Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2022Scopus SJRQ3/r/bayburt/14327
  Turkey's Long-Term Electricity Consumption Forecast

  Emec, S.; Akkaya, G.

  Demand forecasting is essential primarily for planning. Although it is crucial in many sectors and issues, it has particular importance for electricity. Therefore, the issue of electricity consumption forecasting has recently become a prevalent topic. In light of the above, this study aimed to develop an appropriate model to estimate the long-term electricity consumption of Turkey. The study consists of three steps. In the first step, eight models were developed to separately investigate the effects of eight input variables frequently used in electricity consumption forecasting studies in the literature. In the second step of the study, two models consisting of input variables with high impact in the first step were developed, and the trained performances of the developed models were calculated by using the regression analysis. In the final step, the combined effect of eight variables on electricity consumption forecasting was investigated using regression analysis. It can be conclude that the model in the third step showed significant results, and the model performance was good. Finally, Turkey's electricity consumption forecast for the years 2020-2030 was performed using the model in the third step.

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  AtıfWoS1·OpenAlex3

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• MakaleAçık Erişim2022/r/bayburt/15809
  Housing Demand Forecasting with Machine Learning Methods

  Emec, Seyma; Tekin, Duygu

  Housing is a place where sustainable urban spaces are produced and where people's physical, cultural, environmental, economic, social and psychological needs are evaluated together with their surroundings, rather than just a building where the need for shelter is met. With the acceleration of urbanization, new needs arise, and the first of these is the need for housing. The housing sector has become one of the most dynamic and continuous sectors associated with the increase in the need for housing. The need for adequate and accessible housing comes to the forefront in our country as well as in the world. Understanding and predicting the key features determining housing prices and value is an important consideration for urban planners and housing policymakers. In this study, machine learning and artificial neural network models were used to predict the housing demand of Konya, and their forecasting performances were compared. As a result, it was concluded that ANN is a better alternative for housing demand forecasting in Konya.

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  AtıfOpenAlex3·TR Dizin3

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