Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption

Creative Commons License


Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol.13, no.1, pp.307-313, 2024 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.17798/bitlisfen.1395411
  • Journal Name: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
  • Journal Indexes: TR DİZİN (ULAKBİM)
  • Page Numbers: pp.307-313
  • Abdullah Gül University Affiliated: Yes


Natural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.