Genetic Algorithm (GA) Approaches for the Transport Energy Demand Estimation: Model Development and Application

This study deals with estimating future transport energy demand using genetic algorithm (GA) approach. Genetic algorithm transport energy demand (GATENDM) model is developed based on socio-economic indicators (population, gross domestic product (GDP), import and export) and transportation indicators/parameters (car, bus, and truck sales). The GATENDM model developed is applied to Turkey, which is selected as an application country. This model in a quadratic form was found to provide the best fit solution to the observed data. It may be concluded that the model proposed can be used as an alternative solution and estimation technique to available estimation technique in predicting the future transportation energy utilization values of countries.

Keywords:

• genetic algorithm
• GA
• energy
• energy planning
• energy modeling
• energy policy
• future projections
• transportation
• Turkey

Acknowledgments

The authors are grateful for the support provided for the present work by the Ministry of Energy and Natural Resources of Turkey (MENR) and World Energy Council-Turkish National Committee (WEC-TNC).

Notes

^a MENR: The Ministry of Energy and Natural Resources of Turkey.

^b TOE: tons of oil equivalent.

^c Genetic algorithm transport energy demand.

Additional information

Notes on contributors

Arif Hepbasli

Arif Hepbasli was a Visiting Professor in Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada