Forecasting the annual electricity consumption of Turkey using an optimized grey model Coskun Hamzacebi * , Huseyin Avni Es Karadeniz Technical University, Department of Industrial Engineering, Trabzon, Turkey article info Article history: Received 20 November 2013 Received in revised form 17 March 2014 Accepted 24 March 2014 Available online xxx Keywords: Energy demand forecasting Grey modeling (1,1) Direct forecasting abstract 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 pre- dicted for the 2013e2025 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). Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction Turkey, a transit point between Asia and Europe, is important country in terms of energy policy owing to its consumption amounts and strategic location. The population and the GDP of Turkey reached 735 billion people and $73.7 million in 2010 with an average increase of 8.7% and 177% compared to 2000, respec- tively. Energy consumption rates in Turkey have been rapidly increasing in parallel with GDP and population. Thus, Turkey has become a country that has the most swift increase in energy de- mand among the Organization for Economic Co-operation and Development (OECD) member countries in the last decade [1]. Turkey has been fast growing in terms of both its economy and population. As a result, the demand for electrical energy has increased quickly in the last decade. While the electricity consumption of Turkey was 98 billion kWh in 2000, it reached 172 billion kWh in 2010 with an average of 7.5 percent increase per year. Similarly, electricity generation reached 211 billion kWh in 2010 with an average of 7 percent increase per year. Thus, Turkey was the first in European countries and the second in the world countries in terms of growth rates of electrical energy consumption in the last ten years [2]. A final report by the Ministry of Energy and Natural Resources (MENR), electrical energy demand for 2021 is estimated to reach approximately 467 billion kWh and 424 billion kWh according to the higher demand and lower demand scenarios, respectively [3]. Accurate and reliable forecasting of the electrical energy con- sumption is of great importance in order to meet the increasing demand for electrical energy as well as sustaining industrialization and long-term stable energy policies. Since storing the alternating current electricity is impossible, accurate and reliable forecasting is also necessary for planning the right tools which provide the amount of required electrical energy when it is needed. In the literature, many different forecasting techniques were used in order to predict the electrical energy demand. Saab et al. [4] studied univariate autoregressive models to forecast the monthly electrical energy consumption in Lebanon. By using artificial neural networks (ANNs); An et al. [5] investigated the prediction of half- hour electricity demand in Australia, Pao [6] determined the con- sumption of electrical energy in Taiwan, and Gonzalez et al. [7] predicted the monthly electricity demand of Spain. By using regression models; Mohamed and Bodger [8] predicted the elec- tricity consumption under the effect of selected economic and demographic variables, Mirasgedis et al. [9] predicted the demand * Corresponding author. Tel.: þ90 462 377 2950; fax: þ90 462 325 6482. E-mail addresses: hamzacebi@ktu.edu.tr (C. Hamzacebi), avnies@ktu.edu.tr (H. A. Es). Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy http://dx.doi.org/10.1016/j.energy.2014.03.105 0360-5442/Ó 2014 Elsevier Ltd. All rights reserved. Energy xxx (2014) 1e7 Please cite this article in press as: Hamzacebi C, Es HA, Forecasting the annual electricity consumption of Turkey using an optimized grey model, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.03.105