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

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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.

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Authors and Affiliations

  1. Civil Engineering Department, Karadeniz Technical University, 61080, Trabzon, Turkey

    Murat Kankal & Ergun Uzlu

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  1. Murat Kankal
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  2. Ergun Uzlu
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Correspondence to Murat Kankal.

Additional information

This study is dedicated to the memory of the late Assoc. Prof. Dr. Murat İhsan KÖMÜRCÜ, who passed away in February 2013.

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Kankal, M., Uzlu, E. Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Comput & Applic 28 (Suppl 1), 737–747 (2017). https://doi.org/10.1007/s00521-016-2409-2

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  • DOI: https://doi.org/10.1007/s00521-016-2409-2

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