Mathematical Modeling to Anticipate the Global Growth Trends of Renewable Energy

Abstract

Energy is prerequisite for the competitive economic growth or for the livelihoods of the increasing population of any nation but around 80% percent of the energy comes from fossil fuel which creates many environmental hazardous. The renewable energy which can save the planet is still a far cry. In this paper different models were analyzed to forecast the growth rate of renewable energy. To forecasting the trends, wide-range of data is not suitable always for prediction. The most latest only 20 years of data is considered for the analysis here in order to get the exact scenarios of the trends. Four models GM(1,1), NGBM(1,1), Holt’s, Nonlinear Regression were analyzed here and compare their accuracy.  By the Mean Absolute percentage Error (MAPE) method the accuracy and fitness were justified. This study reveals that among all the above four models the nonlinear regression model shows the highest accuracy which is 1.3%, and next grey model with 2.96% accuracy.  From the nonlinear regression model it is predicted that 52% energy may come from renewable sources by 2035.