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Energy consumption forecasting of Iran using recurrent neural networks

Avami, A ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1080/15567240802706734
  3. Publisher: 2011
  4. Abstract:
  5. In this paper, a recurrent neural network model is developed in order to forecast the energy consumption as a complex nonlinear function of gross domestic product (GDP) and population in Iran. This intelligent model is trained by total energy consumption data as output and the population and GDP as inputs during 1976-2001, while 5 annual data points of the following years (2002-2006) are used to validate the model. It can describe time dependencies efficiently and the convergence rate is much faster. This model forecasts the trend of energy consumption annually. Simulation results show that this model can predict energy consumption in Iran with acceptable accuracy. It is expected that this study will be helpful in developing highly applicable energy policies
  6. Keywords:
  7. Artificial neural networks ; Energy consumption ; Convergence rates ; Data points ; Gross domestic products ; Intelligent models ; Iran ; Nonlinear functions ; Recurrent neural network model ; Simulation result ; Time dependency ; Total energy consumption ; Computer simulation ; Electric load forecasting ; Energy policy ; Energy utilization ; Forecasting ; Population statistics ; Recurrent neural networks
  8. Source: Energy Sources, Part B: Economics, Planning and Policy ; Volume 6, Issue 4 , 2011 , Pages 339-347 ; 15567249 (ISSN)
  9. URL: http://www.tandfonline.com/doi/abs/10.1080/15567240802706734