Uzlu, ErgunKankal, MuratDede, Tayfun2022-08-232022-08-232014-10-01Uzlu, E. vd .(2014). "Estimates of energy consumption in Turkey using neural networks with the teaching-learning-based optimization algorithm". Energy, 75, Special Issue, 295-303.0360-54421873-6785https://doi.org/10.1016/j.energy.2014.07.078https://www.sciencedirect.com/science/article/pii/S0360544214009116http://hdl.handle.net/11452/28320The 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.eninfo:eu-repo/semantics/closedAccessTeaching-learning-based optimization algorithmEnergy consumption/demandNeural networksTurkeyParticle swarm optimizationParameter optimizationMultiobjective optimizationDemand estimationColony algorithmEconomic-growthDesignIntelligenceHydropowerPredictionThermodynamicsEnergy & fuelsTurkeyEnergy utilizationLearning algorithmsNeural networksOptimizationPopulation statisticsANN (artificial neural network)Classical back-propagationGross domestic productsIndependent variablesModel accuracyTeaching-learning-based optimizationsTraining and testingAlgorithmData setEnergy useError analysisEstimation methodNumerical modelBackpropagationArticle0003433399000312-s2.0-8490806927829530375Special IssueThermodynamicsEnergy & fuelsArtificial Neural Network; Electricity Demand; Autoregressive Integrated Moving Average