A MACHINE LEARNING APPROACH BASED ON THE HERFINDAHL-HIRSCHMAN INDEX FOR DETERMINING THE ENERGY SUPPLY SECURITY LEVELS OF COUNTRIES

Öz

The security of energy supply constitutes one of the most critical national security concerns for countries. Reliance on a single energy source and supplier poses significant threats to a nation’s future, so governments must diversify the resources used in energy production. The objective of this study is to assess countries’ status with respect to energy supply security by measuring the diversity of their energy sources. To this end, the Herfindahl–Hirschman Index (HHI) and a suite of machine‑learning methods were employed to determine countries’ levels of energy supply security. High HHI values indicate that energy supply security is at risk and that a country is externally dependent on its energy resources. A dataset covering 77 countries from 2000 to 2023 was analysed to evaluate their energy supply security levels. The results show that Spain, with the lowest HHI value (0.2), has the highest level of energy supply security, while Paraguay, with the highest HHI value (1), has the lowest. As a secondary approach, the results from the HHI‑based classification were reclassified using machine‑learning techniques, including K‑Nearest Neighbours (KNN), Random Forest, Logistic Regression and Support Vector Machines (SVM). Among these methods, Random Forest achieved the best performance, with an accuracy rate of 94 %.

Anahtar Kelimeler

• : Energy Supply Security
• Herfindahl-Hirschman Index
• Machine Learning
• Renewable Energy
• Energy Diversity

ÜLKELERİN ENERJİ ARZ GÜVENLİĞİ DÜZEYLERİNİN BELİRLENMESİ İÇİN HERFINDAHL-HIRSCHMAN ENDEKSİ TABANLI MAKİNE ÖĞRENMESİ YAKLAŞIMI

Öz

Enerji arz güvenliği ülkeler açısından en önemli milli güvenlik sorunlarından biridir. Tek bir enerji kaynağına ve tedarikçiye bağımlı olmak ülkelerin geleceği açısından büyük tehditler oluşturmaktadır. Bunun için ülkeler enerji üretiminde kullanacağı kaynakları çeşitlendirmek zorundadırlar. Çalışmanın amacı, enerji kaynaklarının çeşitliliğini ölçerek ülkelerin enerji arz güvenliği açısından hangi durumda olduklarını değerlendirmektir. Bu çalışmada, ülkelerin enerji arz güvenliği düzeylerini belirlemek için Herfindahl-Hirschman Endeksi (HHI) ve makine öğrenmesi yöntemleri kullanılmıştır. HHI değerlerinin yüksek olması enerji arz güvenliğinin riskte olduğunu ve enerji kaynakları açısından dışa bağımlılığın olduğunu göstermektedir. Çalışmada, 2000-2023 yılları arasındaki 77 ülkeye ait veri seti analiz edilerek enerji arz güvenliği seviyeleri değerlendirilmiştir. Elde edilen sonuçlar, İspanya’nın en düşük HHI değeri (0,2) ile en yüksek enerji arz güvenliğine Paraguay’ın ise en yüksek HHI değeri (1) ile en düşük enerji arz güvenliğine sahip olduğunu göstermiştir. İkinci yöntem olarak da, makine öğrenmesi yöntemlerinden K En Yakın Komşu (KNN), Rastgele Orman (Random Forest), Lojistik Regresyon ve Destek Vektör Makineleri (SVM) yöntemleri ile HHI sınıflandırmadan elde edilen sonuçlar ile yeniden sınıflandırma işlemleri yapılmıştır. Random Forest, %94 doğruluk oranıyla en başarılı yöntem olmuştur.

Anahtar Kelimeler

• Enerji Arz Güvenliği
• Herfindahl-Hirschman Endeksi
• Makine Öğrenmesi
• Yenilenebilir Enerji
• Enerji Çeşitliliği

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