A Machine Learning Approach for the prediction of solid fuels consumption in Turkey

Paper ID: 
Environmental data analysis and modelling
Published under CEST2023
Proceedings ISBN:
Proceedings ISSN: 2944-9820
Celik N., (Corresponding) Konyalioglu A.
Solid fuels are very crucial energy sources as most of industries use them for obtaining heat, electricity and light. Furthermore, since solid fuels are scarce sources in Turkey, it is very important to forecast the consumption in order to effectively manage the energy policies and to conduct an effective planning for industries. In this study, it is aimed to forecast and to model the produce of solid fuels like lignite and coal in Turkey. In statistical analysis, machine learning techniques are applied for forecasting. There are several types of different machine learning algorithms such as supervised and unsupervised learning, reinforced learning, self-learning, feature learning etc. The methods we used are categorized as supervised learning since they build a mathematical model of a set of data that contains both the inputs and the desired outputs.
Solid Fuels, Environmental Data Analysis, Machine Learning