###### Abstract  
Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output (\(P_{E}\)) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict \(P_{E}\). In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four  
Accurately predicting the full-load electrical power output of a base-load power plant is crucial as it maximizes the income from the available megawatt hours, resulting in improved efficiency and economic operation of the plant [3]. Furthermore, integrating this prediction with a dynamic condition monitoring system allows for a real-time performance comparison between the power plant and the derived model [4]. Any deviations detected can be promptly diagnosed and inspected. This approach guarantees the plant's safety and reliability across diverse operating conditions. Gas turbines are significantly influenced by ambient air compared to other conventional power generation systems, as they utilize ambient air directly as their working fluid [5]. Steam turbines are affected by exhaust steam pressure (or vacuum). Several studies have employed Artificial Neural Networks (ANNs) to predict the non-linear relationship between ambient conditions and the output of a CCPP. [PERSON] and [PERSON] [6] employed an ANN with a hidden layer consisting of 10 neurons to predict the power output of a CCPP. They analyzed the impact of prediction parameters on the power output and discovered that an increase in ambient temperature results in a decrease of approximately 14% in the power output. [PERSON] and [PERSON] [7] showed that combining an electrostatic discharge algorithm with an ANN outperforms other ANN models. ANN models were applied to predict the output of various power plants [8; 9; 10]. Moreover, several studies have employed traditional machine learning algorithms to predict the electrical power of a CCPP. [PERSON] [3] showed that Bagging REP Tree (BREP), a type of ensemble learning, was the most successful method among some machine learning regression models evaluated. [PERSON] et al. [11] utilized Random Forest to develop a model with lower errors and enhanced reliability in comparison to the methods proposed by [3]. [PERSON] et al. [12] reported that Random Forest achieved the best performance compared to Linear Regression, Multilayer Perceptron, and K-Nearest Neighbors. [PERSON] and [PERSON] [4] showed that the gradient boost regression model outperformed five regression models containing Random Forest. Previous studies have demonstrated that ensemble learning outperforms traditional single learners in predicting the power output of a CCPP. This study employs various machine learning algorithms and ensemble learning algorithms to predict the full-load electrical power output of a CCPP. Furthermore, we employ the Super Learner algorithm by combining them as base learners to enhance predictions compared to traditional single models. Many popular machine learning methods are ensemble algorithms such as Bagging, Random Forest, and Gradient Boosting.