## 1 Introduction
### Literature Review  
The literature indicates significant advancements in ML, especially in EL and tree-based algorithms. The methods, including random forests (RF), Gradient Boosting Machines (GBM), and their derivatives, have reliably attained elevated accuracy in predicting endeavors [7]. The study [8] demonstrated that RF algorithms have exhibited 99% accuracy on datasets such as MNIST, signifying their reliability in classification jobs. In renewable energy, the precision of prediction is crucial; nonetheless, difficulties remain due to data variability and the unpredictable nature of energy sources. Current research has predominantly concentrated on theoretical progress rather than practical implementation [9; 10; 11; 12; 13; 14]. Recent studies in renewable energy prediction highlight the necessity for tailored models to tackle domain-specific issues [15]. The proposed model in [16] employs sophisticated ML approaches, including genetically optimized Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), to categorize strokes. Wildwood is a revolutionary RF methodology that offered efficient subtree aggregation for batch-supervised learning, presenting potential applications in renewable energy contexts. Likewise, sophisticated ML models such as genetically optimized CNN and LSTM networks have improved predicted accuracy and potentially fostered advancements in energy forecasting [17; 18]. There are also several studies on enhancing security predictions with tree-based applications [19; 20; 21; 22; 23; 24]. Table 1 provides detailed highlights of different tree-based approaches and features used in the literature toward identifying the research gaps. EL optimizes Mutual Information Criterion(MIC) strategies in DL with consortia of classifiers trained from the same dataset to provide improved classification performance [25]. EL is a method that combines multiple models to enhance the overall generalization and prediction ability of a classification or prediction model [26]. [PERSON] and [PERSON] aimed to produce an often more accurate ensemble than any individual model. Their practical performance fueled extensive interest in the development of a variety of applications [24]. In essence, EL methods often yield higher predictive accuracy and generalize better on new datasets than any of the single models. There can be marginal effectiveness over single models but they require additional computational resources and algorithm sophistication [27; 28]. There have been efforts using EL to provide more robust global support while handling local and regional details using model training based on regional features [29; 30]. Many factors can impact EL's performance, including the choice of base learners, how they are combined, how to manage base learner learning processes, and how to refine the final model, among others [31]. Several theoretical and numerical deep forest studies have shown the existence of a difficult counterpart between the difficulties paramount for EL models' functioning and the characteristics of the training data [32]. Each of these techniques applies to any learning algorithm, making them suitable for classification as wrapper methods. In addition, the final prediction for regression, classification, or probability prediction tasks can be obtained using a weighted average, majority voting, or product of probability values [33]. Table 1 highlights the need for advanced ML and ensemble learning techniques to address the challenges of renewable energy forecasting. It connects the theoretical background to the practical methodologies used in the study, laying the foundation for the research. Table 1 is not just a summary of prior research but also a tool for critically analyzing the field of renewable energy forecasting. It provides a clear rationale for the methodologies adopted in the study and serves as a benchmark for evaluating the performance of tree-based and ensemble learning models in energy prediction tasks.