Farklı Derin Öğrenme Metotları ile Kısa Dönem Elektrik Yükü Tahmin Karşılaştırması

Year 2021, Issue: 31, 616 - 623, 31.12.2021

İpek Atik

https://doi.org/10.31590/ejosat.1017137

Abstract

Elektrik üretim miktarının tahmin edilmesi iletim ve dağıtım sistemlerinin planlaması, üretim ekonomisi, ünite çalışma programları ve bakım onarım zamanlaması için önemli role sahiptir. Doğru tahmin modellemeleri ile kesintisiz ve güvenilir elektrik enerjisi üretimi sağlanabilir. Çalışmamızda Türkiye için saatlik elektrik üretim verileri kullanılarak farklı derin öğrenme algoritmaları ile 1saat, 2 saat ve 3 saat ilerisi için tahmin çalışmaları yapılmıştır. Modellere ait MAE, RMSE ve korelasyon katsayısı değerleri ile performansları karşılaştırılmıştır. Çalışmada gerçek değerlere en yakın tahmin yapan modelin belirlenmesi amaçlanmıştır. Çalışmanın bu bağlamda gelecek tahmin çalışmaları için faydalı olacağı öngörülmektedir.

Keywords

Derin öğrenme, kısa dönem tahmin, LSTM, SVM, ANN

Comparison of Short-Term Electricity Load Forecasting Using Different Deep Learning Methods

Abstract

Estimation of the amount of electricity generation plays an important role in the planning of transmission and distribution systems, generation economy, unit work schedules and maintenance repair timing. With accurate forecasting models, uninterrupted and reliable electrical energy production can be achieved. In our study, 1-hour, 2-hour and 3-hour ahead predictions were made with different deep learning algorithms using Turkey's hourly electricity generation data. With the MAE, RMSE and correlation coefficient values of the models, their performances were compared. The study aimed to determine the model that makes the closest estimation to the real values. In this context, it is anticipated that the study will be useful for future prediction studies.

Keywords

Deep learning, short-term prediction, LSTM, SVM, ANN

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