Forecasting the Biofuel Consumption Trend on a European Scale  with the Random Forest Algorithm

Melis Alpaslan Takan; Gamzenur Özsin

Makale detayları

Forecasting the Biofuel Consumption Trend on a European Scale with the Random Forest Algorithm

Yazarlar:

Melis Alpaslan Takan | Gamzenur Özsin

Yayıncı:

Kirikkale University

Ana Başlıklar:

Industrial Engineering

Dergi:

International Journal of Engineering Research and Development Vol.17 Issue 1, pp.126-136

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339433

pISSN:

1308-5506

Anahtar kelimeler:

Özet:

 The ever-increasing demand for energy worldwide, driven by population and economic growth, industrialization, as well as concerns about the environmental impact of fossil fuels, has increased the demand for renewable and clean energy. Biofuels play an important role in the worldwide transition to renewable energy. Accurate biofuel forecasting is therefore critical for regional policy making. This will enable policymakers to allocate countries own resources towards their strategic goals, plan the necessary infrastructure and support economic growth. In this study, a forecasting model is constructed using the Random Forest Algorithm RFA approach to predict the trends in biofuels consumption. Therefore, firstly, statistical data for the European region Total Europe and Other Europe are collected for 1992-2022. These values are then predicted for the years 2025, 2030 and 2050. The values obtained in the forecasting model have found the highest successful results for the given years with the number of decision trees being 50 and the R2 value is 0.9975. The results showed that the models created for Europe can be used in renewable energy projections for future planning. Obtained results are analyzed, the measures and requirements that can be taken in line with the European Union Green Deal are interpreted.

 The ever-increasing demand for energy worldwide, driven by population and economic growth, industrialization, as well as concerns about the environmental impact of fossil fuels, has increased the demand for renewable and clean energy. Biofuels play an important role in the worldwide transition to renewable energy. Accurate biofuel forecasting is therefore critical for regional policy making. This will enable policymakers to allocate countries own resources towards their strategic goals, plan the necessary infrastructure and support economic growth. In this study, a forecasting model is constructed using the Random Forest Algorithm RFA approach to predict the trends in biofuels consumption. Therefore, firstly, statistical data for the European region Total Europe and Other Europe are collected for 1992-2022. These values are then predicted for the years 2025, 2030 and 2050. The values obtained in the forecasting model have found the highest successful results for the given years with the number of decision trees being 50 and the R2 value is 0.9975. The results showed that the models created for Europe can be used in renewable energy projections for future planning. Obtained results are analyzed, the measures and requirements that can be taken in line with the European Union Green Deal are interpreted.

 The ever-increasing demand for energy worldwide, driven by population and economic growth, industrialization, as well as concerns about the environmental impact of fossil fuels, has increased the demand for renewable and clean energy. Biofuels play an important role in the worldwide transition to renewable energy. Accurate biofuel forecasting is therefore critical for regional policy making. This will enable policymakers to allocate countries own resources towards their strategic goals, plan the necessary infrastructure and support economic growth. In this study, a forecasting model is constructed using the Random Forest Algorithm RFA approach to predict the trends in biofuels consumption. Therefore, firstly, statistical data for the European region Total Europe and Other Europe are collected for 1992-2022. These values are then predicted for the years 2025, 2030 and 2050. The values obtained in the forecasting model have found the highest successful results for the given years with the number of decision trees being 50 and the R2 value is 0.9975. The results showed that the models created for Europe can be used in renewable energy projections for future planning. Obtained results are analyzed, the measures and requirements that can be taken in line with the European Union Green Deal are interpreted.

 Dünya genelinde enerji talebi, nüfus artışı, ekonomik büyüme ve sanayileşme ile birlikte sürekli yükselen enerji talebinin yanısıra fosil yakıtların çevresel etkileriyle ilişkili endişeler yenilenebilir ve temiz enerjiye olan talebi arttırmıştır. Dünya genelinde yenilenebilir enerjiye geçiş sürecinde, biyoyakıtlar önemli bir rol oynamaktadır. Bu nedenle doğru biyoyakıt tahminlemesi, bölgesel politikaların oluşturulması açısından kritik öneme sahiptir. Böylece politika yapıcıların ülkelerin özkaynaklarını stratejik hedeflerine yönelik tahsis etmeleri, gereken altyapıları planlamaları ve ekonomik büyümeyi desteklemeleri mümkün olabilecektir. Bu çalışmada biyoyakıtların tüketimin trendlerini öngörebilmek amacıyla Rassal Orman Algoritması ROA yaklaşımıyla bir tahmin modeli kurulmuştur. Bu amaca yönelik olarak ilk olarak, Avrupa bölgesindeki istatistiksel veriler Toplam Avrupa ve Diğer Avrupa olmak üzere 1992-2022 yılları için toplanmıştır. Ardından bu değerler, 2025, 2030 ve 2050 yılları için tahminlenmiştir. ROA ile kurulan tahmin modelinde elde edilen değerler verilen yıllar için en yüksek başarılı sonuçları karar ağacı sayısı 50 iken ve R2 değeri 0.9975 ile bulmuştur. Analizlerden elde edilen sonuçlar, Avrupa özelinde oluşturulan modellerin gelecek planlamaya yönelik yenilenebilir enerji projeksiyonlarında kullanılabileceğini göstermiştir. Elde edilen tüm sonuçlar detaylıca analiz edilerek, Avrupa Birliği Yeşil Mütabakatı doğrultusunda alınabilecek önlem ve gereklilikler yorumlanmıştır.

Makale detayları

Forecasting the Biofuel Consumption Trend on a European Scale with the Random Forest Algorithm

Yazarlar:

Melis Alpaslan Takan | Gamzenur Özsin

Yayıncı:

Kirikkale University

Ana Başlıklar:

Industrial Engineering

Dergi:

International Journal of Engineering Research and Development Vol.17 Issue 1, pp.126-136

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339433

pISSN:

1308-5506

energy

biofuel

prediction

random forest algorithmenerji

biyoyakıt

tahminleme

rassal orman algoritması

Sayı

Makale detayları

Yazarlar: Melis Alpaslan Takan | Gamzenur Özsin

Yayıncı: Kirikkale University

Ana Başlıklar: Industrial Engineering

Dergi: International Journal of Engineering Research and Development Vol.17 Issue 1, pp.126-136

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001339433

pISSN: 1308-5506

Sayı

Yazarlar:

Melis Alpaslan Takan | Gamzenur Özsin

Yayıncı:

Kirikkale University

Ana Başlıklar:

Industrial Engineering

Dergi:

International Journal of Engineering Research and Development Vol.17 Issue 1, pp.126-136

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339433

pISSN:

1308-5506