Fosil Yakıtla Elektrik Üretiminin Tahmini: Hibrit Yapay Zeka-İstatistiksel Çok Ülkeli Analiz

Öz

Fosil yakıta dayalı elektrik üretiminin doğru tahmin edilmesi, küresel enerji geçiş stratejileri için kritik bir zorluk olmaya devam ediyor; ancak mevcut yaklaşımlar genellikle hem karmaşık doğrusal olmayan dinamikleri hem de bölgeye özgü politika etkilerini yakalayacak metodolojik çok yönlülükten yoksundur. Bu çalışma, jeopolitik olarak farklı altı ülkeye (Türkiye, Almanya, Birleşik Krallık, Fransa, İran ve Ukrayna) gelişmiş uzun vadeli tahminler sunmak için gelişmiş derin öğrenme modellerini (MLP, ELM, NNETAR) yerleşik istatistiksel yöntemlerle (ARIMA, ETS, TBATS) sistematik olarak entegre eden yeni bir hibrit modelleme çerçevesi sunmaktadır. 1985-2022 yıllarını kapsayan yıllık verileri kullanarak, sürekli başlangıç ​​doğrulama stratejisi kullanıyoruz ve performansı RMSE, MAE ve MAPE ölçümleri aracılığıyla değerlendiriyoruz. Bulgularımız, derin öğrenme modellerinin değişken, doğrusal olmayan trendleri yakalamada istatistiksel muadillerinden sürekli olarak daha iyi performans gösterdiğini ortaya koyuyor (ELM'nin Fransa için %8,75'lik bir MAPE elde etmesi buna örnektir), AUTO.ARIMA gibi geleneksel modeller ise İran'da gösterildiği gibi istikrarlı rejimlerde etkili olmaya devam ediyor (MAPE: %2,40). Daha da önemlisi, bu araştırma yalnızca tahmin doğruluğunu geliştirmekle kalmıyor, aynı zamanda model performansını ulusal enerji geçiş yollarına bağlayarak eyleme dönüştürülebilir, politikayla ilgili bilgiler sağlıyor. Hibrit çerçevelerin özel karbondan arındırma stratejilerine nasıl rehberlik edebileceğini, şebeke istikrarını nasıl optimize edebileceğini ve ulusal enerji politikalarının küresel sürdürülebilirlik hedefleriyle uyumlaştırılmasını nasıl destekleyebileceğini, böylece dünya çapındaki politika yapıcılara ölçeklenebilir ve veri odaklı bir karar destek aracı sunabileceğini gösteriyoruz.

Anahtar Kelimeler

• Enerji Geçişi
• Hibrit Tahmin
• Derin Öğrenme
• Zaman Serisi Analizi
• Politika Bilişimi
• Karbondan Arındırma Stratejileri

Comparative Evaluation of AI and Statistical Models for Forecasting Fossil Fuel Electricity Generation

Öz

Forecasting fossil fuel-based electricity generation remains important for energy planning, particularly in countries undergoing different stages of energy transition. Reliable forecasts can support decision-makers in balancing energy security concerns with long-term sustainability objectives. This study investigates the forecasting performance of several statistical and machine-learning approaches using annual fossil fuel electricity generation data from six countries: Türkiye, Germany, the United Kingdom, France, Iran, and Ukraine. The dataset covers the period from 1985 to 2022 and includes countries with distinct energy structures and policy trajectories. Nine forecasting models were evaluated, including traditional statistical techniques (NAÏVE, AUTO.ARIMA, HOLT-WINTERS, ETS, THETAF, and TBATS) and neural network-based methods (NNETAR, MLP, and ELM). Model performance was assessed using rolling validation strategies and three commonly used error measures: RMSE, MAE, and MAPE. The results indicate that forecasting performance varies considerably across countries and depends on the underlying characteristics of each time series. Neural network-based models generally performed better in countries exhibiting more complex or irregular generation patterns, whereas conventional statistical methods remained competitive for relatively stable series. Among the evaluated approaches, ELM achieved the lowest forecasting errors for France and Ukraine, while AUTO.ARIMA and ETS provided highly accurate results for Iran. Rather than identifying a universally superior forecasting technique, the findings highlight the importance of selecting models according to the structural properties of national energy systems. The study provides a comparative perspective on fossil fuel electricity forecasting and offers insights that may support future energy planning and transition strategies.

Anahtar Kelimeler

• Energy Transition
• Comparative Forecasting
• Deep Learning
• Time Series Analysis
• Policy Informatics
• Decarbonization Strategies

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