## 3 Results and Discussion  
### Sensitivity Analysis: Evaluating Predictor Variable Impacts on Thermal Loads  
Understanding the influence of predictor variables on heating and cooling loads is crucial for optimizing building energy management strategies. In this section, we delve into a sensitivity analysis using the coefficient of determination (R\({}^{2}\)) to assess the impacts of individual predictor variables on the heating (QHEAT) and cooling (QCOOL) thermal loads. Recent studies, such as [33], demonstrate the effectiveness of R\({}^{2}\) in quantifying the relative importance of factors like building geometry and material properties, while advanced sensitivity frameworks [34] highlight methodologies to address uncertainties in load prediction. This analysis is conducted across different climatic conditions, offering insights into the variable dependencies that contribute significantly to the energy dynamics of LEBs. The results found are illustrated in Figures 4-6. Figure 4: Matrix of correlation between the considered predictor variables and thermal loads for Meknes. Figure 5: Matrix of correlation between the considered predictor variables and thermal loads for Irane. Figure 6: Matrix of correlation between predictor variables and thermal loads for Marrakech. As a key metric, the coefficient of determination \(\mathbb{R}^{2}\) provides a quantitative measure of the proportion of variance in thermal loads explained by each predictor variable. By scrutinizing \(\mathbb{R}^{2}\) values, we aim to identify the most influential factors shaping QHEAT and QCOOL under diverse climate scenarios. Recent studies, such as [35], demonstrate that LightGBM models achieve \(\mathbb{R}^{2}\) values as high as 0.9959 when evaluating features like glazing distribution and insulation efficiency across climatic regions, outperforming methods like random forest (RF) and long short-term memory (LSTM) networks. Similarly, deep learning frameworks incorporating bidirectional gated recurrent units (Bi-GRU) show enhanced predictive accuracy for ultra-short-term heating loads, with \(\mathbb{R}^{2}\) values reflecting robust performance in time-shifted feature analysis [36]. This comprehensive examination allows us to discern the relative importance of each predictor variable and elucidate their unique contributions to the overall predictive accuracy of the ML models. For instance, interpretable classifiers like decision trees and rule induction (RI) leverage \(\mathbb{R}^{2}\) to prioritize factors such as relative compactness and glazing area in residential buildings [37]. The upcoming figures and paragraphs present a detailed breakdown of pearson correlation r values for each predictor variable, unveiling the nuances of their impacts on heating and cooling loads. This sensitivity analysis not only refines our understanding of the complex interplay between predictors and thermal loads, but also lays the foundation for informed decision-making in designing energy-efficient solutions tailored to specific climates. In a Mediterranean climate like that of Meknes (Figure 4), there is a strong correlation with QHEAT primarily associated with X6 (r = 0.93). X1, X7, and X2 exhibit very weak correlations, while other predictor variables show almost no correlation with QHEAT. Regarding QCOOL, a moderate correlation is observed with X8 and X7, a minor correlation is present with X6 and X2, and even less of a correlation is seen with X9 and X4. Other variables show a lack of correlation. In a cold climate like that of Ifrane (Figure 5), almost similar to the results shown for Meknes, the correlation between QHEAT and X6 reaches the confidence interval. X1, X7, X2, and X3 exhibit very weak correlations, while other predictor variables show no correlation with QHEAT. Regarding QCOOL, a moderate correlation is observed with X8, followed by X7; a minor correlation is present with X6 and X2, and even less is seen with X4 and X9. Other variables indicate a lack of correlation.