Prospects for future projections of the basic energy sources in Turkey


SÖZEN A. , ARCAKLIOĞLU E.

ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, vol.2, no.2, pp.183-201, 2007 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2 Issue: 2
  • Publication Date: 2007
  • Doi Number: 10.1080/15567240600813930
  • Title of Journal : ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY
  • Page Numbers: pp.183-201

Abstract

The main goal of this study is to develop the energy sources estimation equations in order to estimate the future projections and make correct investments in Turkey using artificial neural network (ANN) approach. It is also expected that this study will be helpful in demonstrating energy situation of Turkey in amount of EU countries. Basic energy indicators such as population, gross generation, installed capacity, net energy consumption, import, export are used in input layer of ANN. Basic energy sources such as coal, lignite, fuel-oil, natural gas and hydraulic are in output layer. Data from 1975 to 2003 are used to train. Three years (1981, 1994 and 2003) are only used as test data to confirm this method. Also, in this study, the best approach was investigated for each energy sources by using different learning algorithms (scaled conjugate gradient [SCG] and Levenberg-Marquardt [LM]) and a logistic sigmoid transfer function in the ANN with developed software. The statistical coefficients of multiple determinations (R-2-value) for training data are equal to 0.99802, 0.99918, 0.997134, 0.998831 and 0.995681 for natural gas, lignite, coal, hydraulic, and fuel-oil, respectively. Similarly, these values for testing data are equal to 0.995623, 0.999456, 0.998545, 0.999236, and 0.99002. The best approach was found for lignite by SCG algorithm with seven neurons so mean absolute percentage error (MAPE) is equal to 1.646753 for lignite. According to the results, the future projections of energy indicators using ANN technique have been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users.