Abstract
Energy demands of countries are changing rapidly in parallel with development, industrialization, urbanization, the spread of technology, prosperity, and population growth. Energy use in the transport sector in the last few years, Turkey has shown a significant increase. Therefore, energy management and predicting are critically important to environmental safety and the upcoming economic well-being. In recent years, studies to determine the energy demand have accelerated. In Addition, in order to estimate the demand levels in the most accurate way, the most appropriate model should be selected. In this study, different models for predicting Turkey's transport energy demand by using artificial neural networks have been established. Population, oil prices, gross domestic product, ton-km, vehicle-km, and passenger-km are selected as parameters by considering 1975 and 2016 data. The best model is tried to be obtained with the models in which different parameters are used together. The best model was established with the oil price, population, ton-km and it was determined that this model had the lowest error and highest R2 values.
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