Estimating transport energy demand using ant colony optimization

Abstract

This study proposes a heuristic algorithm based on ant colony optimization for estimating the transport energy demand (TED) of Turkey using gross domestic product, population, and vehicle-km. Three forms of the improved ant colony optimization transport energy demand estimation (IACOTEDE) models are used for improving estimating capabilities of TED models. Performance of IACOTEDE is compared with the Ministry of Energy and Natural Resources (MENR) projections. Sensitivity analysis is also carried out for testing the effects of the parameters. The quadratic form provided a better-fit solution to the observed data, and it underestimates Turkey's TED by about 28% less than the MENR projection in year 2025. Thus, it may be used with a highest correlation coefficient and considerably lower relative error according as the MENR projection in the testing period. It is also expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies. © 2012 Copyright Taylor and Francis Group, LLC.