Neurogenetic modeling of energy demand in the United Arab Emirates, Saudi Arabia, and Qatar

Syed Masiur Rahman*, A. N. Khondaker, Mohammad Imtiaz Hossain, Md Shafiullah, Md Arif Hasan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Socio-economic variables including gross domestic product, population, and energy and electricity production are used in modeling and forecasting national energy demands of the United Arab Emirates, Saudi Arabia, and Qatar. The proposed model features: (i) the nonlinear component of energy demand (removal of linear trend), (ii) application of double exponential smoothing method for input data projection, and (iii) genetic algorithm-based artificial neural network (ANN) models. The proposed neuro-genetic model performed very well for the three selected countries. The coefficient of determination and Willmott's index of agreement for the training and testing dataset are quite high whereas the mean absolute error, mean absolute percentage error and root mean squared error are quite low. The acceptable agreements between the observed energy consumption and the model predictions revealed its viability for the study of energy demand in the three selected member states of the energy exporting regional alliance Gulf Cooperation Council (GCC).

Original languageEnglish
Pages (from-to)1208-1216
Number of pages9
JournalEnvironmental Progress and Sustainable Energy
Issue number4
StatePublished - Jul 2017

Bibliographical note

Publisher Copyright:
© 2017 American Institute of Chemical Engineers Environ Prog


  • Gulf cooperation council
  • artificial neural network
  • energy demand
  • energy policy
  • neurogenetic model

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Chemical Engineering (all)
  • Renewable Energy, Sustainability and the Environment
  • Water Science and Technology
  • Environmental Science (all)
  • Waste Management and Disposal


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