Mustafaev A.G. Neural network model for forecasting the level of solar energy for solving alternative energy problems

Abstract: One of the problems hampering the development of the active use of renewable energy is to determine the optimal placement of wind and solar power plants on the earth's surface. The paper presents a model for predicting the level of solar energy in the region allowing choosing the most effective location for solar power location. The forecast of the level of solar energy is given using an artificial neural network of direct distribution educated on the basis of meteorological stations data using the back-propagation algorithm. Designing a neural network was carried out with the Neural Network Toolbox package of MATLAB 8.6 (R2015b). Comparison of results of forecasting solar energy performed by artificial neural network level with the current values shows a good correlation. This confirms the possibility of using artificial neural networks for modeling and forecasting in regions where there are no data on the level of solar energy, but some other data of meteorological stations is present.

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