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Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique

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  • Al-Ghandoor, Ahmed
  • Samhouri, Murad
  • Al-Hinti, Ismael
  • Jaber, Jamal
  • Al-Rawashdeh, Mohammad

Abstract

This paper illustrates a new approach to model and forecast the transport energy demand of Jordan based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the double exponential smoothing techniques. The ANFIS model has been developed using socio-economic and transport related indicators based on annual number of vehicles, vehicle ownership level, income level, and fuel prices in Jordan. The double exponential smoothing technique has been used to forecast the different transport indicators to feed the developed ANFIS model in order to forecast the transport energy demand for the next two decades. The model has been validated using testing data and has showed very accurate results of 97%. The results show that the transport energy demand is expected to increase at % 4.9 yr−1 from years 2010–2030. In addition, a number of policy gaps are identified as contributors to the low efficiency composition of the fleet in terms of engine size and vehicle age. It is expected that the results of this study will be helpful in developing highly applicable and productive planning for future transport energy policies.

Suggested Citation

  • Al-Ghandoor, Ahmed & Samhouri, Murad & Al-Hinti, Ismael & Jaber, Jamal & Al-Rawashdeh, Mohammad, 2012. "Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique," Energy, Elsevier, vol. 38(1), pages 128-135.
    • Handle: RePEc:eee:energy:v:38:y:2012:i:1:p:128-135
    DOI: 10.1016/j.energy.2011.12.023
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    5. Al-Ghandoor, A., 2012. "Analysis of Jordan's industrial energy intensity and potential mitigations of energy and GHGs emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4479-4490.
    6. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    7. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
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    Keywords

    Energy; Transportation; ANFIS; Jordan;
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