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

UZLU E., Kankal M., Akpinar A., DEDE T.

ENERGY, cilt.75, ss.295-303, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 75
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.energy.2014.07.078
  • Dergi Adı: ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.295-303
  • Anahtar Kelimeler: Teaching-learning-based optimization algorithm, Energy consumption/demand, Neural networks, Turkey, PARTICLE SWARM OPTIMIZATION, PARAMETER OPTIMIZATION, MULTIOBJECTIVE OPTIMIZATION, DEMAND ESTIMATION, COLONY ALGORITHM, ECONOMIC-GROWTH, DESIGN, INTELLIGENCE, HYDROPOWER, PREDICTION
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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. (C) 2014 Elsevier Ltd. All rights reserved.