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

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 i...

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Published in:	Energy (Oxford) Vol. 75; pp. 295 - 303
Main Authors:	Uzlu, Ergun, Kankal, Murat, Akpınar, Adem, Dede, Tayfun
Format:	Journal Article Conference Proceeding
Language:	English
Published:	Kidlington Elsevier Ltd 01.10.2014 Elsevier
Subjects:	Algorithms Applied sciences data collection Energy Energy consumption Energy consumption/demand Estimates Exact sciences and technology exports gross domestic product imports Learning theory Mathematical models natural resources Neural networks oils Optimization Projection Teaching–learning-based optimization algorithm Turkey Turkey (country) Asia Turkey Turkey (country) Teaching–learning-based optimization algorithm Turkey Energy consumption/demand Neural networks Energy consumption Neural network Algorithm Optimization Teaching
ISSN:	0360-5442
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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. •This study is associated with predicting energy consumption in Turkey.•GDP (gross domestic product), population, import and export were used as predictor variables.•TLBO (teaching–learning-based optimization) and BP (back-propagation) were used to train ANNs (artificial neural networks).•ANN–TLBO predicted the energy consumption more accurately than ANN–BP.•Using the ANN–TLBO model, the energy consumption was forecasted until 2020.
AbstractList	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. 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. •This study is associated with predicting energy consumption in Turkey.•GDP (gross domestic product), population, import and export were used as predictor variables.•TLBO (teaching–learning-based optimization) and BP (back-propagation) were used to train ANNs (artificial neural networks).•ANN–TLBO predicted the energy consumption more accurately than ANN–BP.•Using the ANN–TLBO model, the energy consumption was forecasted until 2020.
Author	Akpınar, Adem Kankal, Murat Dede, Tayfun Uzlu, Ergun
Author_xml	– sequence: 1 givenname: Ergun surname: Uzlu fullname: Uzlu, Ergun email: uzluergun@gmail.com, ergunuzlu@hotmail.com organization: Karadeniz Technical University, Faculty of Engineering, Department of Civil Engineering, 61080 Trabzon, Turkey – sequence: 2 givenname: Murat surname: Kankal fullname: Kankal, Murat organization: Karadeniz Technical University, Faculty of Engineering, Department of Civil Engineering, 61080 Trabzon, Turkey – sequence: 3 givenname: Adem surname: Akpınar fullname: Akpınar, Adem organization: Uludağ University, Faculty of Engineering, Department of Civil Engineering, 16059 Bursa, Turkey – sequence: 4 givenname: Tayfun surname: Dede fullname: Dede, Tayfun organization: Karadeniz Technical University, Faculty of Engineering, Department of Civil Engineering, 61080 Trabzon, Turkey
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Keywords	Teaching–learning-based optimization algorithm Turkey Energy consumption/demand Neural networks Energy consumption Neural network Algorithm Optimization Teaching
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Snippet	The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching–learning-based optimization) algorithm... The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching-learning-based optimization) algorithm...
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SubjectTerms	Algorithms Applied sciences data collection Energy Energy consumption Energy consumption/demand Estimates Exact sciences and technology exports gross domestic product imports Learning theory Mathematical models natural resources Neural networks oils Optimization Projection Teaching–learning-based optimization algorithm Turkey Turkey (country)
Title	Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm
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