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Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey

Listed author(s):
  • Günay, M. Erdem
Registered author(s):

    In this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (2007–2013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460TWh in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0301421515302329
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    Article provided by Elsevier in its journal Energy Policy.

    Volume (Year): 90 (2016)
    Issue (Month): C ()
    Pages: 92-101

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    Handle: RePEc:eee:enepol:v:90:y:2016:i:c:p:92-101
    DOI: 10.1016/j.enpol.2015.12.019
    Contact details of provider: Web page: http://www.elsevier.com/locate/enpol

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