The world’s highest energy consumer (HC) countries currently constitute around 62% of the world energy consumption. Therefore, it is highly important to model their energy consumption to obtain an estimated profile of future world energy consumption. In this study the HCs’ energy consumptions are modeled using artificial neural networks (ANNs). The models are developed based on economic and demographic variables, which are gross domestic product, population, import, and export of the countries selected. Performance of the derived models is assessed using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) for the testing data. The contribution rate of each variable to the HCs’ energy consumption are also determined to demonstrate the governing variables on the energy consumption. The results show that the correlation coefficients between the ANN predictions and actual energy consumptions are higher than 90%. This indicates a high reliability of the models for forecasting future energy consumption of the HC. Additionally, MAPE, MAE and RMSE values indicate that the ANN models can give adequate forecasting for the HCs’ energy consumption. Furthermore, contribution rates of input variables on energy consumption also indicate that energy consumption of each country studied is governed by different variables. It is expected that this study will be helpful for developing highly applicable energy policies for the HC countries. Furthermore, the results of this study can also be used for determining future trends in the global energy demand.

In the current study, consumption of energy sources (CES) are modeled using trend analysis and projections are presented for future planning. For projections, trends in CES during 1965–2010 are first identified. According to the trend observed, the appropriate regression analysis is employed to catch the best relations between CES and years. Developed models are tested by the t-distribution, the F-distribution and residual analysis. Additionally, the calculated results for CES from fitted curves that were compared with the projected CES calculated by British Petroleum and the International Energy Agency. Statistically significant trends are indicated for each kind of energy source. It is seen that differences between calculated and the projected CES from different scenarios are in the acceptable range. It is concluded that the models are statistically valid in terms of t-test and F-test. The residuals also confirm the accuracy of the models. The results show that the developed models can be used for projections of primary energy consumption by the sources for future planning.

In the current modeling, trend analysis is used to model coal consumption (CC) and forecast the future coal demand. For modeling, trends in the CC of the world and the major coal consuming countries during 1965–2010 are identified. According to the trend observed, the appropriate regression analysis is employed to catch the best model concerning the relations between the CC and the years. The developed models are tested by t-distribution, F-distribution and residual analysis. Statistically significant trends are determined for the United States, India, Japan, South Africa, South Korea, Australia, Taiwan, Turkey, Thailand, and the world. The results show that the proposed models can be used for the forecasting of future coal demand. It is also concluded that differences between forecasted CC by proposed models and forecasted CC by British Petroleum and International Energy Agency are also in the acceptable range.

In this study, the global oil production is modeled in order to forecast future projections based on the historical trend using the linear and nonlinear regression analysis. The developed models are validated considering the behavior of determination coefficient, F, and t tests. Forecasting performances of the proposed models are also compared using mean absolute percentage error. The results show that proposed models can be effectively used for forecasting the global oil production. The compared results show that the inverse regression model gives the best forecasting performance. It forecast the global oil production as 4593 Mt in 2020, 8.84% higher than the level in 2014.