Predicting the Power Generation from Renewable Energy Sources by using ANN

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dc.contributor.authorRubanenko, Olena
dc.contributor.authorGundebommu, Sree Lakshmi
dc.contributor.authorCosovic, Marijana
dc.contributor.authorLesko, Vladislav
dc.date.accessioned2023-10-19T11:02:11Z
dc.date.available2023-10-19T11:02:11Z
dc.date.issued2021
dc.description.abstractThis paper proposes power generation forecasting for photovoltaic power plants by using Adaptive Neuro-Fuzzy Inference Systems library in MATLAB and considering meteorological factors. Renewable energy sources (RES) introduce compensation instability problems in the grid hence forecasting methods are considered. Especially important for grid operators is a day ahead forecasting as it can reduce negative imbalance price. 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 or use of hydrogen technologies and modern storage systems can provide grid balance. In addition, decreasing the deviation of the current (real) value from the predicted value of power generation is a way to compensate for power unbalance.
dc.identifier.doi10.1109/INFOTEH51037.2021.9400527
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/848
dc.language.isoen
dc.publisherFaculty of Electrical Engineering, University of East Sarajevo
dc.source20th International Symposium INFOTEH-JAHORINA
dc.subjectrenewable energy sources; power grid; power balance; optimal control
dc.titlePredicting the Power Generation from Renewable Energy Sources by using ANN
dc.typeArticle
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