OECD Ülkelerinin Sektör Bazlı Enerji Tüketim Oranlarının Louvain Kümeleme ile Analizi

Öz

Bu çalışma, OECD ülkelerindeki sektörlerin (tarım, hizmetler, endüstri, taşımacılık ve diğer sektörler) toplam enerji tüketimindeki paylarını 2011-2020 döneminde Louvain kümeleme analizi ile incelemektedir. Enerji tüketimi önemli bir kalkınma göstergesidir ve ülkelerin gelişimi hakkında önemli bilgiler sunar. Özellikle tarım, hizmetler, endüstri ve ulaştırma sektörleri gibi ana sektörlerin enerji tüketimindeki paylarının analizi, bir ülkenin ekonomik çeşitliliği, sanayileşme düzeyi ve ekonomik odakları hakkında önemli bilgiler sunabilir. Kümeleme analizi ile benzer enerji tüketim desenlerine sahip ülkeleri belirleyerek önemli çıkarımlar elde edilebilir. Çalışmada Louvain kümeleme analizi tercih edilmiştir. Louvain K-ortalama ve Hiyerarşik kümeleme yöntemlerine göre hızlı ve gürültü ile başaçıkabilme avantajına sahiptir. Çalışmanın sonuçları iki perspektiften değerlendirilmektedir. İlki veri setinin tanımlayıcı istatistiklerinden elde edilen çıkarımlar, ikincisi kümeleme analizinden elde edilen çıkarımlardır. Kümeleme analizi sonuçları zamansal boyuttaki küme değişimleri ve yıl bazlı kümelerin oluşumuna göre sunduğu içgörüler vuruglanmıştır. Ayrıca Türkiye özelinde kümeleme sonuçlarının sağladığı içgörüler değerlendirilmiştir.

Anahtar Kelimeler

• Enerji Tüketimi
• Sektörel Analiz
• Louvain Kümeleme Analizi
• OECD Ülkeleri
• Türkiye

Analysis of Sector Based Energy Consumption Rates of OECD Countries with Louvain Clustering

Öz

This study examines the shares of sectors (agriculture, services, industry, transportation and other sectors) in total energy consumption in OECD countries for the period 2011-2020 using Louvain cluster analysis. Energy consumption is an important development indicator and provides important information about the development of countries. In particular, the analysis of the shares of energy consumption of main sectors such as agriculture, services, industry and transport sectors can provide important information about a country's economic diversity, level of industrialization and economic focus. Cluster analysis can provide important insights by identifying countries with similar energy consumption patterns. Louvain cluster analysis was preferred in this study. Louvain clustering has the advantage of being fast and dealing with noise compared to K-means and Hierarchical clustering methods. The results of the study are evaluated from two perspectives. The first one is the inferences obtained from the descriptive statistics of the data set and the second one is the inferences obtained from the clustering analysis. The results of the cluster analysis emphasize the insights offered by the cluster changes in the temporal dimension and the formation of year-based clusters. In addition, the insights provided by the clustering results for Türkiye are evaluated.

Anahtar Kelimeler

• Energy Consumption
• Sectoral Analysis
• Louvain Cluster Analysis
• OECD Countries
• Türkiye

Kaynakça

1. Akyol, M. (2020). Enerji Tüketiminin Tarımsal Katma Değer Üzerindeki Etkisi: AB’ye Üye Geçiş Ekonomileri İçin Panel Veri Analizi. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8(İktisadi ve İdari Bilimler), 59–64. https://doi.org/10.18506/anemon.638824
2. Apergis, N., & Payne, J. E. (2009). CO2 Emissions, Energy Usage, and Output in Central America. Energy Policy. https://doi.org/10.1016/j.enpol.2009.03.048
3. Apergis, N., & Payne, J. E. (2010). A Panel Study of Nuclear Energy Consumption and Economic Growth. Energy Economics. https://doi.org/10.1016/j.eneco.2009.09.015
4. Basuchowdhuri, P., Sikdar, S., Nagarajan, V., Mishra, K., Gupta, S., & Majumder, S. (2019). Fast detection of community structures using graph traversal in social networks. Knowledge and Information Systems, 59(1), 1–31. https://doi.org/10.1007/s10115-018-1209-7
5. Bednarczyk, J. L., Brzozowska-Rup, K., & Luściński, S. (2021). Determinants of the Energy Development Based on Renewable Energy Sources in Poland. Energies, 14(20), 6762. https://doi.org/10.3390/en14206762
6. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
7. Bozkurt, K., & Yanardağ, Ö. (2017). Enerji Tüketimi Ve Ekonomik Büyüme: Gelişmekte Olan Ülkeler İçin Bir Panel Eşbütünleşme Analizi. Yönetim ve Ekonomi Araştırmaları Dergisi, 194–194. https://doi.org/10.11611/yead.306823
8. Cansız, Ö. F., Ünsalan, K., & Erginer, İ. (2020). Karayollari Enerji Tüketiminin Yapay Zekâ Ve Regresyon Yöntemleri İle Modellenmesi. Uludağ University Journal of The Faculty of Engineering, 1297–1314. https://doi.org/10.17482/uumfd.719031

9. de Rijk, M. M., Janssen, J. M. W., Fernández Chadily, S., Birder, L. A., Rahnama’i, M. S., van Koeveringe, G. A., & van den Hurk, J. (2022). Between-subject similarity of functional connectivity-based organization of the human periaqueductal gray related to autonomic processing. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.1028925
10. Dekker, M. M., França, A. S. C., Panja, D., & Cohen, M. X. (2021). Characterizing neural phase-space trajectories via Principal Louvain Clustering. Journal of Neuroscience Methods, 362, 109313. https://doi.org/10.1016/j.jneumeth.2021.109313
11. Demir, Y., & Görür, Ç. (2021). OECD Ülkelerine Ait Çeşitli Enerji Tüketimleri ve Ekonomik Büyüme Arasındaki İlişkinin Panel Eşbütünleşme Analizi ile İncelenmesi. Ekoist: Journal of Econometrics and Statistics. https://doi.org/10.26650/ekoist.2020.32.0005
12. Dhas, L. J. S., Mukunthan, B., & Rakesh, G. (2020). Hybridized Gradient Descent Spectral Graph and Local global Louvain Based Clustering of Temporal Relational Data. International Journal of Engineering and Advanced Technology, 9(3), 3515–3521. https://doi.org/10.35940/ijeat.C5989.029320
13. Emmons, S., Kobourov, S., Gallant, M., & Börner, K. (2016). Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale. PLOS ONE, 11(7), e0159161. https://doi.org/10.1371/journal.pone.0159161
14. Erandathi, M., Chung Wang, W. Y., & Hsieh, C.-C. (2022). Clustering the countries for quantifying the status of Covid-19 through time series analysis. Information Discovery and Delivery, 50(3), 297–311. https://doi.org/10.1108/IDD-03-2021-0034
15. Eser Özen, A., & Hanifi, T. (2018). Ana Sektörlerin Enerji Tüketimlerinin Ekonomik Büyüme Üzerine Etkisi: Türkiye Örneği (1972-2015). Business and Economics Research Journal, 9(3), 499–512. https://doi.org/10.20409/berj.2018.119
16. F. Al-Mukhtar, A., & S. Al-Shamery, E. (2018). Greedy Modularity Graph Clustering for Community Detection of Large Co-Authorship Network. International Journal of Engineering & Technology, 7(4.19), 857. https://doi.org/10.14419/ijet.v7i4.19.28058
17. Formoso, A., Chavula, J., Phokeer, A., Sathiaseelan, A., & Tyson, G. (2018). Deep Diving into Africa’s Inter-Country Latencies. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, 2231–2239. https://doi.org/10.1109/INFOCOM.2018.8486024
18. Güngör, C. (2023). Energy Consumption in Agricultural of Türkiye. European Journal of Agriculture and Food Sciences, 5(3), 1–3. https://doi.org/10.24018/ejfood.2023.5.3.672
19. Hosseini, S., & Sardo, S. R. (2021). Data mining tools -a case study for network intrusion detection. Multimedia Tools and Applications, 80(4), 4999–5019. https://doi.org/10.1007/s11042-020-09916-0
20. Indriyanti, I., Ichsan, N., Fatah, H., Wahyuni, T., & Ermawati, E. (2022). Implementasi Orange Data Mining Untuk Prediksi Harga Bitcoin. Jurnal Responsif : Riset Sains Dan Informatika, 4(2), 118–125. https://doi.org/10.51977/jti.v4i2.762
21. Ishak, A., Siregar, K., Aspriyati, Ginting, R., & Afif, M. (2020). Orange Software Usage in Data Mining Classification Method on The Dataset Lenses. IOP Conference Series: Materials Science and Engineering, 1003(1), 012113. https://doi.org/10.1088/1757-899X/1003/1/012113
22. Ke, J., Price, L., Ohshita, S., Fridley, D., Khanna, N., Zhou, N., & Levine, M. (2012). China’s Industrial Energy Consumption Trends and Impacts of the Top-1000 Enterprises Energy-Saving Program and the Ten Key Energy-Saving Projects. Energy Policy. https://doi.org/10.1016/j.enpol.2012.07.057
23. Kim, Y.-H., Seo, S., Ha, Y.-H., Lim, S., & Yoon, Y. (2013). Two Applications of Clustering Techniques to Twitter: Community Detection and Issue Extraction. Discrete Dynamics in Nature and Society, 2013, 1–8. https://doi.org/10.1155/2013/903765
24. Komarnicka, A., & Murawska, A. (2021). Comparison of Consumption and Renewable Sources of Energy in European Union Countries—Sectoral Indicators, Economic Conditions and Environmental Impacts. Energies, 14(12), 3714. https://doi.org/10.3390/en14123714
25. Lean, H. H., & Smyth, R. (2010). CO2 Emissions, Electricity Consumption and Output in ASEAN. Applied Energy. https://doi.org/10.1016/j.apenergy.2010.02.003
26. Li, M., Qin, J., Jiang, T., & Pedrycz, W. (2021). Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making. International Journal of Computational Intelligence Systems, 14(1), 1242. https://doi.org/10.2991/ijcis.d.210329.001
27. Lloyd, P. J. (2017). The Role of Energy in Development. Journal of Energy in Southern Africa, 28(1), 54. https://doi.org/10.17159/2413-3051/2017/v28i1a1498
28. Manimannan, G., Priya, R. L., & Arul Kumar, C. (2019). Application of Orange Data Mining Approach of Argiculture Productivity Index Performance in Tamilnadu. International Journal of Scientific and Innovative Mathematical Research, 7(8). https://doi.org/10.20431/2347-3142.0708003
29. Marinescu, C. (2019). The renewable Energy Sector in the European Union - A Statistical Analysis. Review of International Comparative Management, 20(1). https://doi.org/10.24818/RMCI.2019.1.52
30. Matta, J., Singh, V., Auten, T., & Sanjel, P. (2023). Inferred networks, machine learning, and health data. PLOS ONE, 18(1), e0280910. https://doi.org/10.1371/journal.pone.0280910
31. Maximov, V., Reznikova, K., & Popov, D. (2021). Data mining for marine data analysis. Russian Journal of Resources, Conservation and Recycling, 8(1). https://doi.org/10.15862/06INOR121
32. Murniyati, Mutiara, A. B., Wirawan, S., Yusnitasari, T., & Anggraini, D. (2023). Expanding Louvain Algorithm for Clustering Relationship Formation. International Journal of Advanced Computer Science and Applications, 14(1). https://doi.org/10.14569/IJACSA.2023.0140177
33. Odhiambo, N. M. (2009). Energy Consumption and Economic Growth Nexus in Tanzania: An ARDL Bounds Testing Approach. Energy Policy. https://doi.org/10.1016/j.enpol.2008.09.077
34. Öztürk, İ., Kaplan, M., & Kalyoncu, H. (2013). The Causal Relationship Between Energy Consumption and GDP in Turkey. Energy & Environment. https://doi.org/10.1260/0958-305x.24.5.727
35. Perrin, D., & Zuccon, G. (2018). Recursive module extraction using Louvain and PageRank. F1000Research, 7, 1286. https://doi.org/10.12688/f1000research.15845.1
36. Pradana, C., Kusumawardani, S. S., & Permanasari, A. E. (2020). Comparison Clustering Performance Based on Moodle Log Mining. IOP Conference Series: Materials Science and Engineering, 722(1), 012012. https://doi.org/10.1088/1757-899X/722/1/012012
37. Rezaeipanah, A., & Ghanat, K. (2021). An Ensemble of Community Detection in Social Networks Using Clustering of Users Demographic and Topological Information. Current Chinese Computer Science, 1(1), 12–20. https://doi.org/10.2174/2665997201999200407120239
38. Rohit Ranjan, Swati Agarwal, & Dr. S. Venkatesan. (2017). Detailed Analysis of Data Mining Tools. International Journal of Engineering Research And, V6(05). https://doi.org/10.17577/IJERTV6IS050459
39. Seth, S., Mallik, S., Bhadra, T., & Zhao, Z. (2022). Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data. Frontiers in Genetics, 13. https://doi.org/10.3389/fgene.2022.828479
40. Shahbaz, M., Zeshan, M., & Afza, T. (2012). Is Energy Consumption Effective to Spur Economic Growth in Pakistan? New Evidence From Bounds Test to Level Relationships and Granger Causality Tests. Economic Modelling. https://doi.org/10.1016/j.econmod.2012.06.027
41. Verma, K., Bhardwaj, S., Arya, R., Islam, M. S. U., Bhushan, M., Kumar, A., & Samant, P. (2019). Latest Tools for Data Mining and Machine Learning. International Journal of Innovative Technology and Exploring Engineering, 8(9S), 18–23. https://doi.org/10.35940/ijitee.I1003.0789S19
42. Wang, P., Deng, X., Liu, Y., Guo, L., Zhu, J., Fu, L., Xie, Y., Li, W., & Lai, J. (2022). A Knowledge Discovery Method for Landslide Monitoring Based on K-Core Decomposition and the Louvain Algorithm. ISPRS International Journal of Geo-Information, 11(4), 217. https://doi.org/10.3390/ijgi11040217
43. Wang, S., & Koopman, R. (2017). Clustering articles based on semantic similarity. Scientometrics, 111(2), 1017–1031. https://doi.org/10.1007/s11192-017-2298-x
44. Williams, N., Arnulfo, G., Wang, S. H., Nobili, L., Palva, S., & Palva, J. M. (2019). Comparison of Methods to Identify Modules in Noisy or Incomplete Brain Networks. Brain Connectivity, 9(2), 128–143. https://doi.org/10.1089/brain.2018.0603
45. Xueyao, L. (2022). Relationship Between Energy Consumption and Industrial Structure. Energy Systems Research, 5(3), 44–49. https://doi.org/10.38028/esr.2022.03.0006
46. You, Y., Ren, L., Zhang, Z., Zhang, K., & Huang, J. (2022). Research on improvement of Louvain community detection algorithm. In L. Zhu (Ed.), 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022) (p. 49). SPIE. https://doi.org/10.1117/12.2641420
47. Zhang, L., Hu, Q., & Zhang, F. (2014). Input-Output Modeling for Urban Energy Consumption in Beijing: Dynamics and Comparison. PLoS ONE, 9(3), e89850. https://doi.org/10.1371/journal.pone.0089850