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Transport energy demand forecast using multi-level genetic programming

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  • Forouzanfar, Mehdi
  • Doustmohammadi, A.
  • Hasanzadeh, Samira
  • Shakouri G, H.

Abstract

In this paper, a new multi-level genetic programming (MLGP) approach is introduced for forecasting transport energy demand (TED) in Iran. It is shown that the result obtained here has smaller error compared with the result obtained using neural network or fuzzy linear regression approach. The forecast uses historical energy data from 1968 to 2002 and it is based on three parameters; gross domestic product (GDP), population (POP), and the number of vehicles (VEH). The approach taken in this paper is based on genetic programming (GP) and the multi-level part of the name comes from the fact that we use GP in two different levels. At the first level, GP is used to obtain the time series model of the three parameters, GDP, POP, and VEH, and forecast those parameters for the time interval that their actual data are not available, and at the second level GP is used one more time to forecast TED based on available data for TED along with the data that are either available or predicted for the three parameters discussed earlier. Actual data from 1968 to 2002 are used for training and the data for years 2003–2005 are used to test the GP model. We have limited ourselves to these data ranges so that we could compare our results with the existing ones in the literature. The estimation GP for the model is formulated as a nonlinear optimization problem and it is solved numerically.

Suggested Citation

  • Forouzanfar, Mehdi & Doustmohammadi, A. & Hasanzadeh, Samira & Shakouri G, H., 2012. "Transport energy demand forecast using multi-level genetic programming," Applied Energy, Elsevier, vol. 91(1), pages 496-503.
    • Handle: RePEc:eee:appene:v:91:y:2012:i:1:p:496-503
    DOI: 10.1016/j.apenergy.2011.08.018
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    References listed on IDEAS

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    4. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    5. Tatiana Mitrova & Vyacheslav Kulagin & Dmitry Grushevenko & Ekaterina Grushevenko, 2015. "Technological Innovation as a Factor of Demand for Energy Sources in Automotive Industry," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 9(4), pages 18-31.
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    7. Abdulkerim Karaaslan & Mesliha Gezen, 2017. "Forecasting of Turkey s Sectoral Energy Demand by Using Fuzzy Grey Regression Model," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 67-77.

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