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A COMPARISON OF ARIMA AND GREY MODELS FOR ELECTRICITY CONSUMPTION DEMAND FORECASTING: THE CASE OF TURKEY

Year 2016, Volume: 13 Issue: 3, 234 - 245, 15.07.2016

Abstract

During last two decades energy requirements continue to rising with increasing population. The development and growth of a country and people living of standards are almost related to the energy utilization rate. Authors and researchers made different studies on Turkish electricity consumption that among the European Union and made predictions for the coming years. The purpose of this study is to compare ARIMA and Grey models each other with error estimations and estimate future electricity demand. This study is a proposition of a new approach by comparing grey prediction and ARIMA models with Model of Analysis of the Energy Demand (MAED) from 1970 until 2013. This study also explores new approach by using more data and suggestions regarding to electricity consumption. As a result, proposed approaches estimates have more accurate results than MAED model in the comparison of electricity consumption.

References

  • Akay, D. & Atak, M. (2007). “Grey Prediction With Rolling Mechanism For Electricity Demand Forecasting of Turkey”, Energy, 32, 1670-1675.
  • Al-Ghandoor, A, Al-Hinti, I, Jaber, J.O. & Sawalha, S.A. (2008). “Electricity Consumption and Associated GHG Emissions of The Jordanian Industrial Sector: Empirical Analysis and Future Projection”. Energy Policy, 36 (1), 258–67.
  • Amber, K. P., Aslam, M. W. & Hussain, S. K. (2015). “Electricity Consumption Forecasting Models For Administration Buildings of The UK Higher Education Sector”. Energy and Buildings, 90, 127-136.
  • Azadeh, A. & Tarverdian, S. (2007). “Integration of Genetic Algorithm, Computer Simulation and Design of Experiments For Forecasting Electrical Energy Consumption”. Energy Policy, 35, 5229-5249.
Year 2016, Volume: 13 Issue: 3, 234 - 245, 15.07.2016

Abstract

References

  • Akay, D. & Atak, M. (2007). “Grey Prediction With Rolling Mechanism For Electricity Demand Forecasting of Turkey”, Energy, 32, 1670-1675.
  • Al-Ghandoor, A, Al-Hinti, I, Jaber, J.O. & Sawalha, S.A. (2008). “Electricity Consumption and Associated GHG Emissions of The Jordanian Industrial Sector: Empirical Analysis and Future Projection”. Energy Policy, 36 (1), 258–67.
  • Amber, K. P., Aslam, M. W. & Hussain, S. K. (2015). “Electricity Consumption Forecasting Models For Administration Buildings of The UK Higher Education Sector”. Energy and Buildings, 90, 127-136.
  • Azadeh, A. & Tarverdian, S. (2007). “Integration of Genetic Algorithm, Computer Simulation and Design of Experiments For Forecasting Electrical Energy Consumption”. Energy Policy, 35, 5229-5249.
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Details

Journal Section Issue
Authors

Bilal Şişman

Publication Date July 15, 2016
Submission Date June 2, 2017
Published in Issue Year 2016 Volume: 13 Issue: 3

Cite

APA Şişman, B. (2016). A COMPARISON OF ARIMA AND GREY MODELS FOR ELECTRICITY CONSUMPTION DEMAND FORECASTING: THE CASE OF TURKEY. Kastamonu Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 13(3), 234-245.