Türkiye'de Yenilenebilir Enerji Tahmini: Analitik Yaklaşımlar

Öz

Artan nüfus ve sanayileşme, enerji talebinin artmasına neden olmuş, bu da kirlilik ve iklim değişikliği gibi çevre sorunlarını daha da kötüleştirmiştir. Yenilenebilir enerji kaynakları, çevresel faydaları ve sınırsız potansiyelleri nedeniyle ümit verici bir çözüm olarak değerlendirilmektedir. Bu çalışma, Türkiye'de yenilenebilir enerji kaynaklarından elektrik üretim oranlarını tahmin etmek için sinir ağlarının ve zaman serisi analizinin kullanımını incelemektedir. Sinir ağı tahminleri için her ikisi de geri yayılım algoritmasını temel alan LSTM, NNAR ve ELM modellerini kullanıyoruz. Ayrıca zaman serisi analizi için ARIMA, Holt trendi, doğrusal regresyon, ortalama ve üstel düzeltme modellerini kullanıyoruz. Performansı, eğitim ve test verilerinde ortalama mutlak hata ve kök ortalama kare hata kullanarak değerlendiriyoruz. Çalışma, LSTM modellerinin tahmin doğruluğunda ARIMA (1,2,1), ARIMA (2,2,1), ARIMA (3,2,1) ve NNAR yöntemlerinden daha iyi performans gösterdiğini göstermiştir. NNAR modeli başlangıçta en düşük hataya sahip olmasına rağmen doğrusal tahminleri onu pratik uygulamalar için daha az uygun hale getirdi. Çalışma, yenilenebilir enerji kaynaklarının tahmin edilmesinde sinir ağlarının ve zaman serisi analizinin etkinliğini vurguluyor. ARIMA (1,2,1), LSTM ve ARIMA (3,2,1) modelleme yöntemleri, Türkiye'nin yenilenebilir enerji geleceğinin planlanması ve yönetimini optimize etmek ve daha sürdürülebilir bir enerji ortamına katkıda bulunmak için kullanışlıdır.

Anahtar Kelimeler

• Yenilenebilir enerji
• Türkiye
• zaman serileri
• sinir ağları
• iklim değişikliği
• ARIMA
• LSTM

Renewable Energy Forecasting in Turkey: Analytical Approaches

Öz

The growing population and industrialization have resulted in an increased demand for energy, which has worsened environmental problems such as pollution and climate change. Renewable energy sources are considered a promising solution due to their environmental benefits and limited potential. This study examines the use of neural networks and time series analysis to predict electricity generation rates from renewable energy sources in Turkey. We use the LSTM, NNAR, and ELM models, all of which utilize the backpropagation algorithm for neural network forecasting. Additionally, we apply ARIMA, Holt’s trend, linear regression, mean, and exponential smoothing models for time series analysis. We evaluate the performance using the mean absolute error and root mean square error on the training and test data. The study showed that LSTM models outperformed the ARIMA (1,2,1), ARIMA (2,2,1), ARIMA (3,2,1), and NNAR methods in forecasting accuracy. Although the NNAR model initially had the lowest error, its linear predictions made it less suitable for practical applications. This study highlights the effectiveness of neural networks and time series analysis in predicting renewable energy sources. The ARIMA (1,2,1), LSTM and ARIMA (3,2,1) modeling methods are useful for optimizing the planning and management of Turkey's renewable energy future, contributing to a more sustainable energy landscape.

Anahtar Kelimeler

• Renewable energy
• Turkey
• time series
• neural networks
• climate change
• ARIMA
• LSTM

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