İtalya’daki CO2 Emisyon Tahmini için Veri Madenciliği

Bu çalışmada uılusal ve uluslararası literatür çalışmaları gözden geçirilerek ve global veri kümeleri ve veri madenciliği teknikleri kullanılarak İtalya’daki CO2 emisyon durum tahmini ve değerlendirmesi yapılmıştır. Çalışmada tanımlayıcı teknikleri kullanılmış ve İtalya'daki enerji kaynaklarına dayalı olarak CO2 emisyonlarının konsantrasyonunu etkileyen ana potansiyel parametreleri bulmaya odaklanılmıştır. Sıralı Minimal Optimizasyon regresyonu (SMOreg), Doğrusal Regresyon ve Basit Doğrusal Regresyon kullanılmıştır. Analize göre, Sıvı Yakıt sektörü CO2 emisyonunda en yüksek artış oranı% 56,8 olmaktadır. R. Doğrusal Regresyon algoritması, CO2 emisyonları tahmininde ikinci algoritma Basit Doğrusal Regresyondan daha iyi bir performans sağlamaktadır. Elde edilen bu sonuçlar, karbondioksit emisyonlarını azaltmaya odaklanan İtalyan İklim Değişikliği Ulusal Programı nedeniyle İtalya'daki mevcut urumla uyumludur.

Anahtar Kelimeler:

CO2 emisyonu, İklimdeğişikliği, Enerji tüketimi, Veri madenciliği, WEKA

Data Mining For CO2 Emissions Prediction In Italy

This study is a preliminary evaluation of the situation of CO2 emissions in Italy, reviewing the international and national literature, using global datasets, and using data mining techniques for analysis and prediction. The study used descriptive methods. It focuses on finding the main potential parameters that effect the concentration of CO2 emissions based on energy resources in Italy. SMOreg, Linear Regression, and Simple Linear Regression are used. Based on the analysis, the Liquid Fuel sector has had the highest rate of increase in CO2 emission 56.8%. R. Linear Regression algorithm gives us a better performance of the prediction for the CO2 emissions than the second algorithm Simple Linear Regression. These results are in line with the present condition in Italy due to the Italian National Program on Climate Change which focuses on reducing carbon dioxide emissions.

Keywords:

CO2 emissions, Climatechange, energy consumption, Data Mining, WEKA,

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