eISSN: 2618-6446
Latest Issue Archive Future Issues About Us JOURNALS

SETSCI - Volume 3 (2018)
ISAS2018-Winter - 2nd International Symposium on Innovative Approaches in Scientific Studies, Samsun, Turkey, Nov 30, 2018

Estimation of Electricity Consumption of Turkey by using ARIMA, Grey Model and Linear Regression Analysis (ISAS2018-Winter_173)
Zeynep Ceylan1*, Hakan Öztürk2, Birol Elevli3
1Ondokuz Mayıs University, Samsun, Turkey
2Ondokuz Mayıs University, Samsun, Turkey
3Ondokuz Mayıs University, Samsun, Turkey
* Corresponding author: zeynep.dokumacı@omu.edu.tr
Published Date: 2019-01-14   |   Page (s): 907-910   |    39     7

ABSTRACT The successful estimation of future electricity consumption has a crucial importance in the energy planning.
Because, in order to meet rising energy demand, policy makers should formulate electricity supply policies and make critical
decisions and develop new strategies. This study aims to compare prediction capabilities of three different techniques in order
to forecast electricity energy consumption of Turkey. These three techniques are; Autoregressive Integrated Moving Average
(ARIMA), grey prediction model GM (1,1) and Linear Regression (LR) analysis. Yearly electricity consumption data of
Turkey between 1970 and 2017 were obtained from the Turkish Electricity Transmission Company (TEIAS). The future
electricity demand for a period of 6 years from 2018 to 2023 has been predicted. ARIMA (1,1,2) model showed best results in
terms of highest value of coefficient of determination (R2 = 99.9 %). The results of the study can help decision makers in
planning future applications.  
KEYWORDS ARIMA, electricity consumption, estimation, grey model, linear regression analysis
REFERENCES [1] Hamzacebi, C., & Es, H. A. (2014). Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70, 165-171.
[2] Kavaklioglu, K. (2011). Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression. Applied Energy, 88(1), 368-375.
[3] Kavaklioglu, K. (2014). Robust electricity consumption modeling of Turkey using singular value decomposition. International Journal of Electrical Power & Energy Systems, 54, 268-276.
[4] Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438.
[5] Ozturk, H. K., Ceylan, H., Canyurt, O. E., & Hepbasli, A. (2005). Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy, 30(7), 1003-1012.
[6] Hamzaçebi, C. (2007). Forecasting of Turkey's net electricity energy consumption on sectoral bases. Energy policy, 35(3), 2009-2016.
[7] Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719-2727.
[8] Sözen, A., Akçayol, M. A., & Arcaklioğlu, E. (2006). Forecasting net energy consumption using artificial neural network. Energy Sources, Part B, 1(2), 147-155.
[9] Sözen, A., & Arcaklioglu, E. (2007). Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy policy, 35(10), 4981-4992.
[10] Sözen, A., Arcaklioğlu, E., & Özkaymak, M. (2005). Turkey’s net energy consumption. Applied Energy, 81(2), 209-221.
[11] Kankal, M., Akpınar, A., Kömürcü, M. İ., & Özşahin, T. Ş. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939.
[12] TEIAS electricity consumption in Turkey between 1970 and 2017, (2017). Retrieved from www.teias.gov.tr/sites/default/files/2017-06/Önsöz.xls.
[13] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
[14] Gujarati, D. N. (2004). Basic Econometrics. (4 th edtn) The McGrawHill Companies.
[15] Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). John Wiley & Sons.
[16] Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2001). Applied hybrid grey model to forecast seasonal time series. Technological Forecasting and Social Change, 67(2-3), 291-302.

SET Technology - Turkey

eISSN  : 2618-6446

E-mail : info@set-science.com
+90 533 2245325

Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TURKEY
©2018 SET Technology