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Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

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  • Kavaklioglu, Kadir

Abstract

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, [epsilon]-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best [epsilon]-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption.

Suggested Citation

  • Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:1:p:368-375
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    1. Hamzacebi, Coskun, 2007. "Forecasting of Turkey's net electricity energy consumption on sectoral bases," Energy Policy, Elsevier, vol. 35(3), pages 2009-2016, March.
    2. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    3. Kavrakoglu, Ibrahim, 1983. "Modelling energy-economy interactions," European Journal of Operational Research, Elsevier, vol. 13(1), pages 29-40, May.
    4. Ediger, Volkan S. & Camdali, Unal, 2007. "Energy and exergy efficiencies in Turkish transportation sector, 1988-2004," Energy Policy, Elsevier, vol. 35(2), pages 1238-1244, February.
    5. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    6. Duran Toksari, M., 2007. "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, Elsevier, vol. 35(8), pages 3984-3990, August.
    7. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
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