Thermal Load Predictions in Low-Energy Buildings: A Hybrid AI-Based Approach Integrating Integral Feature Selection and Machine Learning Models  
[PERSON]  
1 Department of Energetics, Ecole Nationale Superieure d'Arts et Metiers, Moulay Ismail University,  
Me  
of multi-step forecasting with remarkably low errors in complex environments [6], while Physics-Informed Neural Networks (PINNs) provide robust predictions in thermodynamic applications, even with sparse datasets [7]. These methods are further supported by interpretable models like decision trees, which help identify critical predictors (e.g., glazing area, compactness) and align well with Morocco's material efficiency and solar integration objectives [2;4]. The use of hybrid AI techniques is growing in building energy prediction. For instance, [8] combined Support Vector Regression (SVR) and XGBoost with six metaheuristic algorithms and identified SBO-XGBoost as the best-performing model for both heating (R\({}^{2}\) = 0.9380) and cooling (R\({}^{2}\) = 0.9583) loads. In [9], the authors evaluated four ML models--Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Response Surface Methodology (RSM)--and found ELM to outperform others (R\({}^{2}\) = 0.9850 for heating and R\({}^{2}\) = 0.9916 for cooling). Likewise, [10] used an enhanced SVR combined with Feature Selection (FS) to predict cooling loads in public buildings, relying on dispersion metrics for evaluation. In Morocco-specific research, [11] compared Artificial Neural Network (ANN) and Generalized Linear Model(GLM) models for heating load prediction across six climatic zones and found ANN to be more accurate (R\({}^{2}\) = 0.95), although it suffered from stochastic variability. An extensive review in [12] highlights two critical gaps: (1) the underrepresentation of building-specific characteristics--such as geometry or orientation--as predictors, and (2) the lack of clear taxonomies distinguishing between ML, deep learning, and forecasting methods. Most models in the literature rely heavily on historical energy consumption data, overlooking the predictive value of architectural design variables. Furthermore, while numerous studies use AI to predict thermal loads using various features, none systematically compare all possible combinations of predictor variables to find optimal input configurations. In contrast, the present study offers a holistic and comparative approach to thermal load prediction in LEBs using hybrid AI models. It investigates how different predictor variables interact across three distinct Moroccan climates, with the goal of optimizing both model accuracy and computational efficiency. This work integrates Integral Feature Selection (IFS) techniques with several advanced ML algorithms to discover the most relevant input features and ideal model structures. The primary aim of this study is not merely to achieve accurate predictions of thermal loads using AI models, but to leverage these predictions to inform and optimize energy-efficient design decisions and operational strategies for LEBs. By identifying the most influential building and environmental parameters through hybrid IFS-ML frameworks, the findings provide actionable insights for architects, engineers, and energy managers. These insights support more informed design choices--such as optimized window-to-wall ratios, insulation levels, and orientation--which can contribute to substantial reductions in heating and cooling demand. While this study does not directly quantify the exact energy savings from each intervention, it lays the foundation for integrated building performance simulations and future work that links predictive accuracy to real-world energy savings and Heating, Ventilation and Air-Conditioning (HVAC) system optimization. The main objectives and contributions of this study are:  
1. To analyze the partial dependence and relative importance of predictor variables for heating and cooling loads;
2.