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FORECASTING TURKEY’S ENERGY DEMAND USİNG ARTIFICIAL NEURAL NETWORKS: THREE SCENARIO APPLICATIONS

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Author(s): Hakan HOTUNLUOĞLU | Etem KARAKAYA

Journal: Ege Academic Review
ISSN 1303-099X

Volume: 11;
Issue: Special;
Start page: 87;
Date: 2011;
Original page

Keywords: Energy demand | Energy demand forecasting | Energy demand modelling.

ABSTRACT
Energy has become increasingly crucial for countries as we have experienced high economic growth, increases in population together with rapid urbanization in the globalized world. Turkey’s energy demand has grown rapidly and is expected to continue growing. In this context many studies have been car-ried out to forecast energy demand in Turkey. The energy de-mand forecasts are officially prepared by the Turkish Ministry of Energy and Natural Resources (MENR). However, MENR fore-casts are significantly higher when compared with realized de-mand and the results of other academic studies. In this study, Turkey’s energy demand is forecasted by using artificial neural network technique, a type of artificial intelligence application. For this purpose, three different scenarios are developed. These are: ‘static scenarios’, where economic growth is assumed to be stable, ‘sustainability scenarios’, where energy intensiti-es are assumed to be decreasing and finally ‘periodic-change scenarios’, where the economic growth is assumed to change during five different time periods by 2030. Moreover, both static and sustainability scenarios are further investigated un-der high, medium and slow economic growth assumptions. Periodic-change scenarios also consist of two subscenarios, where energy intensities are assumed to decrease and stay the same. All scenarios are applied to the total energy demand of Turkey. The results of the energy demand estimations found by our models are compared with the official estimations of the MENR. It is concluded that the MENR estimations are sig-nificantly higher than what we have found with our models.
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