Title of article :
Prediction of Renewable Energy Production Using Grey Systems Theory
Author/Authors :
Darvishi Salookolaei, D Department of Mathematics - Payame Noor University, Tehran, Iran , Babaei, P Department of Mathematics - Payame Noor University, Tehran, Iran , Heydari gorji, S Department of Mathematics - Payame Noor University, Tehran, Iran
Abstract :
Due to the reduction of renewable energy resources such as fossil fuels, the energy crisis is one
of the most critical issues in today’s world. The application of these resources brings about many
environmental pollutions that lead to global warming. Therefore, various countries have attempted to
reduce potential damage and use renewable energies by the introduction and promotion of renewable
energies as an essential strategy to reduce CO2 emissions and to find alternatives to fossil energy in
the transportation and electricity generation sectors. This study attempts to predict the production
process of renewable energies in Iran by 2025 and study the characteristics of this energy and its
usage in the world and Iran. Since there are very few data in this field, four grey prediction models
are used including GM(1,1), DGM(2,1), Grey Verhulst and FGM(1,1) models. According to the
three indices of the error values of MSE, RMSE, and MAPE, all the predictions done by the methods
above are among the best prediction methods. By examining the results achieved, FGM(1,1) method
was the best model concerning its less error than other models and has estimated 16740.45 MW for
renewable energy production in 2025.
Keywords :
GM(1,1) , Renewable Energy , Absolute prediction error , Grey system , Prediction