Artificial neural network analysis of world green energy use


Ermis K., Midilli A. , Dincer I., Rosen M. A.

ENERGY POLICY, cilt.35, ss.1731-1743, 2007 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 35 Konu: 3
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.enpol.2006.04.015
  • Dergi Adı: ENERGY POLICY
  • Sayfa Sayıları: ss.1731-1743

Özet

This paper focuses on the analysis of world green energy consumption through artificial neural networks (ANN). In addition, the consumption is also analyzed of world primary energy including fossil fuels such as coal, oil and natural gas. A feed-forward back-propagation ANN is used for training and learning processes by taking into consideration data from the literature of world energy consumption front 1965 to 2004. Also, an ANN approach for forecasting world green energy consumption to the year 2050 is presented, and the consumption equations for different energy sources are derived. The environmental aspects of green energy and fossil fuels are discussed in detail. The resulting ANN-based equation curve profiles verify that the available economic reserves of fossil fuel resources are limited, and become "depleted" in the near future. It is expected that world green energy consumption will reach almost 62.74 EJ by 2010, and be on average 32.29% of total energy use between 2005 and 2025. However, world green energy and natural gas consumption will continue increasing after 2050, while world oil and coal consumption are expected to remain relatively stable after 2025 and 2045, respectively. The ANN approach appears to be a suitable method for forecasting energy consumption data, should be utilized in efforts to model world energy consumption. (c) 2006 Elsevier Ltd. All rights reserved.