Özet:

Energy, which is one of the main determinants of the economy, is an important production factor for all countries. As a developing country, Turkey is a country that increases its energy demand day by day. It is very important to make reliable energy consumption forecasts for the future in today's world where there is an energy crisis. In this work; the artificial neural networks (ANN) and adaptive-network-based fuzzy inference system (ANFIS) models were used to examine the effects of imports, exports, economic growth (Gross Domestic Product) and population on net energy consumption of Turkey. The reliability of the ANN and ANFIS models was determined using several statistical indicators. In the ANN model; R2, MAPE, and cov values were found as 0.997397669, 0.78259322, and 5.3228538, respectively. In the ANFIS model; R2, MAPE, and cov values were found as 0.997845364, 0.70709233, and 4.84339908, respectively. The obtained results from the ANN are compared with the ANFIS, in which the same data sets are used. The ANFIS model is a little better than ANN model. Using the weights obtained from the trained network, a new formula for determining net energy consumption is proposed. The results obtained, it is showing that both models can be successfully used to forecast energy consumption.

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