dc.contributor.author | Rubanenko, O. | en |
dc.contributor.author | Gundebommu, S. L. | en |
dc.contributor.author | Cosovic, M. | en |
dc.contributor.author | Lesko, V. | en |
dc.contributor.author | Рубаненко, О. О. | uk |
dc.contributor.author | Лесько, В. О. | uk |
dc.date.accessioned | 2021-11-17T12:58:20Z | |
dc.date.available | 2021-11-17T12:58:20Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Predicting the Power Generation Renewable Energy Sources by using ANN [Text] / O. Rubanenko, S. L. Gundebommu, M. Cosovic, V. Lesko // 20th International Symposium INFOTEH-JAHORINA, 17-19 March 2021. – Bosnia and Herzegovina, 2021. – P. 1-6. | en |
dc.identifier.uri | http://ir.lib.vntu.edu.ua//handle/123456789/34196 | |
dc.description.abstract | The analysis of decreasing trend in prediction of CO2 emission is presented in this paper. Different reasons of CO2 reduction in Ukraine are presented; one is by increasing generation by renewable energy sources (RES). This on the other hand creates a new problem of RES power generation compensation instability. Means of ensuring the balance reliability of the power system in terms of RES integration are presented. The installation of charging stations for electric vehicles and the use of hydrogen technologies and modern storage can provide power grid balance. Also, decreasing the deviation of the current (real) value the predicted value of power generation is a way to compensate for power unbalance. This paper proposes power generation forecasting for photovoltaic power plants by using Adaptive Neuro-Fuzzy Inference Systems library in MATLAB and taking into account meteorological factors. | en |
dc.language.iso | en | en |
dc.publisher | Institute of Electrical and Electronics Engineers | en |
dc.relation.ispartof | 20th International Symposium INFOTEH-JAHORINA, 17-19 March 2021 : 1-6. | en |
dc.subject | renewable energy sources | en |
dc.subject | power grid | en |
dc.subject | power balance | en |
dc.subject | optimal control | en |
dc.title | Predicting the Power Generation Renewable Energy Sources by using ANN | en |
dc.type | Article | |
dc.identifier.doi | 10.1109/INFOTEH51037.2021.9400527 | |