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Oil Consumption Forecasting in Turkey using Artificial Neural Network

Author

Listed:
  • Ebru Turanoglu

    (Ebru Turanoglu, Izmir University, Department of Industrial Engineering, Izmir, Turkey)

  • Ozlem Senvar

    (Department of Industrial Engineering, Marmara University, Göztepe, Istanbul, Turkey)

  • Cengiz Kahraman

    (Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey)

Abstract

Oil and energy markets have experienced dramatic changes over the past three decades. Due to these changes, it may be difficult to model and forecast the oil consumption with traditional methods such as regression. Artificial Neural Networks (ANNs) are the strong rival of regression and time series in forecasting. ANNs provide good accuracy along with more reliable and precise forecasting for policy makers, in this regard, ANNs can establish the foundation for oil consumption management by providing good model results. This paper tries to unfold the oil consumption forecasting in Turkey using ANN through some predetermined inputs, which is data for population, GDP, import and export of Turkey from 1965 to 2010, with the aim of finding the essential structure of the data to forecast future oil consumption in Turkey with less error.

Suggested Citation

  • Ebru Turanoglu & Ozlem Senvar & Cengiz Kahraman, 2012. "Oil Consumption Forecasting in Turkey using Artificial Neural Network," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 1(4), pages 89-105, October.
    • Handle: RePEc:igg:jeoe00:v:1:y:2012:i:4:p:89-105
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    Cited by:

    1. Lean Yu & Zebin Yang & Ling Tang, 2016. "Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 423-451, March.
    2. Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, vol. 11(6), pages 1-21, June.

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