LSTM ve GRU Ağları Kullanılarak Türkiye’nin Elektrik Enerjisi Tüketiminin Tahmin Edilmesi

Abstract

Enerji talep yönetimi, gelişmekte olan ve yükselen ekonomiler için özellikle önemlidir. Büyüyen ekonomilerine bağlı olarak enerji tüketimleri önemli ölçüde artmaktadır. Türkiye’nin hızlı ekonomik ve nüfus artışının bir sonucu olarak elektrik tüketimi artmaktadır. Elektrik tüketimi tahmini enerji tedarikçileri, tüketiciler ve politika yapıcılar için önemli bir rol oynar. Bu nedenle, gelecekteki elektrik tüketim eğilimlerini doğru ve güvenilir bir şekilde tahmin etmek için modellerin kullanılması, elektrik güç sistemlerinin planlanması ve işletilmesi için kilit bir konudur. Bu makale, zaman serisi verileri için Uzun Kısa-Süreli Bellek (Long Short-Term Memory-LSTM) ve Kapılı Yinelemeli Birim (Gated Recurrent Unit-GRU) modelleri gibi derin öğrenme yöntemlerini kullanarak elektrik enerjisi tüketimini tahmin etmeye odaklanmıştır. Türkiye’de elektrik enerjisi tüketiminin geçmişe dönük veri seti kullanılarak bir saatlik ve üç saatlik ileriye yönelik tahminler gerçekleştirilmiştir. Karşılaştırma sonuçları, GRU modelinin LSTM modelinden biraz daha iyi olduğunu göstermektedir. Çalışmamız ayrıca, bir saat ileri tahminlerin üç saat ileri tahminlerden daha doğru olduğunu ortaya koymaktadır.

Keywords

• Derin Öğrenme
• Elektrik Enerji Tüketimi
• Tahmin
• Kapılı Yinelemeli Birim
• Uzun Kısa-Süreli Bellek

Forecasting of Turkey’s Electrical Energy Consumption using LSTM and GRU Networks

Abstract

Energy demand management is particularly important for developing and emerging economies. Their energy consumptions increase significantly, depending on their growing economies. As a result of Turkey’s rapid economic and population growth, electricity consumption is increasing. Electricity consumption forecasting plays an essential role for energy suppliers, consumers, and policy makers. Therefore, using models to accurately and reliably forecast future electricity consumption trends is a key issue for the planning and operation of electric power systems. This paper focused on forecasting electrical energy consumption by utilizing deep learning methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, for time series data. One-hour and three-hour ahead forecasting are accomplished by using a historical dataset of electrical energy consumption in Turkey. The comparison results show that the GRU model is slightly better than that of the LSTM. Our study also reveals that one-hour ahead predictions are more accurate than three-hour ahead predictions.

Keywords

• Deep Learning
• Electrical Energy Consumption
• Forecasting
• Gated Recurrent Unit
• Long Short-Term Memory

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