TY - JOUR T1 - Using artificial neural network for solar energy level predicting AU - A.G. Mustafaev JO - Journal of Information and Computing Science VL - 3 SP - 163 EP - 167 PY - 2024 DA - 2024/01 SN - 13 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22441.html KW - solar energy, artificial neural network, multilayer perceptron, backpropagation. AB - Dagestan state university of national economy, Makhachkala, Russia E-mail: arslan_mustafaev@mail.ru (Received May 10 2018, accepted August 21 2018) Abstract One of the problems with the use of renewable energy sources is the determination of the optimal location of a wind or solar power station on the earth's surface. The paper suggests a model for predicting the solar energy level in the region, which allows choosing the most effective location for the location of a solar power plant. The forecast of the solar energy level is made by means of an artificial neural network, trained at the data of meteorological stations, using the backpropagation algorithm. Comparison of results of the solar energy level forecast made by the artificial neural network with the actual values shows a good correlation. This confirms the possibility of using artificial neural networks for modeling and forecasting in regions where there is no data on the level of solar energy, but there are other data from meteorological stations.

A.G. Mustafaev. (2024). Using artificial neural network for solar energy level predicting. Journal of Information and Computing Science. 13 (3). 163-167. doi: