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Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models

Author

Listed:
  • Ahmat Khazali Acyl

    (Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon)

  • Flavian Emmanuel Sapnken

    (Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon; & Laboratory of Technologies and Applied Science, PO Box 8698, IUT Douala, Douala, Cameroon; & Energy Insight- Tomorrow Today, PO Box 2043 Douala, Cameroon)

  • Aubin Kinfack Jeutsa

    (Higher Technical Teachers’ Training College, University of Buea, Buea, Cameroon)

  • Jean Marie Stevy Sama

    (Laboratory of Technologies and Applied Science, PO Box 8698, IUT Douala, Douala, Cameroon)

  • Marcel Rodrigue Ewodo-Amougou

    (Laboratory of Technologies and Applied Science, PO Box 8698, IUT Douala, Douala, Cameroon)

  • Jean Gaston Tamba

    (Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon; & Laboratory of Technologies and Applied Science, PO Box 8698, IUT Douala, Douala, Cameroon; & Energy Insight- Tomorrow Today, PO Box 2043 Douala, Cameroon)

Abstract

This study aims to estimate the demand for petroleum products (PP) in the Chadian road sector by 2030 and to determine which of the two models used is the most efficient. The methodology is based on two optimised Grey models, namely: the Sequential-GM(1,N)-GA and NeuralODE-GM(1,1) models. These models reduce forecasting errors compared with the conventional Grey model. The forecasts confirm that both models are robust, with MAPEs of 1.16% and 2.5% respectively for gasoline and diesel obtained with the Sequential-GM(1,N)-GA, and 3.3% and 4.8% respectively for gasoline and diesel obtained with the NeuralODE-GM(1,1). We note that the Sequential-GM(1,N)-GA is more robust than NeuralODE-GM(1,1) with regard to MAPEs. The estimated consumption needs for gasoline and diesel in the road transport sector by 2030 are 294376818.5 and 381570061.5 litres respectively for the Sequential-GM(1,N)-GA and 264376818.5 and 375570061.5 litres for the NeuralODE-GM(1,1). Based on these results, securing the supply of PP in the road transport sector requires the development of the downstream petroleum sector. The development of alternative energies and the acquisition of hybrid vehicles. A policy encouraging mass transport in urban areas can considerably reduce energy consumption in this sector. This study adds to the literature through the simultaneous use of two new optimised grey models and their comparison in terms of predicting demand for PP in the Chadian road transport sector.

Suggested Citation

  • Ahmat Khazali Acyl & Flavian Emmanuel Sapnken & Aubin Kinfack Jeutsa & Jean Marie Stevy Sama & Marcel Rodrigue Ewodo-Amougou & Jean Gaston Tamba, 2024. "Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 603-611, January.
    • Handle: RePEc:eco:journ2:2024-01-65
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; Petroleum Products; Road Transport; Grey Models; Chad;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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