###### Abstract
Ensemble learning is a method that combines prediction skill from multiple learning algorithms to generate more reliable predictions than a single learning algorithm. Bagging, called Bootstrap aggregating, is a method that combines multiple weak learners trained by random sampling with replacement [13]. Boosting is an ensemble method that reduces bias by iteratively combining multiple weak learners while reweighting the data [14]. Misclassified examples gain weights, while correctly classified ones lose weights. The next weak learner improves training error by focusing on the misclassified data from the previous weak learner. Stacking is also called \"Stacked Generalization\" and \"Super Learning\". Unlike Bagging and Boosting, Stacking aims to combine a diverse set of strong learners. First, base learners of Stacking learn the original data called level-zero data. [PERSON] [15] introduced a method that utilizes the output values of base learners as new input components called level-one data. The final prediction is generated by a meta-learner that learns from the level-one data as its new input [16]. [PERSON] [17], known for his work on Classification and Regression Trees (CART) and the creator of Random Forest algorithm, formalized Stacking. This approach proposed using k-fold cross-validation in Stacking and improved performance by giving constraints when calculating weights of the level-one data. Until then, the mathematical reasons for why Stacking worked well were unknown, and Stacking was considered a \"black art\". However, [PERSON] et al. [18] mathematically showed the theoretical background of Stacking; hence, the algorithm took on the name \"Super Learner\". They proved that the Super Learner ensemble represents an asymptotically optimal system for learning. In this study, we compare and analyze various machine learning algorithms to predict the full-load electric power of a CCPP and aim to identify the best prediction model. Several machine learning algorithms used in this paper include individual and ensemble models such as Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GBM), and eXtreme Gradient Boosting (XGB). Additionally, we use Super Learner (SL), which combines individual and ensemble models. The machine learning algorithms and SL used in this study predict the average hourly power output using average hourly variables such as ambient temperature, atmospheric pressure, relative humidity, and vacuum. Furthermore, by analyzing the coefficients derived through SL, we gain insights into the model's contributions to predicting power output. Our method provides a more robust framework for power output prediction, especially under varying operational conditions. This paper is organized as follows. Section 2 provides an overview of the CCPP structure and the dataset employed in this study. In Section 3, the machine learning algorithms and SL used for predicting the average hourly electrical power of the CCPP over six years are described. Section 4 presents a comparative evaluation of the prediction performance of these models. Finally, Section 5 offers a concise conclusion.