Forecasting of Turkey's Sectoral Energy Demand by Using Fuzzy Grey Regression Model


Abstract views: 152 / PDF downloads: 213

Authors

  • Abdulkerim Karaaslan Atatürk University
  • Mesliha Gezen Atatürk University

Abstract

Population growth, technological developments, economical growth and efforts to achieve a high standard of living increase the demand for energy. Satisfying this increasing demand without interruption is of vital importance for countries to ensure security of supply. Safely forecasting the energy demand of Turkey, which is about 3-4 times the world average, is important for sustainable development and improving standards of living in the country. This study seeks to forecast Turkey's total energy demand and determine the distribution of this demand among sectors and the amount of unutilized energy. In the study, the energy demand projection until 2023 was revealed with fuzzy grey regression model (FGRM) using the data between years 1990-2012.Keywords: fuzzy grey prediction; sectorial energy demand in Turkey; fuzzy grey regression modelJEL Classifications: C610, L690

Downloads

Download data is not yet available.

Downloads

Published

2017-01-17

How to Cite

Karaaslan, A., & Gezen, M. (2017). Forecasting of Turkey’s Sectoral Energy Demand by Using Fuzzy Grey Regression Model. International Journal of Energy Economics and Policy, 7(1), 67–77. Retrieved from https://www.econjournals.com/index.php/ijeep/article/view/2759

Issue

Vol. 7 No. 1 (2017)

Section

Articles

License

Submission of an article implies that the work described has not been published previously , that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.