## 5 Conclusions  
CCPPs generate additional electrical power from steam turbines by utilizing the exhaust heat from gas turbines, making them more efficient than simple cycles. Accurately predicting the full-load \(P_{E}\) of CCPP is crucial to ensure efficient and economical operation. In this study, six machine learning models, namely SVR, KNN, DT, RF, GBM, and XGB, were employed to predict the hourly electrical power output of a CCPP. These models were also utilized as base learners to generate SL. All models predict the hourly average \(P_{E}\) using input variables that affect the power output of gas and steam turbines, such as AT, V, AP, and RH. To generate 10 validation folds, we utilized five files from the randomly shuffled dataset and performed two-fold cross-validation on each file. Performance metrics for comparison are RMSE and MAE on the 10 validation folds. As a result, the prediction distributions of all models exhibit relatively lower variability compared to the distribution of the target \(P_{E}\). Ensemble learning models, including RF, GBM, XGB, and SL, outperformed traditional single machine learning models. SL achieves the best prediction performance among the ensemble models, yielding a RMSE of 3.1772 and a MAE of 2.2537. It represents a more stable ensemble learning model by combining various models. Additionally, the prediction error incurred by SL is lower than that of BREP, which was previously proposed as the optimal model in a different study [3].  
When estimating the coefficients of base learners, constraints were employed to minimize sensitivity to small fluctuations in data. In a future study, we aim to explore different methods for combining the predicted values of base learners in various aspects.  
This study demonstrates that the SL model significantly improves the accuracy of CCPP power output predictions, contributing to enhanced operational efficiency and better-informed decision-making. The proposed framework offers a robust approach for optimizing CCPP performance under diverse operational conditions.  
Conceptualization, Y.S. and J.P.; methodology, C.K.; software, Y.S. and J.P.; validation, Y.S. and J.P.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S. and J.P.; project administration, C.K.; funding acquisition, M.-S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Meteorological Administration Research and Development Program, grant number RS-2023-00239653 and was supported by a research grant from the Kongju National University in 2024.  
Not applicable.  
The original data presented in the study are openly available in UCI Machine Learning Repository at [[https://doi.org/10.24432/C5002N](https://doi.org/10.24432/C5002N)]([https://doi.org/10.24432/C5002N](https://doi.org/10.24432/C5002N)) ([PERSON] and [PERSON], 2014).  
Author [PERSON] was employed by the company Penta Systems Technology; Author [PERSON] was employed by the company YOUNGJIN Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.