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

Published in journal "Software systems and computational methods", 2016-2 in rubric "Knowledge Base, Intelligent Systems, Expert Systems, Decision Support Systems", pages 150-157.

Resume: 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.

Keywords: alternative power engineering, prediction, error back propagation, multilayer perceptron, modeling, artificial neural network, solar energy, feedforward network, supervised learning, resource map

DOI: 10.7256/2305-6061.2016.2.18314

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Bibliography:
Kruglov V.V., Borisov V.V. Iskusstvennye neyronnye seti. Teoriya i praktika. - M.: Goryachaya liniya - Telekom, 2001. - 382 s.
Izmerenie solnechnogo izlucheniya v solnechnoy energetike. [Elektronnyy resurs] http://www.kippzonen.com/Download/672/Solar-Energy-Guide-Russian (data obrashcheniya: 13.02.2016).
Sozen A., Arcaklioglu E., Ozalp M., Kanit E.G. Use of Artificial neural network for mapping of solar potential in Turkey. Applied Energy, 2004, Vol. 77, No. 3, pp. 273286.
Mustafaev A.G. Primenenie iskusstvennykh neyronnykh setey dlya ranney diagnostiki zabolevaniya sakharnym diabetom // Kibernetika i programmirovanie. 2016. - 2. - S.1-7. DOI: 10.7256/2306-4196.2016.2.17904. URL: http://e-notabene.ru/kp/article_17904.html
Ahmed M., Ahmad F., Wasim M. Estimation of global and diffuse solar radiation for Hyderabad, Sindh, Pakistan, Journal of Basic and Applied Sciences, 2009, Vol. 5, No. 2, pp. 73-77.
Nauchno-prikladnoy spravochnik Klimat Rossii. [Elektronnyy resurs] http://aisori.meteo.ru/ClspR (data obrashcheniya: 14.02.2016).
Tsaregorodtsev V.G. Optimizatsiya predobrabotki dannykh: konstanta Lipshitsa obuchayushchey vyborki i svoystva obuchennykh neyronnykh setey // Neyrokomp'yutery: razrabotka, primenenie. 2003, 7. - S.3-8.
Osovskiy S. Neyronnye seti dlya obrabotki informatsii. M.: Finansy i statistika, 2002. 344 s.
Galushkin A. Neyronnye seti. Osnovy teorii. Goryachaya Liniya Telekom, 2012. - 496 s.

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