Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption

Abstract

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.

Keywords

• Machine Learning
• Symbolic Aggregate Approximation
• Clustering
• Natural Gas

References

1. [1] T. S. Adebayo, M. T. Kartal, and S. Ullah, “Role of hydroelectricity and natural gas consumption on environmental sustainability in the United States: Evidence from novel time-frequency approaches,” Journal of Environmental Management, vol. 328, no. 116987, p.116987, 2023.
2. [2] M. O. Turan, T. Flamand, “Optimizing investment and transportation decisions for the European natural gas supply chain,” Applied Energy, vol. 337, p. 120859, 2023.
3. [3] S. Yildiz, “Doğal Gaz Tüketim Tahmini,” Sosyal Ve Beşeri Bilimler Dergisi, vol. 5, no. 1, 440-452, 2013.
4. [4] M. S. Shaari, T. B. Majekodunmi, N. F. Zainal, N. H. Harun, A. R Ridzuan, “The linkage between natural gas consumption and industrial output: New evidence based on time series analysis,” Energy, vol. 284, no. 1, p. 129395, 2023.
5. [5] B. Soldo, “Forecasting natural gas consumption,” Applied Energy, vol. 92, pp. 26–37, 2012.
6. [6] O. Kaynar, S. Taştan, F. Demirkoparan, “Yapay sinir ağlari ile doğalgaz tüketim tahmini,” Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 25, pp. 463-474, 2012.
7. [7] O. Çoban, C. C. Özcan, “Sektörel Açidan Enerjinin Artan Önemi: Konya İli İçin Bir Doğalgaz Talep Tahmini Denemesi,” Sosyal Ekonomik Araştırmalar Dergisi, vol. 11, no. 22, pp. 85-106, 2011.
8. [8] K. Oruç, K., &Ş. Çelik, “Isparta İli İçin Doğal Gaz Talep Tahmini,” Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 22, no. 1, pp.31-42, 2017

9. [9] Y. Hou, Q. Wang, & T. Tan, “A robust stacking model for predicting oil and natural gas consumption in China. Energy Sources,” Part B: Economics, Planning, and Policy, vol. 19, no. 1, 2024.
10. [10] EUROSTAT. (2023). DATABASE. Europa.eu. URL: https://ec.europa.eu/eurostat/ databrowser /view/nr g_cb_gasm/default/table?lang=en
11. [11] D. H. Yang, & Y. S. Kang, “Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification,” Sensors, vol. 22, no.14, pp. 5095–5095, 2022.
12. [12] J. Lin, E. Keogh, S. Lonardi, & B. Chiu, “A symbolic representation of time series, with implications for streaming algorithms,” in Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery - DMKD ’03, 2003
13. [13] Lin, J., Keogh, E., Wei, L., & Lonardi, S. “Experiencing SAX: a novel symbolic representation of time series,” Data Mining and Knowledge Discovery, vol. 15, no. 2, pp.107–144, 2007.
14. [14] A. Roques, & A. Zhao, “Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm,” Image Processing on Line, 12, pp. 604–624, 2022
15. [15] J. W Earnest, “Sum of Gaussian Feature-Based Symbolic Representations of Eddy Current Defect Signatures,” Research in Nondestructive Evaluation, vol. 34, no. 3-4, pp. 136–153, 2023.