Forecasting the annual electricity consumption of Turkey using an optimized grey model

Hamzacebi C., Es H. A.

ENERGY, vol.70, pp.165-171, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 70
  • Publication Date: 2014
  • Doi Number: 10.1016/
  • Journal Name: ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.165-171
  • Keywords: Energy demand forecasting, Grey modeling (1,1), Direct forecasting, ENERGY-CONSUMPTION, PREDICTION-APPROACH, DEMAND, PERFORMANCE, NETWORK
  • Karadeniz Technical University Affiliated: Yes


Energy demand forecasting is an important issue for governments, energy sector investors and other related corporations. Although there are several forecasting techniques, selection of the most appropriate technique is of vital importance. One of the forecasting techniques which has proved successful in prediction is Grey Modeling (1,1). Grey Modeling (1,1) does not need any prior knowledge and it can be used when the amount of input data is limited. However, the basic form of Grey Modeling (1,1) still needs to be improved to obtain better forecasts. In this study, total electric energy demand of Turkey is predicted for the 2013-2025 period by using an optimized Grey Modeling (1,1) forecasting technique called Optimized Grey Modeling (1,1). The Optimized Grey Modeling (1,1) technique is implemented both in direct and iterative manners. The results show the superiority of Optimized Grey Modeling (1,1) when compared with the results from literature. Another finding of the study is that the direct forecasting approach results in better predictions than the iterative forecasting approach in forecasting Turkey's electricity consumption. The supply values of primary energy resources in order to produce electricity have calculated for 2015, 2020 and 2025 by using the outputs of Optimized Grey Modeling (1,1). (C) 2014 Elsevier Ltd. All rights reserved.