Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables

Kankal M., AKPINAR A., Komurcu M. I., ÖZŞAHİN T. Ş.

APPLIED ENERGY, cilt.88, sa.5, ss.1927-1939, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 88 Sayı: 5
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.apenergy.2010.12.005
  • Dergi Adı: APPLIED ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1927-1939
  • Anahtar Kelimeler: Energy consumption, Artificial neural network, Regression models, Turkey, ARTIFICIAL NEURAL-NETWORKS, GENETIC ALGORITHM APPROACH, ELECTRICITY CONSUMPTION, CAUSALITY RELATIONSHIP, FUTURE PROJECTION, GREY PREDICTION, ECONOMIC-GROWTH, UNIT-ROOT, DEMAND, GDP
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4 Mtoe in 2014. (C) 2010 Elsevier Ltd. All rights reserved.