What question did this study set out to answer?

The aim is to predict exhaust gas temperature (EGT) in gas turbines under varying loads and inlet guide vane positions.

June 5, 2026Open Access

Gas Turbine Exhaust Gas Temperature Prediction Under Variable Operating Loads and IGV Positions Using Tree-Based Ensemble Learning

Key Points

The aim is to predict exhaust gas temperature (EGT) in gas turbines under varying loads and inlet guide vane positions.
Developed a data-driven model using a dataset of 18,334 hours of operational data from a 401 MW gas turbine.
Evaluated parameters including gas turbine power output, inlet guide vane positions, compressor inlet temperature, and fuel gas flow.
Implemented six tree-based ensemble learning methods: Bagged Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost.
LightGBM achieved the best prediction performance with R2 of 0.9703 and RMSE of 1.5280.
GTPO, CIT, FGF, and IGV were identified as the most influential parameters on EGT.
Environmental variables like atmospheric pressure and relative humidity also affected EGT, albeit to a lesser extent.

Abstract

Exhaust Gas Temperature (EGT) is a critical parameter in Gas Turbines (GTs) in terms of performance monitoring, fault detection, and operational optimization. In this study, a comprehensive and data-driven modeling approach was developed to predict EGT under variable load conditions and different Inlet Guide Vane (IGV) positions in a 401 MW GT unit located in a Combined Cycle Power Plant (CCPP) with a single-shaft design. A large-scale dataset obtained from a total of 18,334 h of real operating conditions was used in the study. Operational parameters such as Gas Turbine Power Output (GTPO), IGV, Compressor Inlet Temperature (CIT), Fuel Gas Flow (FGF), and Lower Heating Value (LHV), together with environmental parameters such as Atmospheric Pressure (AP) and Relative Humidity (RH), were evaluated simultaneously, and the combined effect of these variables on EGT was investigated. In order to model the nonlinear relationships between EGT and the input variables, six different tree-based ensemble learning methods, namely Bagged Trees, Random Forest, Gradient Boosting, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), were applied and compared. The results showed that all models were able to predict EGT with high accuracy. The most successful model was LightGBM, which achieved the best overall prediction performance with a Coefficient of Determination (R2) of 0.9703 and a Root Mean Square Error (RMSE) of 1.5280. The analyses revealed that the most influential parameters affecting EGT were GTPO, CIT, FGF, and IGV, whereas the environmental variables had secondary but still significant effects. The proposed approach provides a reliable and computationally efficient tool for sensor validation, fault detection, and predictive maintenance applications.

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