Türkiye Elektrik Piyasası Takas Fiyatının Tahmininde Makine ve Derin Öğrenme Yöntemlerinin Karşılaştırmalı Analizi

Year 2024, Volume: 36 Issue: 2, 859 - 867, 30.09.2024

Ahmed İhsan Şimşek

https://doi.org/10.35234/fumbd.1473145

Abstract

Elektrik piyasa takas fiyatının tahmini enerji alanında stratejik öneme sahiptir. Doğru bir şekilde piyasa takas fiyatının tahmin edilmesi ile enerji şirketleri müşterilerine daha güvenilir fiyat alternatifleri sunarak operasyonel verimliliğini artırabilmektedir. Piyasa takas fiyatının doğru bir şekilde tahmini enerji sektöründeki karar vericilerin ve yatırımcıların stratejik seçimler yapmalarına yardımcı olması açısından büyük önem taşımaktadır. Enerji piyasasında istikrarın sağlanması ve tüketiciler açısından enerji güvenilirliğini artırmak için fiyat tahminlerinin doğru bir şekilde yapılması gerekmektedir. Bu nedenle enerji endüstrisinde doğru fiyat tahminlerinin tapılması için yeni yöntemlerin kullanılması ve daha doğru tahminlerin yapılması oldukça önemlidir. Bu çalışmada elektrik piyasa takas fiyatının tahmin edilmesi için Doğalgaz, baraj, linyit, ithal kömür, rüzgâr, güneş, jeotermal ve biokütleden üretilen saatlik elektrik verileri ile saatlik elektrik talep verileri girdi değişkeni olarak kullanılmıştır. Çalışma 17.04.2023-16.04.2024 arasındaki 8772 saatlik veriyi kapsamaktadır. Çalışmada XGBoost, Random Forest, LSTM ve SVR yöntemlerinin yanı sıra doğrusal regresyon ile de tahmin yapılmıştır. Modellerin performansları RMSE, MSE, MAE ve R2 istatistik katsayıları kullanılarak karşılaştırılmıştır. Elde edilen performans metriklerine göre en iyi tahmin performansının XGBoost yöntemi tarafından üretildiği gözlemlenmiştir.

Keywords

Piyasa takas fiyatı, makine öğrenmesi, derin öğrenme, karar destek, enerji.

Comparative Analysis of Machine and Deep Learning Methods in Estimating the Turkish Electricity Market Clearing Price

Abstract

The estimation of the clearing price in the electricity market holds significant strategic importance within the energy sector. Energy firms can enhance their operational efficiency by providing clients with more dependable price alternatives through precise estimation of the market clearing price. The precise determination of the market clearing price holds significant significance in facilitating strategic decision-making for decision makers and investors operating within the energy sector. Accurate pricing projections are crucial for ensuring stability in the energy market and enhancing energy reliability for consumers. Hence, it is imperative to employ novel methodologies and enhance the precision of predictions within the energy sector in order to ascertain precise price estimates. This study utilized hourly power data derived from various sources such as natural gas, dam, lignite, imported coal, wind, solar, geothermal, and biomass. Additionally, hourly electricity demand data was employed as input variables to estimate the clearing price of the electricity market. The study encompasses a total of 8772 hours of data collected between April 17, 2023, to April 16, 2023. The study employed linear regression, XGBoost, Random Forest, LSTM, and SVR techniques for prediction. The models were evaluated by comparing their performances using statistical coefficients such as RMSE, MSE, MAE, and R2. Based on the acquired performance measures, it was noted that the XGBoost approach exhibited the highest level of prediction performance.

Keywords

Electricity clearing price, machine learning, deep learning, decision support, energy.

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