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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2017

07.06.2016 | Original Article

Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey

verfasst von: Murat Kankal, Ergun Uzlu

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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Abstract

This paper studies the performance of an artificial neural network (ANN) with teaching–learning-based optimization (TLBO) for modeling electric energy demand (EED) in Turkey. The ANN with TLBO (ANN-TLBO) was compared to the ANN with backpropagation (ANN-BP) and the ANN with artificial bee colony algorithm (ANN-ABC) models. Gross domestic product, population, import, and export were selected as independent variables in the models. The results reveal that the ANN-TLBO models perform better than the ANN-BP and ANN-ABC models in EED estimation. The average root-mean-square error of the ANN-BP and ANN-ABC models was decreased by 42.3 and 39.3 % using the ANN-TLBO model, respectively. Different scenarios have been studied over a projected 6-year period, from 2013 to 2018, to forecast Turkey’s EED. The results of the proposed model give excellent clues with regards to its use in future energy studies.
Vorheriger Artikel The use of neural networks for the prediction of cone penetration resistance of silty sands
Nächster Artikel Effects of nonlinear Rosseland thermal radiation on MHD steady wall jet flow

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Metadaten
Titel
Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey
verfasst von
Murat Kankal
Ergun Uzlu
Publikationsdatum
07.06.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2409-2

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