2020
DOI: 10.35378/gujs.586107
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Estimation of Turkey’s Natural Gas Consumption by Machine Learning Techniques

Abstract: Highlights• Real data for energy demand of Turkey are obtained.• Five machine learning techniques are utilized to predict the future energy demand of Turkey.• Relative performances of the techniques are compared. • R statistical programming language is used to code algorithms.

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Cited by 4 publications
(2 citation statements)
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“…Machine learning is a method of building general algorithms that can give you detailed information about a particular dataset without having to write code [21]. We can divide the machine learning algorithms used in various models into groups: unsupervised and supervised learning.…”
Section: Machine Learning Algorithmsmentioning
confidence: 99%
“…Machine learning is a method of building general algorithms that can give you detailed information about a particular dataset without having to write code [21]. We can divide the machine learning algorithms used in various models into groups: unsupervised and supervised learning.…”
Section: Machine Learning Algorithmsmentioning
confidence: 99%
“…When it comes to real-world AI applications, the breadth of possibilities is striking. AI-driven techniques play a pivotal role in forecasting Turkey's natural gas consumption [10], utilizing LSTM-based deep learning methods for earthquake prediction through ionospheric data analysis [11], and improving the precision of daily wind energy predictions through machine learning and statistical techniques [12]. In the healthcare sector, AI comes to the forefront with a machine learning model for diagnosing Type 2 diabetes based on health behavior [13], while in the field of speech recognition, recurrent units like LSTM and GRU find applications in Turkish speech recognition techniques and broader speech processing endeavors [14].…”
Section: Introductionmentioning
confidence: 99%