Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm

Ergun Uzlu, Murat Kankal, Adem Akpınar and Tayfun Dede

Energy, 2014, vol. 75, issue C, 295-303

Abstract: The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching–learning-based optimization) algorithm to estimate energy consumption in Turkey. Gross domestic product, population, import, and export data were selected as independent variables in the model. Performances of the ANN–TLBO model and the classical back propagation-trained ANN model (ANN–BP (teaching–learning-based optimization) model) were compared by using various error criteria to evaluate the model accuracy. Errors of the training and testing datasets showed that the ANN–TLBO model better predicted the energy consumption compared to the ANN–BP model. After determining the best configuration for the ANN–TLBO model, the energy consumption values for Turkey were predicted under three scenarios. The forecasted results were compared between scenarios and with projections by the MENR (Ministry of Energy and Natural Resources). Compared to the MENR projections, all of the analyzed scenarios gave lower estimates of energy consumption and predicted that Turkey's energy consumption would vary between 142.7 and 158.0 Mtoe (million tons of oil equivalent) in 2020.

Keywords: Teaching–learning-based optimization algorithm; Energy consumption/demand; Neural networks; Turkey (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:75:y:2014:i:c:p:295-303

DOI: 10.1016/j.energy.2014.07.078

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