## 2 Materials and Methods
### Machine Learning Algorithms  
This study employed machine learning algorithms to predict power output (\(Po\)) based on time, environmental, technical, and locational variables. Machine learning offers a robust framework for handling complex datasets, capturing nonlinear relationships, and improving prediction accuracy through adaptive models [32]. The selected algorithms--random forest (RF), k-nearest neighbors (kNNs), gradient boosting (GB), linear regression (LR), and decision trees (Tree)--represent a diverse spectrum of approaches, ranging from simple interpretable models to sophisticated ensemble methods. These algorithms were chosen to comprehensively compare performance and interpretability while addressing the challenges of high-dimensional data and varying levels of feature importance [33]. By leveraging the strengths of these methods, this study aims to identify the most effective predictive model and gain insights into the underlying relationships between the dependent and independent variables [34]. The workflow diagram of the algorithms used for this study is given in Figure 1. In this study, Orange was used in the modeling, training, and validation stages of machine learning algorithms, which offers a visual interface to facilitate data mining and machine learning processes and is user-friendly. Data preprocessing, model optimization, and performance evaluation strategies were applied to reduce the risk of the models learning from the noise in the training set. By  
Figure 1: Workflow diagram of machine learning algorithms. following these steps, the reliability of the models was increased, and the generalizability of the predictions was ensured. RF is an ensemble learning algorithm that builds multiple decision trees and merges their predictions to improve accuracy and reduce overfitting [35]. This study used RF to predict _Po_ due to its robustness in handling nonlinear relationships and complex interactions between variables. By averaging predictions from diverse trees, RF minimizes the impact of noise and ensures a stable and reliable prediction framework. Additionally, its built-in feature importance metric provides insights into which independent variables (_Ws_, _Nd_, etc.) contribute most significantly to the predictions. The RF method allows the errors of individual decision trees to be balanced during merging. Since each tree may risk bias or deviation, merging reduces the error and increases the model's generalizability. However, in some cases, the voting and averaging process of the RF model can increase the error. Ensemble models such as RF provide lower error rates when errors vary among individual trees. For this reason, the superiority of the RF model has been proven using performance metrics. These results show that the majority voting or averaging method is suitable for the data in this study and increases the prediction accuracy. kNN is a non-parametric algorithm that predicts a target variable based on the similarity of data points within a defined neighborhood [36]. In this context, kNN was used to estimate power output by finding the k most similar instances in the dataset and averaging their values. Its simplicity and effectiveness in capturing local patterns make it particularly useful for this dataset. However, kNN's performance can be influenced by the choice of k and the distance metric, requiring careful optimization for accurate results [36]. GB is a robust ensemble algorithm that sequentially builds decision trees, with each tree correcting the errors of the previous ones [35]. GB was applied in this study to enhance the predictive accuracy of power output by focusing on minimizing the residual errors. Its ability to capture intricate patterns and improve performance iteratively makes it highly suitable for datasets with complex relationships. Despite its computational intensity, the algorithm's efficiency and adaptability make it valuable for power prediction tasks.