithms, the network geometry as well as the number and the set of input parameters. According to studies [7, 11], the choice of necessary geographic and meteorological variables as input data for the ANN models are an understudied research element. It is planned to obtain regression equations and the correlation parameters to compute the solar radiation, to perform the output evaluation, and to formulate problems for the further continuation of studies
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