Modeling and Forecasting Turkey’s Electricity Consumption by using Artificial Neural Network

  • Şule Birim Business Administration, Faculty of Economical and Administrative Sciences, Celal Bayar University, IIBF Uncubozkoy Campus, Manisa, Turkey
  • Ayça Tümtürk Business Administration, Faculty of Economical and Administrative Sciences, Celal Bayar University, IIBF Uncubozkoy Campus, Manisa, Turkey
Keywords: Electricity consumption, artificial neural networks, Turkey, Multiple regression analysis.

Abstract

The article describes the future projections of electricity demand in Turkey by using multiple linear regression (MLR) and artificial neural Networks (ANN). For this purpose six independent variables which are GDP, population, import, export, employment and natural gas are identified as the possible predictors of electricity consumption. We used MLR to determine which independent variables will be selected to forecast future electricity consumption with ANN. These variables are used in stepwise regression in order to identify which variables predict the dependent variable best by using 1992 - 2014 data. Four different models were identified as the result of MLR including various combinations of selected four variables that are population, import, natural gas and employment. In model 1 population, in model 2 population, import, in model 3 population, import and natural gas, in model 4 population, import, natural gas and employment are used as independent variables in ANN.  In this study to model the proposed problem of forecasting Turkey’s electricity consumption in the years 2015-2023, a feed forward multilayer perceptron neural network has been used. According to the forecasted results of four models Turkey’s electricity consumption is projected to vary between 337087.4 and 385006.6 Gwh by 2023. Forecasted results were compared with Turkish Electricity Transmission Company (TEIAS) projections. Except Model 2 our forecast results showed lower values than TEIAS estimates.

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Published
2016-09-22
Section
Articles