Forecasting Hydrogen Fuel Requirement for Highly Populated Countries Using NARnet

Petroleum is being consumed at a rapid pace all over world, but the amount of petroleum is constant in earth crust and production to consumption requirement is not up to mark. It is expected that a day may come when this world will witness the crisis of this oil. For this our paper addresses the prediction of petroleum crisis in two most populated country of the world i.e. India and China using novel Artificial Neural Network (ANN) based approach. The set of observation comprising three features like population, petroleum production and petroleum consumption are being considered to design the predictive model. Our work shows that petroleum production over consumption with respect to sharp increase of population, leads to a decisive issue in production of an alternative fuel like Hydrogen fuel. In our analysis, we used the data provided by different government sources over a period of more than 30 years and then simulated by a multistep ahead prediction methodology, i.e. nonlinear autoregressive Network (NARnet) to predict petroleum crisis in near future. The results of present study reveals that for India, the Normalized Mean Square Error (NMSE) values found for population petroleum production and consumption are 0.000046, 0.2233 and 0.0041 respectively. Similarly for China the corresponding values are 0.0011, 0.0126 and 0.0041 respectively, which validates the accuracy of the proposed model. The study forecasts that by 2050 hydrogen fuel may be a suitable replacement for petroleum, and will not only reduce pollution, but also enhance the fuel efficiency at lower cost as compared to that of petroleum.