Outliers Treatment for Improved Prediction of CO and NOx Emissions from Gas Turbines Using Ensemble Regressor Approaches

Yıl 2025, Cilt: 8 Sayı: 1, 63 - 83, 18.03.2025

Vahid Sinap

https://doi.org/10.38016/jista.1566965

Öz

Gas turbines are widely used in power generation plants due to their high efficiency, but they also emit pollutants such as CO and NOx. This study focuses on developing predictive models for predicting CO and NOx emissions from gas turbines using machine learning algorithms. The dataset used includes pollutant emission data from a combined cycle gas turbine (CCGT) in Türkiye, collected hourly between 2011 and 2015. Various outlier treatment methods such as Z-Score, Interquartile Range (IQR), and Mahalanobis Distance (MD) are applied to the dataset. Machine learning algorithms including Random Forest, Extra Trees, Linear Regression, Support Vector Regression, Decision Tree, and K-Nearest Neighbors are used to build the predictive models, and their performances are compared. Additionally, Voting Ensemble Regressor (VR) and Stacking Ensemble Regressor (SR) methods are employed, using Gradient Boosting, LightGBM, and CatBoost as base learners and XGBoost as a meta-learner. The results demonstrate that the SR model, when applied to the dataset processed using the IQR method, achieves the highest prediction accuracy for both NOx and CO emissions, with R² values of 0.9194 and 0.8556, and RMSE values of 2.7669 and 0.4619, respectively. These findings highlight the significant role of the IQR method in enhancing model accuracy by effectively handling outliers and reducing data noise. The improved data quality achieved through this method contributes to the superior performance of the SR model, making it a reliable approach for predicting NOx and CO emissions with high precision.

Anahtar Kelimeler

Gas turbine emissions, Machine learning, Outlier processing, Combined cycle power generation, Interquartile range, Mahalanobis distance

Gaz Türbinlerinden Kaynaklanan CO ve NOx Emisyonlarının Tahmininde Aykırı Değer İşleme ve Topluluk Regresyon Yaklaşımlarının Kullanımı

Öz

Gaz türbinleri, yüksek verimlilikleri nedeniyle enerji üretim tesislerinde yaygın olarak kullanılmaktadır; ancak, aynı zamanda CO ve NOx gibi zararlı gaz emisyonlarına da neden olmaktadırlar. Bu çalışma, gaz türbinlerinden kaynaklanan CO ve NOx emisyonlarını tahmin etmek için makine öğrenmesi algoritmalarını kullanarak tahmin modelleri geliştirmeye odaklanmaktadır. Kullanılan veri seti, Türkiye'deki bir kombine çevrim gaz türbininden (CCGT) 2011 ve 2015 yılları arasında saatlik olarak toplanan emisyon verilerini içermektedir. Veri setine Z-Skoru, Çeyrekler Arası Aralık (IQR) ve Mahalanobis Mesafesi (MD) gibi çeşitli aykırı değer işleme yöntemleri uygulanarak modellerin performansına etkisine incelenmiştir. Modeller oluşturulurken Rastgele Orman, Ekstra Ağaçlar, Doğrusal Regresyon, Destek Vektör Regresyonu, Karar Ağacı ve K-En Yakın Komşu gibi makine öğrenmesi algoritmaları kullanılmış ve performansları karşılaştırılmıştır. Ayrıca, Gradient Boosting, LightGBM ve CatBoost algoritmalarını temel temel öğrenici ve XGBoost'u meta-öğrenici olarak kullanan Oylama Topluluk Regresyonu (VR) ve İstifleme Topluluk Regresyonu (SR) yöntemlerinin de performansları incelenmiştir. Sonuçlar, IQR yöntemiyle işlenen veri seti üzerinde uygulanan SR modelinin hem NOx hem de CO emisyonları için en yüksek tahmin doğruluğunu sağladığını göstermektedir. Modelin R² değeri NOx için 0.9194, CO için 0.8556 olarak bulunmuş; RMSE ise sırasıyla 2.7669 ve 0.4619 olarak elde edilmiştir. IQR yöntemiyle elde edilen iyileştirilmiş veri kalitesi, SR modelinin üstün performans göstermesine katkı sağlamakta ve modelin NOx ve CO emisyonlarını yüksek hassasiyetle tahmin edebilmesi açısından güvenilir bir yaklaşım olduğunu ortaya koymaktadır.

Anahtar Kelimeler

Gaz türbini emisyonları, Makine öğrenmesi, Aykırı değer işleme, Kombine çevrim enerji üretimi, Çeyrekler arası aralık, Mahalanobis uzaklığı

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