Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers

Gokhan Aydin, Hyongdoo Jang, Erkan Topal

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


ABSTRACT: 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.

Original languageEnglish
Pages (from-to)212-219
Number of pages8
JournalEnergy Sources, Part B: Economics, Planning and Policy
Issue number3
Publication statusPublished - Mar 3 2016
Externally publishedYes


  • Artificial neural networks
  • energy consumption
  • highest energy consumers
  • modeling

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Energy Engineering and Power Technology
  • Fuel Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)


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