Prediction of Oil Consumption and Oil Access of Countries in The European Union Region with Machine Learning

Murat Beken, Onder Eyecioglu, Nursac Kurt

Abstract


Depending on the pandemic process, there are some problems around the world. One of them is the problem of fuel consumption and access to fuel. As a result of breakdowns in supply chains, accessibility and consumption processes for oil have changed. Accordingly, it is of great importance that future oil production, consumption, and access to oil can be predicted by some methods. Artificial intelligence stands out as a tool that can be used in this prediction. In current studies, artificial intelligence is often used for predictive purposes. In this study, it is tried to predict the future change in oil consumption and access to oil in the European Union region and candidate countries. Decision trees, Naive Bayes, Support vector machines, K nearest neighbor (KNN), and Ensemble Boosted trees were used as methods from supervised and unsupervised machine learning approaches. Depending on the different test parameters of the methods used, the estimation successes were observed, and the results were reported.


Keywords


Oil consumption, time-series estimation, artificial intelligence, machine learning

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References


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DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v6i3.250.g242

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