Time Series Installed Capacity Forecasting with Deep Learning Approach for Türkiye

Year 2024, , 709 - 718, 03.10.2024

Zeynep Altıparmak , İnayet Özge Aksu

https://doi.org/10.21605/cukurovaumfd.1560142

Abstract

Deep learning methods have been developed to solve different problems due to the complex nature of real-world problems. Accurate future forecasting of a country's installed capacity is also crucial for developing a good energy sustainability strategy for the country. In this paper, three different time series forecasting methods are used for forward forecasting of installed capacity: Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Installed power values for the years 1923-2021 were used in the study. Then, future forecasts are made until 2030. The GRU model achieved the best RMSE in the testing phase compared to the LSTM and CNN models. Although CNN is successful during training, it has a higher RMSE during testing compared to GRU. While all models predict a potential increase in electricity capacity by 2030, GRU and LSTM predict a more significant increase up to this point compared to CNN.

Keywords

Deep learning, Future prediction, Installed capacity, Time series

Türkiye için Derin Öğrenme Yaklaşımı ile Zaman Serisi Kurulu Kapasite Tahmini

Abstract

Gerçek dünya problemlerinin karmaşık yapısı nedeniyle farklı problemleri çözmek için derin öğrenme yöntemleri geliştirilmiştir. Ülkelere ait kurulu gücün doğru şekilde ileri tahmini de ülkenin iyi bir enerji sürdürülebilirliği stratejisi geliştirilmesi için büyük önem taşımaktadır. Bu makalede, kurulu gücün ileri tahmini için üç farklı zaman serisi tahmin yöntemi kullanılmıştır: Kapılı Tekrarlayan Birim (GRU), Evrişimli Sinir Ağı (CNN) ve Uzun Kısa Süreli Bellek (LSTM). Çalışmada 1923-2021 yıllarına ait kurulu güç değerleri kullanılmıştır. Daha sonra 2030 yılına kadar gelecek tahminleri yapılmıştır. GRU modeli, test aşamasında LSTM ve CNN modellerine göre, en iyi RMSE'yi elde ederek en doğru model olarak ortaya çıkmıştır. CNN eğitim sırasında başarılı olmasına rağmen, test sırasında GRU'ya kıyasla daha yüksek RMSE sergilemiştir. Tüm modeller 2030 yılına kadar elektrik kapasitesinde potansiyel bir artış öngörürken GRU ve LSTM, CNN'e kıyasla bu noktaya kadar daha belirgin bir artış öngörmüştür.

Keywords

Derin öğrenme, İleri tahmin, Kurulu güç, Zaman serileri

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